diff --git "a/notebooks/03_modelisation.ipynb" "b/notebooks/03_modelisation.ipynb" new file mode 100644--- /dev/null +++ "b/notebooks/03_modelisation.ipynb" @@ -0,0 +1,5134 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "70148918-76b1-4657-8d02-442a8883e0c0", + "metadata": {}, + "source": [ + "# Project 4 - Identifiez les causes d'attrition au sein d'une ESN¶" + ] + }, + { + "cell_type": "markdown", + "id": "e389e683-cad2-4ef9-a3c8-0b838fdbb5ed", + "metadata": {}, + "source": [ + "# Etape 3: Modelisation\n", + "**Objectif**: construire un premier modéle de classification\n", + "\n", + "**Contenu :**\n", + "\n", + "- Chargement des données traitées\n", + "\n", + "- Entraînement d’un modèle de base (un modèle Dummy, régression logistique, un modèle Dummy, un modèle non-linéaire RandomForest)\n", + "\n", + "- Évaluation : accuracy, precision, rappel, f1-score, roc_auc\n", + "\n", + "- Visualisation de la matrice de confusion\n", + "\n", + "- Premières conclusions sur les performances" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "368ef874-cdb9-4742-b214-56acca76bc11", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score, GridSearchCV, cross_val_predict, cross_validate\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.preprocessing import StandardScaler, OneHotEncoder, OrdinalEncoder\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.dummy import DummyClassifier\n", + "from sklearn.metrics import (\n", + " accuracy_score, precision_score, recall_score, f1_score,\n", + " roc_auc_score, average_precision_score,\n", + " classification_report, confusion_matrix, ConfusionMatrixDisplay,\n", + " precision_recall_curve\n", + ")\n", + "# imblearn\n", + "from imblearn.over_sampling import SMOTE\n", + "from imblearn.pipeline import Pipeline as ImbPipeline\n", + "\n", + "from sklearn.inspection import permutation_importance\n", + "\n", + "import shap\n", + "shap.initjs() \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74640976-e963-4d1c-a0c5-b07374c29120", + "metadata": {}, + "outputs": [], + "source": [ + "# Données\n", + "df = pd.read_csv(\"../data/processed/df_central_norm.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da42bdf9-9d82-45d3-8fbb-aff126cf81d0", + "metadata": {}, + "outputs": [], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "fd6bbb58-ac12-4a87-87f9-dda7149b139a", + "metadata": {}, + "source": [ + "### Préparation de variables\n", + "- binaires (0,1)\n", + "- catégoriques\n", + "- numeriques" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a9c6d579-9d1b-49c9-b666-ef24c11f44b6", + "metadata": {}, + "outputs": [], + "source": [ + "target = \"attrition\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bc386a9c-6605-48f6-8ba5-02b8366619f4", + "metadata": {}, + "outputs": [], + "source": [ + "num_cols = [\n", + " \"age\", \"revenu_mensuel\", \"nombre_experiences_precedentes\",\n", + " \"annee_experience_totale\", \"annees_dans_l_entreprise\",\n", + " \"annees_dans_le_poste_actuel\", \"satisfaction_employee_environnement\",\n", + " \"note_evaluation_precedente\", \"satisfaction_employee_nature_travail\",\n", + " \"satisfaction_employee_equipe\", \"satisfaction_employee_equilibre_pro_perso\",\n", + " \"note_evaluation_actuelle\", \"heure_supplementaires\",\n", + " \"augementation_salaire_precedente\", \"nombre_participation_pee\",\n", + " \"nb_formations_suivies\", \"distance_domicile_travail\",\n", + " \"niveau_education\", \"annees_depuis_la_derniere_promotion\",\n", + " \"annes_sous_responsable_actuel\",\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0d843f23-d103-4175-a385-98c29eec10ee", + "metadata": {}, + "outputs": [], + "source": [ + "ord_col = [\"frequence_deplacement\"] # encodage ordinal Aucune/Occasionnel/Fréquent\n", + "ord_categories = [[\"Aucun\", \"Occasionnel\", \"Frequent\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de4d9521-6ade-4c0f-a978-862aa9130027", + "metadata": {}, + "outputs": [], + "source": [ + "cat_cols = [\n", + " \"genre\", \"statut_marital\", \"departement\", \"poste\", \"domaine_etude\"\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f45b62b2-5a0b-4317-9eb7-c9d53c7096be", + "metadata": {}, + "outputs": [], + "source": [ + "X = df[num_cols + ord_col + cat_cols].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c50efaec-99b1-4d4b-af93-18152e0eff24", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "X" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b7745af4-011d-4807-b9ab-b5815c4dadd0", + "metadata": {}, + "outputs": [], + "source": [ + "X.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ebb626bb-3c46-481d-9e9c-65d78acbb5e9", + "metadata": {}, + "outputs": [], + "source": [ + "y = df[target].astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "79667a14-a653-458d-955d-0e8425df0698", + "metadata": {}, + "outputs": [], + "source": [ + "y" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6175fbe4-8636-4fc3-8ba0-5a2df402b579", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"X shape:\", X.shape, \"| y shape:\", y.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d128c132-cf14-45e7-8950-6a1fa6a4399b", + "metadata": {}, + "outputs": [], + "source": [ + "# Split stratifié train / test\n", + "X_train, X_test, y_train, y_test = train_test_split (\n", + " X, y, test_size=0.2, random_state=42, stratify=y\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "799d262c-af56-45c9-91d2-21180f62f24b", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"X_train:\", X_train.shape, \"| X_test:\", X_test.shape)\n", + "print(\"y_train:\", y_train.shape, \"| y_test:\", y_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a09fffc9-f813-4f1d-9914-5070b9433435", + "metadata": {}, + "outputs": [], + "source": [ + "enc_ord = OrdinalEncoder(\n", + " categories=ord_categories,\n", + " handle_unknown=\"use_encoded_value\", unknown_value=-1\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c9a11ff7-a297-4e02-b5f2-3f7a7eda7e06", + "metadata": {}, + "outputs": [], + "source": [ + "ohe = OneHotEncoder(\n", + " handle_unknown=\"ignore\",\n", + " sparse_output=False\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14f8f3ab-968e-4eac-98eb-a3433b72ec36", + "metadata": {}, + "outputs": [], + "source": [ + "preproc = ColumnTransformer(\n", + " transformers=[\n", + " (\"ord\", enc_ord, ord_col),\n", + " (\"ohe\", ohe, cat_cols),\n", + " (\"num\", \"passthrough\", num_cols)\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "b91142c3-a2fe-4ae5-b3ef-a60bad0da1c7", + "metadata": {}, + "source": [ + "## Réalisation d'un premier modéle de classification\n", + "\n", + "## Modèles : Dummy, Logit, RandomForest\n", + "\n", + "Modèle de base (baseline) Dummy\n", + "\n", + "Modèle linéaire LogisticRegression\n", + "\n", + "Modèle non-linéaire (principal pour l’amélioration) RandomForest" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "019b6275-d8a1-444b-9e2f-148e0c1339b1", + "metadata": {}, + "outputs": [], + "source": [ + "# Modèle de base (baseline)\n", + "dummy = DummyClassifier(strategy=\"most_frequent\", random_state=42)\n", + "\n", + "# Modèle linéaire\n", + "logit_bal = LogisticRegression(\n", + " solver=\"liblinear\",\n", + " class_weight=\"balanced\",\n", + " max_iter=1000,\n", + " random_state=42\n", + ")\n", + "\n", + "# Modèle non-linéaire (principal pour l’amélioration)\n", + "rf_bal = RandomForestClassifier(\n", + " n_estimators=100,\n", + " random_state=42,\n", + " n_jobs=-1\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c20487b0-f9f1-4318-9291-4953c9136072", + "metadata": {}, + "outputs": [], + "source": [ + "# Pipelines (préprocess + modèle)\n", + "\n", + "pipe_dummy = Pipeline([\n", + " (\"prep\", preproc),\n", + " (\"clf\", dummy)\n", + "])\n", + "\n", + "pipe_logit = Pipeline([\n", + " (\"prep\", preproc),\n", + " (\"clf\", logit_bal)\n", + "])\n", + "\n", + "pipe_rf = Pipeline([\n", + " (\"prep\", preproc),\n", + " (\"clf\", rf_bal)\n", + "])" + ] + }, + { + "cell_type": "markdown", + "id": "673b6620-bd8d-4f44-a09f-ebefeb1f4444", + "metadata": {}, + "source": [ + "### Fonction d’évaluation (TRAIN / TEST, métriques complètes)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1799b3f4-572a-44c0-9b20-b7604a52c18e", + "metadata": {}, + "outputs": [], + "source": [ + "def eval_side(X, y, model, set_name=\"TEST\", plot_cm=True):\n", + " \n", + " \"\"\" Fonction pour evalué un modéle avce le seuil standart 0.5 (via model.predict).\n", + " Calculer et afficher les métriques clés + matrice de confusion pour un jeu (train/test).\"\"\"\n", + " \n", + " # Prédictions avce seuil = 0.5 interne au modèle\n", + " y_pred = model.predict(X)\n", + " # Probabilité pour la classe 1, si dispo\n", + " y_proba = model.predict_proba(X)[:, 1] if hasattr(model, \"predict_proba\") else None\n", + "\n", + " acc = accuracy_score(y, y_pred)\n", + " prec = precision_score(y, y_pred, pos_label=1, zero_division=0)\n", + " rec = recall_score(y, y_pred, pos_label=1, zero_division=0)\n", + " f1 = f1_score(y, y_pred, pos_label=1, zero_division=0)\n", + "\n", + " if y_proba is not None:\n", + " roc = roc_auc_score(y, y_proba)\n", + " pr = average_precision_score(y, y_proba)\n", + " else:\n", + " roc, pr = float(\"nan\"), float(\"nan\")\n", + "\n", + "\n", + " print(f\"\\n--- {set_name} ---\")\n", + " print(f\"Accuracy : {acc:.3f} | Précision : {prec:.3f} | Rappel : {rec:.3f} | F1 : {f1:.3f}\")\n", + " if y_proba is not None:\n", + " print(f\"ROC AUC : {roc:.3f} | PR AUC : {pr:.3f}\")\n", + " print(\"\\nClassification report :\\n\", classification_report(y, y_pred, digits=3))\n", + "\n", + " if plot_cm:\n", + " cm = confusion_matrix(y, y_pred, labels=[0, 1])\n", + " disp = ConfusionMatrixDisplay(cm, display_labels=[\"Restés (0)\", \"Partis (1)\"])\n", + " fig, ax = plt.subplots(figsize=(4,4))\n", + " disp.plot(ax=ax, cmap=\"Blues\", values_format=\"d\", colorbar=False)\n", + " ax.set_title(f\"Matrice de confusion – {set_name}\")\n", + " plt.tight_layout(); plt.show()\n", + "\n", + " return {\"accuracy\":acc, \"precision\":prec, \"recall\":rec, \"f1\":f1, \"roc_auc\":roc, \"pr_auc\":pr}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8303dc94-9ee0-44e5-9c21-117216ca50a4", + "metadata": {}, + "outputs": [], + "source": [ + "def fit_and_evaluate(name, model, X_train, y_train, X_test, y_test):\n", + " \n", + " \"\"\"Fonction pour entraîner le modèle sur TRAIN et évaluer sur TRAIN et TEST. \n", + " Retourne les scores.\"\"\"\n", + " print(f\"\\n================= {name} =================\")\n", + " model.fit(X_train, y_train)\n", + " scores_train = eval_side(X_train, y_train, model, set_name=\"TRAIN\")\n", + " scores_test = eval_side(X_test, y_test, model, set_name=\"TEST\")\n", + " return {\"train\": scores_train, \"test\": scores_test}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d9dfe68f-aea3-431f-bc25-5e12dd52f2f5", + "metadata": {}, + "outputs": [], + "source": [ + "# Évaluation des 3 modèles de base\n", + "scores_dummy = fit_and_evaluate(\"Dummy\", pipe_dummy, X_train, y_train, X_test, y_test)\n", + "scores_logit = fit_and_evaluate(\"Logistic Regression (balanced)\", pipe_logit, X_train, y_train, X_test, y_test)\n", + "scores_rf = fit_and_evaluate(\"RandomForest (balanced)\", pipe_rf, X_train, y_train, X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b34ab5a5-aa29-4500-8110-195502e929ac", + "metadata": {}, + "outputs": [], + "source": [ + "# Construire les DataFrames\n", + "df_metrics_train = pd.DataFrame([\n", + " {\"model\": \"Dummy\", \"set\": \"TRAIN\", **scores_dummy[\"train\"]},\n", + " {\"model\": \"Logistic Regression (balanced)\",\"set\": \"TRAIN\", **scores_logit[\"train\"]},\n", + " {\"model\": \"RandomForest (balanced)\", \"set\": \"TRAIN\", **scores_rf[\"train\"]},\n", + "])\n", + "\n", + "df_metrics_test = pd.DataFrame([\n", + " {\"model\": \"Dummy\", \"set\": \"TEST\", **scores_dummy[\"test\"]},\n", + " {\"model\": \"Logistic Regression (balanced)\",\"set\": \"TEST\", **scores_logit[\"test\"]},\n", + " {\"model\": \"RandomForest (balanced)\", \"set\": \"TEST\", **scores_rf[\"test\"]},\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12cce1f7-dff9-4e69-94a5-315a1074f98a", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"METRICS – TRAIN\")\n", + "display(df_metrics_train.round(3))\n", + "\n", + "print(\"METRICS – TEST\")\n", + "display(df_metrics_test.round(3))" + ] + }, + { + "cell_type": "markdown", + "id": "372bd167-e7e1-4bbd-b128-895c13478b1e", + "metadata": {}, + "source": [ + "Dummy = baseline très mauvais sur la classe 1\n", + "\n", + "Logit (balanced) = bon compromis\n", + "\n", + "RF (balanced) = surapprentissage (train parfait, test mauvais rappel)" + ] + }, + { + "cell_type": "markdown", + "id": "a5426c0f-db57-4520-9209-39bdd151fe64", + "metadata": {}, + "source": [ + "**Résultats:**\n", + "\n", + "**Dummy** : baseline très mauvais sur la classe 1\n", + "\n", + "84% d’accuracy mais rappel=0 → fort déséquilibre (le modèle prédit tout “0”).\n", + "\n", + "**Logistique** : bon compromis\n", + "\n", + "AUC=0.824, précision=0.63, rappel=0.36 → il y a du signal linéaire, mais on rate encore beaucoup de départs. \n", + "\n", + "Modèle utile pour capter les départs (bon rappel) avec un coût raisonnable en FP.\n", + "\n", + "Écart train/test modéré → peu d’overfit.\n", + "\n", + "**RandomForest** : overfit massif - surapprentissage \n", + "\n", + "train parfait, \n", + "\n", + "test mauvais rappel (ne capte presque aucun départ)\n" + ] + }, + { + "cell_type": "markdown", + "id": "be90bba5-6778-4556-8906-7c113fee8e8b", + "metadata": {}, + "source": [ + "# Amélioration du modèle non-linéaire (RF)" + ] + }, + { + "cell_type": "markdown", + "id": "cb820937-32ad-4755-b69a-2996c6cfbd45", + "metadata": {}, + "source": [ + "CV avec `StratifiedKFold + cross_val_score`\n", + "\n", + "Courbe PR avec out-of-fold\n", + "\n", + "Choix du seuil optimal (F1, F2, etc.)\n", + "\n", + "Évaluation finale avec eval_with_threshold sur le test." + ] + }, + { + "cell_type": "markdown", + "id": "b9337e46-d89f-4185-b6ea-a368d4f4b99e", + "metadata": {}, + "source": [ + "### Validation croisée (StratifiedKFold) et stockage des métriques par fold\n", + "- `StratifiedKFold`\n", + "- `cross_val_score` avec 3 métriques : accuracy, ROC AUC, PR AUC\n", + "- calcule moyenne + écart-type." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d4007b53-4044-41d7-a64e-3fe97f6d7280", + "metadata": {}, + "outputs": [], + "source": [ + "def cv_scores_summary(model, X_train, y_train, n_splits=10, random_state=42):\n", + " \n", + " \"\"\"CV sur X_train/y_train avec stratification + scaling dans la Pipeline.\"\"\"\n", + " \n", + " cv = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=random_state)\n", + "\n", + " # pipeline = préproc + modèle\n", + " pipe = Pipeline(steps=[(\"prep\", preproc), (\"clf\", model)])\n", + "\n", + " s_roc = cross_val_score(pipe, X_train, y_train, cv=cv, scoring=\"roc_auc\", n_jobs=-1)\n", + " s_pr = cross_val_score(pipe, X_train, y_train, cv=cv, scoring=\"average_precision\", n_jobs=-1)\n", + " s_acc = cross_val_score(pipe, X_train, y_train, cv=cv, scoring=\"accuracy\", n_jobs=-1)\n", + "\n", + " df_folds = pd.DataFrame({\n", + " \"fold\": np.arange(1, n_splits+1),\n", + " \"roc_auc\": s_roc,\n", + " \"pr_auc\": s_pr,\n", + " \"accuracy\": s_acc\n", + " })\n", + " \n", + " summary = (\n", + " df_folds[[\"roc_auc\",\"pr_auc\",\"accuracy\"]]\n", + " .agg([\"mean\",\"std\"]).T.reset_index()\n", + " .rename(columns={\"index\":\"metric\"})\n", + " )\n", + " return df_folds, summary" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd47d65f-8619-46f8-979b-9932173c03af", + "metadata": {}, + "outputs": [], + "source": [ + "# Logit équilibré\n", + "df_logit_folds, sum_logit = cv_scores_summary(logit_bal, X_train, y_train, n_splits=10)\n", + "\n", + "display(\"Logit – CV (moyenne et STD)\", sum_logit.round(3))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3288261-49bc-4464-b7a3-80be6ac88de7", + "metadata": {}, + "outputs": [], + "source": [ + "# Modèle Random Forest non-linéaire, pondéré\n", + "rf_bal = RandomForestClassifier(\n", + " n_estimators=100,\n", + " class_weight=\"balanced\",\n", + " n_jobs=-1,\n", + " random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ae4c7b0-b219-4b24-8d78-3d368acb33d3", + "metadata": {}, + "outputs": [], + "source": [ + "# Random Forest équilibré\n", + "df_rf_folds, sum_rf = cv_scores_summary(rf_bal, X_train, y_train, n_splits=10)\n", + "display(\"RF – CV (moyenne et std):\\n\", sum_rf.round(3))\n" + ] + }, + { + "cell_type": "markdown", + "id": "2aa0aa41-0d7a-4116-a1f7-14638b3926b1", + "metadata": {}, + "source": [ + "Mon modèle RF non linéaire généralise de manière assez cohérente sur différents splits de train\n", + "\n", + "En validation croisée stratifiée (10 folds), le RandomForest pondéré obtient en moyenne :\n", + "\n", + "**ROC AUC ≈ 0.80 (± 0.05)** le modèle sépare assez bien “restés” / “partis”\n", + "\n", + "**PR AUC ≈ 0.56 (± 0.11)** sur une classe minoritaire (~13%), c’est nettement mieux que le hasard (≈ 0.13)\n", + "\n", + "**Accuracy ≈ 0.86 (± 0.02)** haute, mais à interpréter avec prudence à cause du déséquilibre\n", + "\n", + "Les écarts-types relativement faibles indiquent une bonne stabilité du modèle d’un fold à l’autre.\n", + "Comparé au modèle Dummy, le RandomForest apporte un gain net en capacité de détection des salariés à risque (PR AUC nettement supérieure).\n", + "Cependant, par rapport à la régression logistique pondérée, le RF reste moins performant en PR AUC, ce qui est important dans notre contexte où l’on préfère bien identifier les employés à risque de départ." + ] + }, + { + "cell_type": "markdown", + "id": "3292b789-8660-46c4-803a-3e6590e0a844", + "metadata": {}, + "source": [ + "**À retenir comme métrique principale : PR AUC**\n", + "\n", + "**PR AUC (average precision)** : métrique la plus pertinente quand la classe “partis” est minoritaire.\n", + "\n", + "**ROC AUC**: qualité de tri des scores (utile mais moins sensible à l’imbalance que PR AUC).\n", + "\n", + "**Accuracy** : à prendre avec beaucoup de recul (peut être trompeuse en cas de déséquilibre)." + ] + }, + { + "cell_type": "markdown", + "id": "b92822d4-cf30-4bbf-84f8-dbd6ea72f4da", + "metadata": {}, + "source": [ + "### Courbe Précision–Rappel (sur TRAIN en CV) + seuil optimal pour RandomForest (OOF)\n", + "\n", + "On construit des predictions out-of-fold sur tout le TRAIN pour RF, puis on calcule la courbe PR et les F1 / F2 pour choisir un seuil.\n", + "\n", + "On utilise `cross_val_predict(..., method=\"predict_proba\")` en CV stratifiée sur le train pour calculer la courbe PR et choisir un seuil." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4784c977-db45-42bb-a366-1542ce1bd909", + "metadata": {}, + "outputs": [], + "source": [ + "cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9857f48d-2247-4e19-8b5f-ad3c3e0d2ff6", + "metadata": {}, + "outputs": [], + "source": [ + "oof_proba = cross_val_predict(\n", + " pipe_rf,\n", + " X_train,\n", + " y_train,\n", + " cv=cv,\n", + " method=\"predict_proba\",\n", + " n_jobs=-1\n", + ")[:, 1]\n", + "\n", + "oof_true = y_train.values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "608078eb-75a5-482f-a41b-abc6468f165c", + "metadata": {}, + "outputs": [], + "source": [ + "# Courbe PR\n", + "precisions, recalls, thresholds = precision_recall_curve(oof_true, oof_proba)\n", + "pr_auc_oof = average_precision_score(oof_true, oof_proba)\n", + "\n", + "print(f\"Average Precision (PR AUC) OOF RF = {pr_auc_oof:.3f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a789a1e4-42dd-478f-85ee-5dc01e1d7159", + "metadata": {}, + "outputs": [], + "source": [ + "# Comme thresholds a une taille de len(precisions)-1,\n", + "# on aligne sur thresholds en enlevant le dernier point de précision/rappel\n", + "p = precisions[1:] # ou precisions[:-1]\n", + "r = recalls[1:] # ou recalls[:-1]\n", + "t = thresholds # longueur N-1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d32884d5-92ef-4354-88e2-eec6a0dac504", + "metadata": {}, + "outputs": [], + "source": [ + "beta2 = 2.0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "506a64bf-cc95-479c-931a-fd49175a2744", + "metadata": {}, + "outputs": [], + "source": [ + "# Calcul F1 et F2 pour chaque seuil\n", + "\n", + "f1_scores = 2 * (precisions * recalls) / (precisions + recalls + 1e-8)\n", + "f2_scores = (1 + beta2**2) * (precisions * recalls) / (beta2**2 * precisions + recalls + 1e-8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba5a0dea-bece-4bc1-8df3-4d67d0ceff35", + "metadata": {}, + "outputs": [], + "source": [ + "best_idx_f1 = np.nanargmax(f1_scores)\n", + "best_thr_f1 = thresholds[best_idx_f1] " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "215ebb63-8151-4f2c-bc3c-75877aebaab3", + "metadata": {}, + "outputs": [], + "source": [ + "best_idx_f2 = np.nanargmax(f2_scores)\n", + "best_thr_f2 = thresholds[best_idx_f2]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2637d3ca-4527-4ae0-9362-4768858b2ba1", + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"Seuil F1 optimal : {best_thr_f1:.3f} | P={precisions[best_idx_f1]:.3f} R={recalls[best_idx_f1]:.3f} F1={f1_scores[best_idx_f1]:.3f}\")\n", + "print(f\"Seuil F2 optimal : {best_thr_f2:.3f} | P={precisions[best_idx_f2]:.3f} R={recalls[best_idx_f2]:.3f} F2={f2_scores[best_idx_f2]:.3f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9118a56b-a93b-4228-a297-5f5fcd13f8bc", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot PR + points F1/F2\n", + "plt.figure(figsize=(6,5))\n", + "plt.plot(recalls, precisions, label=f\"PR curve (AP={pr_auc_oof:.3f})\")\n", + "plt.scatter(recalls[best_idx_f1], precisions[best_idx_f1], color=\"tab:orange\", label=f\"Best F1 (thr={best_thr_f1:.2f})\")\n", + "plt.scatter(recalls[best_idx_f2], precisions[best_idx_f2], color=\"tab:green\", label=f\"Best F2 (thr={best_thr_f2:.2f})\")\n", + "plt.xlabel(\"Recall\")\n", + "plt.ylabel(\"Precision\")\n", + "plt.title(\"Courbe précision-rappel – RF (OOF)\")\n", + "plt.legend()\n", + "plt.grid(alpha=0.4)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b4d7a001-e109-44c3-9168-cd9b130cc0e1", + "metadata": {}, + "source": [ + "**Choix d'optimisation (métiers -> seuil)** Notre priorité est de ne pas rater des démissions, en acceptant une baisse de précision. Pour ça, on privilégie un seuil plus bas pour augmenter le rappel (capturer un maximum de futurs départs)\n", + "\n", + "Sur la base de la courbe précision–rappel, le Random Forest atteint une Average Precision de 0,54 en out-of-fold, contre ~0,16 pour un modèle aléatoire.\n", + "\n", + "En optimisant la F1, on obtient un seuil d’environ 0,25, qui offre un compromis équilibré entre précision (50 %) et rappel (60 %).\n", + "\n", + "Cependant, dans un contexte de prévention des démissions, il est plus coûteux de rater un salarié réellement en risque (faux négatif) que d’alerter à tort (faux positif). Nous avons donc également étudié la F2-score, qui donne davantage de poids au rappel. Le seuil optimisant F2 est plus bas (~0,14) : il augmente le rappel au prix d’une baisse de précision." + ] + }, + { + "cell_type": "markdown", + "id": "99000822-3871-4007-a3b8-e5fb28a1eea3", + "metadata": {}, + "source": [ + "### Fit final sur TRAIN et évaluation sur TEST avec le seuil choisi" + ] + }, + { + "cell_type": "markdown", + "id": "96ad94a1-209e-4a9e-b6cf-0f4551aae6b6", + "metadata": {}, + "source": [ + "Nouvelle fonction eval_with_threshold (seuil personnalisé)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "5f18571b-a9c7-4281-9249-8e2d46d0c07c", + "metadata": {}, + "outputs": [], + "source": [ + "def eval_with_threshold(y_true, y_proba, threshold,\n", + " set_name=\"TEST – seuil custom\", plot_cm=True):\n", + " \"\"\"\n", + " Évalue les performances pour un seuil donné appliqué sur y_proba.\n", + " y_true : séries/array de labels (0/1)\n", + " y_proba : probabilités P(y=1)\n", + " threshold : seuil de classification\n", + " \"\"\"\n", + " # Binarisation par seuil\n", + " y_pred = (y_proba >= threshold).astype(int)\n", + "\n", + " acc = accuracy_score(y_true, y_pred)\n", + " prec = precision_score(y_true, y_pred, pos_label=1, zero_division=0)\n", + " rec = recall_score(y_true, y_pred, pos_label=1, zero_division=0)\n", + " f1 = f1_score(y_true, y_pred, pos_label=1, zero_division=0)\n", + " roc = roc_auc_score(y_true, y_proba)\n", + " pr = average_precision_score(y_true, y_proba)\n", + "\n", + " print(f\"\\n=== {set_name} (seuil={threshold:.3f}) ===\")\n", + " print(f\"Accuracy : {acc:.3f} | Précision : {prec:.3f} | Rappel : {rec:.3f} | F1 : {f1:.3f}\")\n", + " print(f\"ROC AUC : {roc:.3f} | PR AUC : {pr:.3f}\")\n", + " print(\"\\nClassification report :\\n\", classification_report(y_true, y_pred, digits=3))\n", + "\n", + " if plot_cm:\n", + " cm = confusion_matrix(y_true, y_pred, labels=[0, 1])\n", + " disp = ConfusionMatrixDisplay(cm, display_labels=[\"Restés (0)\", \"Partis (1)\"])\n", + " fig, ax = plt.subplots(figsize=(4,4))\n", + " disp.plot(ax=ax, cmap=\"Blues\", values_format=\"d\", colorbar=False)\n", + " ax.set_title(f\"Matrice de confusion – {set_name}\")\n", + " plt.tight_layout(); plt.show()\n", + "\n", + " return {\n", + " \"accuracy\": acc, \"precision\": prec, \"recall\": rec, \"f1\": f1,\n", + " \"roc_auc\": roc, \"pr_auc\": pr\n", + " }\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0d621da3-0c33-40eb-ab89-dc460fcc6c35", + "metadata": {}, + "outputs": [], + "source": [ + "# On fige le seuil choisi (F2)\n", + "chosen_thr = float(best_thr_f2) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d520866-d029-4ead-bd16-e5a47235a6b5", + "metadata": {}, + "outputs": [], + "source": [ + "# Fit final sur tout X_train / y_train\n", + "pipe_rf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fd2ca6f0-ff9a-4530-96c9-71f23b74fa97", + "metadata": {}, + "outputs": [], + "source": [ + "# Probabilités sur TRAIN & TEST\n", + "proba_train_rf = pipe_rf.predict_proba(X_train)[:, 1]\n", + "proba_test_rf = pipe_rf.predict_proba(X_test)[:, 1]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13b2ec8e-cd48-4bba-8b20-b93075ababc1", + "metadata": {}, + "outputs": [], + "source": [ + "# Évaluation avec seuil optimisé\n", + "scores_rf_test_F2 = eval_with_threshold(\n", + " y_test, proba_test_rf,\n", + " threshold=chosen_thr,\n", + " set_name=\"TEST – RF (F2)\",\n", + " plot_cm=True\n", + ")\n", + "\n", + "scores_rf_train_F2 = eval_with_threshold(\n", + " y_train, proba_train_rf,\n", + " threshold=chosen_thr, \n", + " set_name=\"TRAIN – RF F2\",\n", + " plot_cm=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3cb9f58a-595e-4b0f-99b8-08b07aca8c70", + "metadata": {}, + "outputs": [], + "source": [ + "# Pour comparaison : seuil F1\n", + "scores_rf_test_F1 = eval_with_threshold(\n", + " y_test, proba_test_rf,\n", + " threshold=0.26,\n", + " set_name=\"TEST – RF seuil F1\",\n", + " plot_cm=True\n", + ")\n", + "\n", + "\n", + "scores_rf_train_FA = eval_with_threshold(\n", + " y_train, proba_train_rf,\n", + " threshold=0.26, \n", + " set_name=\"TRAIN – RF F1\",\n", + " plot_cm=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "e5ac4d91-fd82-4a09-ad73-5db8f4cc6ced", + "metadata": {}, + "source": [ + "### Interprétation" + ] + }, + { + "cell_type": "markdown", + "id": "a94e39d7-7e16-4cbf-a6fb-9a4d93475466", + "metadata": {}, + "source": [ + "1. **Seuil optimisé F2 (≈ 0,14)**\n", + "\n", + "Matrice de confusion (test)\n", + "\n", + "TN (restés bien prédits) : 171\n", + "\n", + "FP (faux “partis”) : 76\n", + "\n", + "FN (partis manqués) : 13\n", + "\n", + "TP (partis bien prédits) : 34\n", + "\n", + "Le modèle repère ~72 % des salariés qui quittent (rappel élevé).\n", + "\n", + "Mais se trompe souvent en annonçant “partira” pour des gens qui restent (beaucoup de FP → précision 0,31).\n", + "\n", + "C’est un mode “radar sensible” : on attrape la plupart des départs, au prix de nombreux faux signaux.\n", + "\n", + "2. **Seuil standard 0,5**\n", + "\n", + " Le modele ne signale comme “partira” que très peu de personnes.\n", + "\n", + "Résultat : il rate 40 sur 47 départs (rappel 0,085 !), mais quand il prédit “partira”, il a plus souvent raison (précision 0,57).\n", + "\n", + "C’est un mode “radar peu sensible” : peu de faux positifs, mais énormément de départs non détectés\n", + "\n", + "**Résultat:**\n", + "Seuil F2 (~0,14) → beaucoup plus intéressant: il détecte la majorité des démissions, au prix d’alertes supplémentaires, ce qui est acceptable si ces alertes déclenchent surtout des actions RH (entretiens, mesure de prévention)." + ] + }, + { + "cell_type": "markdown", + "id": "4aea35bd-9367-470a-b528-3dad32e3b2bc", + "metadata": {}, + "source": [ + "### Feature engineering" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "b3897be3-b8cd-430c-b154-174f96634666", + "metadata": {}, + "outputs": [], + "source": [ + "# Données\n", + "df = pd.read_csv(\"../data/processed/df_central_norm.csv\")" + ] + }, + { + "cell_type": "markdown", + "id": "01203898-658a-46a9-a813-a47d0c84ba24", + "metadata": {}, + "source": [ + "**Creation des features** \n", + "\n", + "**Satisfaction** ��au travail” au sens large `satisfaction_globale` : moyenne des 3 dimensions (environnement, nature, équipe)." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "8200cab0-b7df-4d1a-a0ab-32308c40e02e", + "metadata": {}, + "outputs": [], + "source": [ + "sats = [\"satisfaction_employee_environnement\",\n", + " \"satisfaction_employee_nature_travail\",\n", + " \"satisfaction_employee_equipe\",\n", + " \"satisfaction_employee_equilibre_pro_perso\"\n", + " ]\n", + "df[\"satisfaction_globale\"] = df[sats].mean(axis=1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "c21a93f9-2865-4cf1-93ee-f1e9b972d645", + "metadata": {}, + "outputs": [], + "source": [ + "df[\"exp_moins_3_years\"] = (\n", + " (df[\"annee_experience_totale\"] < 3) &\n", + " (df[\"annees_dans_l_entreprise\"] < 3)\n", + ").astype(\"uint8\")" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "ae42126f-992d-44c3-9e85-671d1303d327", + "metadata": {}, + "outputs": [], + "source": [ + "df.drop(columns=['satisfaction_employee_environnement', 'satisfaction_employee_nature_travail',\n", + " 'satisfaction_employee_equipe', 'satisfaction_employee_equilibre_pro_perso',\n", + " 'annee_experience_totale', 'annees_dans_l_entreprise',\n", + " ],\n", + " inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "2b48d4c0-98a3-4d12-83e4-8b9b63806065", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 1470 entries, 0 to 1469\n", + "Data columns (total 23 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 age 1470 non-null int64 \n", + " 1 genre 1470 non-null object \n", + " 2 revenu_mensuel 1470 non-null int64 \n", + " 3 statut_marital 1470 non-null object \n", + " 4 departement 1470 non-null object \n", + " 5 poste 1470 non-null object \n", + " 6 nombre_experiences_precedentes 1470 non-null int64 \n", + " 7 annees_dans_le_poste_actuel 1470 non-null int64 \n", + " 8 note_evaluation_precedente 1470 non-null int64 \n", + " 9 note_evaluation_actuelle 1470 non-null int64 \n", + " 10 heure_supplementaires 1470 non-null int64 \n", + " 11 augementation_salaire_precedente 1470 non-null int64 \n", + " 12 attrition 1470 non-null int64 \n", + " 13 nombre_participation_pee 1470 non-null int64 \n", + " 14 nb_formations_suivies 1470 non-null int64 \n", + " 15 distance_domicile_travail 1470 non-null int64 \n", + " 16 niveau_education 1470 non-null int64 \n", + " 17 domaine_etude 1470 non-null object \n", + " 18 frequence_deplacement 1470 non-null object \n", + " 19 annees_depuis_la_derniere_promotion 1470 non-null int64 \n", + " 20 annes_sous_responsable_actuel 1470 non-null int64 \n", + " 21 satisfaction_globale 1470 non-null float64\n", + " 22 exp_moins_3_years 1470 non-null uint8 \n", + "dtypes: float64(1), int64(15), object(6), uint8(1)\n", + "memory usage: 254.2+ KB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "2a0d51da-0e7e-4673-b73a-87dd1a521cf6", + "metadata": {}, + "outputs": [], + "source": [ + "target = \"attrition\"" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "c086b4fa-61c7-4c4a-aa33-5a974f8a338e", + "metadata": {}, + "outputs": [], + "source": [ + "num_cols = [\n", + " 'age','revenu_mensuel','nombre_experiences_precedentes',\n", + " 'annees_dans_le_poste_actuel','note_evaluation_precedente',\n", + " 'note_evaluation_actuelle','heure_supplementaires','augementation_salaire_precedente',\n", + " 'nombre_participation_pee','nb_formations_suivies','distance_domicile_travail',\n", + " 'niveau_education','annees_depuis_la_derniere_promotion','annes_sous_responsable_actuel',\n", + " 'satisfaction_globale', 'exp_moins_3_years'\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "262f4123-b22e-432f-9c52-bcd250c616c1", + "metadata": {}, + "outputs": [], + "source": [ + "ord_col = [\"frequence_deplacement\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "bc35ec56-8c5d-4099-81c8-7dbfd6d18303", + "metadata": {}, + "outputs": [], + "source": [ + "cat_cols = [\"genre\", \"statut_marital\", \"departement\", \"poste\", \"domaine_etude\"]" + ] + }, + { + "cell_type": "markdown", + "id": "fad8b3b3-7751-48d5-96a2-94cbbc2b37b7", + "metadata": {}, + "source": [ + "Mettre à jour les colonnes numériques + préprocesseur" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "f7b495a4-0dab-49f2-86c1-777eb1c64eba", + "metadata": {}, + "outputs": [], + "source": [ + "# ---- Encodeurs ----\n", + "enc_ord = OrdinalEncoder(\n", + " categories=[[\"Aucun\",\"Occasionnel\",\"Frequent\"]],\n", + " handle_unknown=\"use_encoded_value\",\n", + " unknown_value=-1\n", + ")\n", + "\n", + "ohe = OneHotEncoder(\n", + " handle_unknown=\"ignore\",\n", + " sparse_output=False\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "42b05846-f273-4e51-9a76-137923bd4b02", + "metadata": {}, + "outputs": [], + "source": [ + "preproc = ColumnTransformer(\n", + " transformers=[\n", + " (\"ord\", enc_ord, ord_col),\n", + " (\"ohe\", ohe, cat_cols),\n", + " (\"num\", \"passthrough\", num_cols)\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "47e6191c-74c3-4fef-8063-350303b3bb42", + "metadata": {}, + "source": [ + "Recréer X / y + nouveau train/test split" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "7d5c02b6-c4b7-41c4-a579-52a08ed249be", + "metadata": {}, + "outputs": [], + "source": [ + "X = df[num_cols + ord_col + cat_cols].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "9752660c-815f-4702-b1c6-10155c6dfc55", + "metadata": {}, + "outputs": [], + "source": [ + "y = df[target].astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "36c16dc4-e2c7-4c4c-901a-2b9c98858939", + "metadata": {}, + "outputs": [], + "source": [ + "# Division initiale en train/test\n", + "X_train, X_test, y_train, y_test = train_test_split (\n", + " X, y, test_size=0.2, random_state=42, stratify=y\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "449f6070-0912-42bb-a0f2-cf53d11dbb79", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_train: (1176, 22) | X_test (294, 22)\n", + "y_train: (1176,) | y_test (294,)\n" + ] + } + ], + "source": [ + "print(\"X_train:\", X_train.shape, \"| X_test\", X_test.shape)\n", + "print(\"y_train:\", y_train.shape, \"| y_test\", y_test.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "8dab5152-d49f-409f-8aed-b5ea41c6c437", + "metadata": {}, + "source": [ + "Nouveau RandomForest + validation croisée (moyenne ± std)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "96a79c73-15a9-496e-a0d8-37e7301e3a8d", + "metadata": {}, + "outputs": [], + "source": [ + "# RF plus régularisé\n", + "rf_reg = RandomForestClassifier(\n", + " n_estimators=200,\n", + " class_weight=\"balanced\",\n", + " max_depth=8, \n", + " min_samples_leaf=5, \n", + " min_samples_split=10, \n", + " max_features=\"sqrt\",\n", + " n_jobs=-1,\n", + " random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "4b1c1ae1-869c-419c-bed6-c1fe54ec0f57", + "metadata": {}, + "outputs": [], + "source": [ + "# Pipeline = preproc + modèle\n", + "pipe_rf_reg = Pipeline([\n", + " (\"prep\", preproc),\n", + " (\"clf\", rf_reg)\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "f6b3cb06-ca15-4d64-b941-ae2d459c5457", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Pipeline(steps=[('prep',\n",
+       "                 ColumnTransformer(transformers=[('ord',\n",
+       "                                                  OrdinalEncoder(categories=[['Aucun',\n",
+       "                                                                              'Occasionnel',\n",
+       "                                                                              'Frequent']],\n",
+       "                                                                 handle_unknown='use_encoded_value',\n",
+       "                                                                 unknown_value=-1),\n",
+       "                                                  ['frequence_deplacement']),\n",
+       "                                                 ('ohe',\n",
+       "                                                  OneHotEncoder(handle_unknown='ignore',\n",
+       "                                                                sparse_output=False),\n",
+       "                                                  ['genre', 'statut_marital',\n",
+       "                                                   'departement', 'poste',\n",
+       "                                                   'domaine_etude']),\n",
+       "                                                 ('num', 'pass...\n",
+       "                                                   'nombre_participation_pee',\n",
+       "                                                   'nb_formations_suivies',\n",
+       "                                                   'distance_domicile_travail',\n",
+       "                                                   'niveau_education',\n",
+       "                                                   'annees_depuis_la_derniere_promotion',\n",
+       "                                                   'annes_sous_responsable_actuel',\n",
+       "                                                   'satisfaction_globale',\n",
+       "                                                   'exp_moins_3_years'])])),\n",
+       "                ('clf',\n",
+       "                 RandomForestClassifier(class_weight='balanced', max_depth=8,\n",
+       "                                        min_samples_leaf=5,\n",
+       "                                        min_samples_split=10, n_estimators=200,\n",
+       "                                        n_jobs=-1, random_state=42))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Pipeline(steps=[('prep',\n", + " ColumnTransformer(transformers=[('ord',\n", + " OrdinalEncoder(categories=[['Aucun',\n", + " 'Occasionnel',\n", + " 'Frequent']],\n", + " handle_unknown='use_encoded_value',\n", + " unknown_value=-1),\n", + " ['frequence_deplacement']),\n", + " ('ohe',\n", + " OneHotEncoder(handle_unknown='ignore',\n", + " sparse_output=False),\n", + " ['genre', 'statut_marital',\n", + " 'departement', 'poste',\n", + " 'domaine_etude']),\n", + " ('num', 'pass...\n", + " 'nombre_participation_pee',\n", + " 'nb_formations_suivies',\n", + " 'distance_domicile_travail',\n", + " 'niveau_education',\n", + " 'annees_depuis_la_derniere_promotion',\n", + " 'annes_sous_responsable_actuel',\n", + " 'satisfaction_globale',\n", + " 'exp_moins_3_years'])])),\n", + " ('clf',\n", + " RandomForestClassifier(class_weight='balanced', max_depth=8,\n", + " min_samples_leaf=5,\n", + " min_samples_split=10, n_estimators=200,\n", + " n_jobs=-1, random_state=42))])" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipe_rf_reg.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "9ad2e144-1e81-47b1-b588-6062583f8dba", + "metadata": {}, + "outputs": [], + "source": [ + "cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "eb265a32-9e6c-4305-a7cd-a50dab8bf81b", + "metadata": {}, + "outputs": [], + "source": [ + "# Scores CV sur X_train / y_train\n", + "roc_cv_reg = cross_val_score(pipe_rf_reg, X_train, y_train, cv=cv,\n", + " scoring=\"roc_auc\", n_jobs=-1)\n", + "pr_cv_reg = cross_val_score(pipe_rf_reg, X_train, y_train, cv=cv,\n", + " scoring=\"average_precision\", n_jobs=-1)\n", + "acc_cv_reg = cross_val_score(pipe_rf_reg, X_train, y_train, cv=cv,\n", + " scoring=\"accuracy\", n_jobs=-1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "a8c9f46d-bf93-440f-952a-016b74a88e04", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RF régularisé – CV sur TRAIN\n", + "ROC AUC mean : 0.821 std : 0.055\n", + "PR AUC mean : 0.602 std : 0.081\n", + "ACC mean : 0.867 std : 0.028\n" + ] + } + ], + "source": [ + "print(\"RF régularisé – CV sur TRAIN\")\n", + "print(\"ROC AUC mean :\", roc_cv_reg.mean().round(3),\" std :\", roc_cv_reg.std().round(3))\n", + "print(\"PR AUC mean :\", pr_cv_reg.mean().round(3), \" std :\", pr_cv_reg.std().round(3))\n", + "print(\"ACC mean :\", acc_cv_reg.mean().round(3), \" std :\", acc_cv_reg.std().round(3))" + ] + }, + { + "cell_type": "markdown", + "id": "7f44ef9c-4efc-4c19-8d67-30a7230b5f35", + "metadata": {}, + "source": [ + "Courbe PR OOF + recherche du meilleur seuil F1 / F2" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "19de1770-99f2-4569-b5bc-b132af39acb9", + "metadata": {}, + "outputs": [], + "source": [ + "# Probabilités OOF sur TRAIN (méthode predict_proba)\n", + "oof_proba_reg = cross_val_predict(\n", + " pipe_rf_reg,\n", + " X_train,\n", + " y_train,\n", + " cv=cv,\n", + " method=\"predict_proba\",\n", + " n_jobs=-1\n", + ")[:, 1]" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "839afce3-150c-4034-bf9f-0444b2e1b919", + "metadata": {}, + "outputs": [], + "source": [ + "oof_true = y_train.values" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "d3893dad-5308-4b4a-bf1d-05e87b4db653", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Precision (PR AUC) OOF RF régularisé = 0.582\n" + ] + } + ], + "source": [ + "precisions_reg, recalls_reg, thresholds_reg = precision_recall_curve(oof_true, oof_proba_reg)\n", + "pr_auc_oof_reg = average_precision_score(oof_true, oof_proba_reg)\n", + "print(f\"Average Precision (PR AUC) OOF RF régularisé = {pr_auc_oof_reg:.3f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "cb10698d-b79f-416c-8378-465ebb4d7f46", + "metadata": {}, + "outputs": [], + "source": [ + "beta2 = 2.0" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "af40755f-bc42-4812-99c5-2220741cf327", + "metadata": {}, + "outputs": [], + "source": [ + "f1_scores_reg = 2 * (precisions_reg * recalls_reg) / (precisions_reg + recalls_reg + 1e-8)\n", + "f2_scores_reg = (1 + beta2**2) * (precisions_reg * recalls_reg) / (beta2**2 * precisions_reg + recalls_reg + 1e-8)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "e543dd9f-c799-4340-baee-aed19ee4db16", + "metadata": {}, + "outputs": [], + "source": [ + "best_idx_f1_reg = np.nanargmax(f1_scores_reg[:-1])\n", + "best_thr_f1_reg = thresholds_reg[best_idx_f1_reg]" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "25d584c0-646e-4731-acf4-f6e205062f29", + "metadata": {}, + "outputs": [], + "source": [ + "best_idx_f2_reg = np.nanargmax(f2_scores_reg[:-1])\n", + "best_thr_f2_reg = thresholds_reg[best_idx_f2_reg]" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "0e2610d1-52ce-4b2f-be1a-e36279a70c7d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Seuil F1 optimal RF régularisé : 0.427 | P=0.491 R=0.589 F1=0.536\n", + "Seuil F2 optimal RF régularisé : 0.330 | P=0.356 R=0.789 F2=0.635\n" + ] + } + ], + "source": [ + "print(f\"Seuil F1 optimal RF régularisé : {best_thr_f1_reg:.3f} | \"\n", + " f\"P={precisions_reg[best_idx_f1_reg]:.3f} R={recalls_reg[best_idx_f1_reg]:.3f} F1={f1_scores_reg[best_idx_f1_reg]:.3f}\")\n", + "print(f\"Seuil F2 optimal RF régularisé : {best_thr_f2_reg:.3f} | \"\n", + " f\"P={precisions_reg[best_idx_f2_reg]:.3f} R={recalls_reg[best_idx_f2_reg]:.3f} F2={f2_scores_reg[best_idx_f2_reg]:.3f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "9e9bf14e-0612-4175-89ab-56e61cb43c7b", + "metadata": {}, + "outputs": [ + { + "data": { + 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9a9asfM/bs2ePOsUfUy/g56NtgwYN0lavXn3dPt69e7f6uf/880++x/D/Bu8D//fwXfL09FSfO/7PFTaDe3GfY+7M4ZgRH/93ExISrrsvkRO6oOCQioiIigs1aaiTwZBf3tPgHRGmLsAQIKYvsFWYXBQzvTv6Qs5kHtY4ERGVEGZ5z/u3J4qUMRxnvOSKI8Ns4wsXLryhKTwsCcu04KQMDKMSmYMZJyKiEsIac5jsEpNvouYK67xhkk7U26DYGHVQRFS+sDiciKiEME0EzhzD3FJ60TdOOkDRPIMmovKJGSciIiIiM7HGiYiIiMhMDJyIiIiIzOTqiKcKYzVwTGjHxRyJiIhI0zQ1MSymzrjeQuYOFzghaCpqcVIiIiJyTBEREWpW/aI4XOCETJPeOVgywBIZLbw2grPrRa3Efrd3/L6z7x0Nv/Pls98TEhLUa+sxQlEcLnDSh+cQNFkqcELH47UZOJUd9rt1sN+th33PfnckOWV0bDWnhIcpESIiIiIzMXAiIiIiMhMDJyIiIiIzOVyNExGRNWVnZ0tmZmap1HzgtbDQMOspyw773T773c3NTVxcXEqlLQyciIjKaJ6YqKgouXr1aqm9Hg4kZ86c4Zx0ZYj9br/9HhAQICEhITf8/4WBExFRGdCDpqCgIPH29r7hX944kCBzhb+kOZlv2WG/21+/47kpKSly6dIldb9q1ao31BYGTkREFoa/lPWgqXLlyqXymjgYYMjC3d2dgVMZYr/bZ797eXmpawRP+H94I8N2LA4nIrIwvaYJmSYisg79/9+N1hgycCIiKiMcUiOy//9/DJyIiMgm7d27Vz744APJysqS8iYjI0PeeecdOXz4sLWbQsXEwImIiGzOlStX5J577pFGjRqJq2vpluPqZ2YhMLtRNWvWlBkzZhT7ec8995zs379fGjZseN19V69erfoBtXJUsDlz5kj//v2l3AdOGzZsUG+0WrVq6kv822+/Xfc569atk9atW4uHh4fUrVtX5s2bVyZtJSJyRMOHD1e/n3HBGU21atWSF198Uc2nY0zfx/hyyy23lLgQeOjQofLSSy/JHXfcIbZsx44dMmrUqGI956effpKDBw/KN998Y9bwEfr71VdfzVfQnJqaKpUqVZLAwEBJT08vMKjTP4sKFSqoY+fPP/8sJYXPfMyYMeoEBx8fHxXYRkdHm/390S99+/Y12efYsWNy5513qveBtejwvVm7dq3h8X379smQIUOkTp06qk4JQeTHH39s8hqPPPKI7N69W/755x8p14FTcnKytGjRQmbOnGnW/qdPn5Z+/fpJ9+7d1V8Kzz77rIwcOVKWL19u8bYSETkqHOguXrwop06dko8++kj+97//yaRJk/LtN3fuXLWffvnjjz+KNXSlw8H1zz//LHZAUpb09lapUqXYRf+DBg2SNWvWqDPErmfjxo1y8uRJFaTk9csvv0iTJk1U1qqwxMObb76pPos9e/bITTfdJIMHD5bNmzdLSYwbN06WLFmigq/169fLhQsX5O677zb7+6NffvzxR5PHERxjOBZ9smvXLhUXYBum8ABsQz/j+3XgwAF55ZVXZOLEifLZZ58ZXgN9ieDqk08+EYvTbASa8uuvvxa5z4svvqg1adLEZNvgwYO1Pn36mP1z4uPj1c/CtSVkZ2drp0+fVtdUdtjv1sF+N09qaqp26NAhdV1acnJytLS0NHVtScOGDdPuvPNOk21333231qpVq2L/DjfWtWtXbcyYMdozzzyjVa5cWevWrZvavn//fq1v375ahQoVtKCgIO2hhx7SYmJiDM9LSEjQhgwZonl7e2shISHa9OnT1WvhdYpqi7+/vzZ37lx1G7+jsc+ePXvU/aysLO2RRx7RatasqXl6emr169fXZsyYUWA/vPXWW1rVqlXVvlCjRg3to48+UrfxWUyaNEkLCwvT3N3d1X5PPfWU4TXweT333HNatWrVVPvbtWunrV27tsh+Qh/de++9BT6GPpszZ442e/ZsrVevXvkeN24bZGZmqp87YcIErbiuXr2qubm5aT///LNh2+HDh1U/btmypVjfH2P4bPEaGzZsMPmMsW3lypWFft9Hjx6tde/e3eS11q9fr/o9JSWl2P8PixMb2NU8Tlu2bJGePXuabOvTp4/KPBUG6UvjFGZCQoJh+nZcStOJS0ky6rtdKnJ2dT1d5L7OTk7ySKeaMqR9eKm2wVHpn2dpf6bEfi8N+F7ieK5fANepmTdWs5KRkS1ZUvzCaS83lxJNIgj4ix8Zixo1ahi2Ge+Td1tRMFT1xBNPqKwKxMXFSY8ePeTRRx+V6dOnq6GoCRMmqAwN6nz0rMemTZvk999/l+DgYJX5whANshTGP7ugthT0GegzUoeGhqohNAxD4f09/vjjapZp/Gwd2oChpL/++ksNW+Z9nUWLFqmMHDIqyAQhY4JhJn0/DHOhGByPo0Tl119/VdmYf//9V+rVq1dgH2Ho6YEHHsj3XpCFwjERWSc8hn5B7RY+l8I+Ewz1od04JmLb/PnzVf8XZenSpdK5c2fZuXOnOo3/1ltvNbxegwYNJDw8XPVX+/btiyyxwdxJFStWVCNGb7/9tmE+Mww14nXwXWjVqpUqw0G9EvbH0GJB3ydsi4+PV881frxNmzbq+Lt161bp1q1bgc/DpaBjRXGOHXYVOOFLiP8oxnAfwRD+g+kTXBl79913ZfLkyfm2R0REiK+vb6m272xsqpy5nHLt3n9p58LM3XhCbrmxCUzpGvxnwC9dfQydygb73Tw4MOtr1OnrbKVkZEurKf/VcZSlPa90F2938yYARLsxbIbflzgo4aCL94AAwXh4DTBUYlyHg6GVAQMGFPi6OFChThUHUePf1wiA3njjDcO22bNnq/1QE4RA5ttvv1UHWRzMAQdZ1F3h9Yzbg7bmbZ++Td+Oz0O/jeEf3X333aeCs4ULF8rAgQMN/YA6IZSW4P2jYB3P1QMv3EY5CY5JXbp0UQEK2tuyZUv12Llz51RN7vHjx1XQBE8//bT8/fff8uWXX8pbb71VYD+dPXtWBRF538sXX3yhEgdoE/Tq1Uu9zmuvvWbYx7htuKCIHQEH2of7CNq2b98uRUFbse/58+fVcJi3t7dJW9A2DNnlbZ8OgRZqmVFvhaHe119/XW677TY11Kd/VxCIIkBFUIq+xWtimBfvLe9nCggY8dkg8DR+HJ+Jv7+/Cio7duyYry34vNEfGC7MWy+WmJgo5TJwKgmMg44fP95wH0FWWFiYuuBDKk2Vg7NkQUCgRF+KluCgYHFyLvgAfiAyQd7667C4uLqpaJ1K7y96fK5c8LTssN/NL6pFNgAHU72upSSZotLi7o52mPfrHwcYZAlmzZql6lJx8MU21MrkhSyR8agAlrYorI4H/0+RITB+HMERDqgFza6OwAMHThz8cFDUn4faF2Qs9FmljQ+ieX+2vk3fbvx5ICBCoIefgz/EcUBG0KM/jvfcrFkzVRSNx/Tt+EMNj+H+/fffr+puULyMoOb2229XQQN+7tGjR9VBG69hDIEo3kNh/YS24GcaP47XQbYIn4W+/eGHH5YXXnhBJQr034FoGwJCBKL4DuJ1EJyiEBvQz+bOZK+f2eiep534GXn73thDDz1kuI0MEi4IhJGl0rNXOEYj4MQJY0iAIABETReCurzLo6CQHIEtAjDUPOeF5xt/Pnl/X+Gzwmt6enqaPKaPRpnVF2JHEL3nreDHfQRABWWbAGk/XPLCB13aB1hfL3dpV7uynHNNlvDwyoW+fva1jKCT5H7hqHTonyn7tGyx383rI+OzisDb3VUOvdmnxP2OA05GRqYKgoqbZS3uUB3+8teHkr7++muVFcI1htSM4YBU2JBTQXAgN25HUlKSCjSmTp2ab1+89okTJ9TtgjLLxtvyXgMCrryfgX57wYIFKuj48MMPpUOHDiq7hvmjtm3bZvIaenbH+Gcavw7+EEaAtGrVKlm5cqUamps2bZoKBhF04qCNQue82Y68/WAMZ5phuR7jx1esWCGRkZEqUDOGgAoF1sg+6fC+cGYbfgaCE+PXQfCFIcmiICOG7B76HwFJfHy8WizX+BiMx8z9PuHMOLwnZIUQZKO9yGhixEBPZiCgRh8iu4ihWv37jmFOBKQ4acA4s5Z3GgtkrApqj/45FXScKM5xw64CJ3yhMd5qDF9ObCcisif4BY7gqaRwIHGVHJU5KsvhaRxgXn75ZZUlwNBcYX+0lgSyEajZwbBOQXM31a5dW2WJMAWAnq3HgRxZCAw/6ZDBwXCMDsNjWOS1MBiWQxZr9OjRhm04sJcE+gPBHy4InHDGG+ZrQv0OAhuslaYPM5oDzzt06JDJtq+++koFTcbDizBlyhT1mHHghCAFGZ6CYBi1qNokQO2XHsyg71evXm04ww9BIjJ0xTkGY8jv8uXLhkyS/rkUFMgY1x0hG4mgCdNU4H0WBJ8ZMmvoM0uyaroDf11gWgF9EjKMD+M2Pgh9mA2dpEMRG8ZIMafFkSNHVOoYxXwoiiMiorKBoRJkTcydSsZcCDSQMUAxNIIjHAgx3cyIESNU0IFM0LBhw1QWBfP84GCKrJee0dOhwBxDZjgFH0XNOHbgoF8YZMmwH34WgjBkM/Dziws1TAhcUECPY9X333+vAikUbNevX18efPBBdUxbvHixOt5hKApDZ6jxKQyCBb14HmJiYtSUAOiHpk2bmlzw2piWAH1oDvQngqqiLnpgjNoh9PX48eNV3yNzhs8FQdPNN99seE0Eiqg90o/x+KxQrI2hagRdGCbE6+J9AZ6PonG8HxTSo//xHH36IUB/4jPF0B5+PuqdcUFf5C2kR3CNrFa5DZzwRUVkqEeH6BDcxtgl4C8GPYgCFADiC4YsE1LFSKtiLFT/AIiIyPKQDRo7dqy8//77agiqtKAQGdkfBEm9e/dW9UA4axpDQ3pGAnVUONhinh8M9XTq1EnVFBnXrODYgHpHZHaQFXv++eeLnGsJw1WYjwh1W8jAICNinH0yF9qJom20qXnz5mq4CUGOXkeEGioEN5g1HHVZKDw3zp4VBMEWAkRkdwDDVxgyRBCRF7Yh0EHAZgk4IeCOO+5QGSdk+FA+gyDQGNqJLCAguMYZg8hsIXBE4IXMFQIcvYQGGbFly5apIAvBUdu2bVWgiLMmcZwHnK2IIEk/GxHZKlwwL5UxPP7YY4+JpTlhTgJxICgAQ+SMD7a0i8MBqUUEe/iPUNiY6eYTsTLky23SINhXlo/7L71Mlu13Kn3sd/Ng+AB/QeOPv7xFqTdW45RbBOvIZ5IicMNwEoKlvDVXlmCNfkcGBscuTDzqqLTr9DuCSwReyFjhGF/c/4fFiQ14hCEiIruB4TdkFjCMh/mbkJEB/Uyx8gi1TBju4zx1hcMIFbJxhQVNpcmuisOJiIhwphqGhJB90Id+MORTXmEIEAX5VLi8k2NbEgMnIiKyG6iDRWEykbVwqI6IiIjITAyciIiIiMzEwImIiIjITAyciIiIiMzEwImIiIjITAyciIiIiMzEwImIiBzeww8/LO+8885116LDumrl2YQJE+Spp56ydjNsGgMnIiIq1PDhw9USF/oF66717dtXrUFWWt544w1p2bKlWfsZt0W/YE04fdkNrKNWs2ZNtX3GjBlm/XwsLrt06VJ5+umnDdvwGuY+3xKwhBQWucUae0FBQWrZlaysLLOem56ervoTfbB3717Ddkwa2r17dwkODlZLjmBB3FdffVUyMzMN+2Bdv2+++UYtUkwFY+BERGRPcrJFTv8jsn+ROJ3dlHvfwhAoYUkLXLDCPRb5xWKv1tCkSRNDW/QLFpyFlJQUFQy89957agFac3366ady3333iY+PT6m3F+urFRcWOUbQhOdu3rxZBTLIdr3++utmPf/FF19Ui+Hm5ebmphYZXrFihQqiEBhiUeJJkyYZ9sEM7H369JHZs2cXu92OgoETEZG9OPSHyIymIt/cIU6LR4r7D3eJfNwsd7sFYSV7BCK4IJOB4ZyIiAi1Yr0O9wcNGqSWB6lUqZJaO+7MmTOGx9etWyft2rWTChUqqH06deokZ8+eVQHB5MmTVdZHzyBhW2EQtOlt0S9YegVuuukm+eCDD+T+++9XbTY3SFm0aJH079/fsK1bt26qbePGjTO0ydjKlSulcePGKtDSg0rjDN3AgQNlypQpKnhp0KCBFBcCm0OHDsn333+v+vu2226Tt956S2bOnHndQOzvv/9Wz8eyNHkhqBwxYoS0aNFCrX03YMAAtdYflqwxhr5YsGBBsdvtKBg4ERHZAwRHPw0VSbhguj3hYu52CwdPuqSkJHVAr1u3rhq2Awz1IEvh6+urDsKbNm0yBBU40GOICcFE165d1RDfli1bZNSoUSogGTx4sDz33HMmmSRsKytoT3x8vLRt29awbfHixVK9enV58803DW3SIav10UcfqQVlN2zYoIbUMLxlDFk5ZHQQYP35559q2xNPPKH6pKiLDv3TrFkzNaSmQ/8mJCSo4cjCREdHy2OPPSbfffedGuK7nhMnTsiyZcvU52IMAe758+dNAl/6D9eqIyKydRiOW/aSiGj5HnJS25xElk0QadhPxNml1H88Dv76gT05OVmqVq2qtjk75/7tvXDhQsnJyZEvv/zSkJ2ZO3euyiwh04SgBMEJhvfq1KmjHm/UqJHh9fHaeibpevbv328SZCDzs3379hK/N2SWXFxcVB2RDhkzbEMgmLdNCBIxtIf2472OHTtWBVjGkFVDX+iZMMA+eQOswkRFRZkETaDfx2MF0TRNZbsQoKG/iwp6OnbsKLt371a1UAhg87ZfH+ZD36DWi0wxcCIisnVnN+fPNJnQRBIic/er1bnUfzwKivWal7i4OJk1a5YaPkLAgiEfDLMhe4FAw1haWpqcPHlSevfurQ7qyJr06tVLrWSPYT0EYMWFoa8//vgvu2bukFxhUlNT1WvkHY4rDDI5evAHeA+XLl0y2QfZIuOgCRCYGQdnpQ3BXGJiokycOPG6+yLQxb743FB0jmE91EXpvLy8DNk1yo+BExGRrUuKLt39igkZFAzN6ZBN8ff3V4XFb7/9thq+a9OmjcyfPz/fc6tUqWLIQOGsNQwN4cCNs7kwlHXzzTcXqy0ISIzbcqNQDI0AAUOKeYOdgqDA2hgCLmR78vZXXsgEYYizKOhHQJYrbxYNw3D6YwVZs2aNGuLLG0gi+4Q6JhSY68LCwgzZOtR4IeuE4VJk2eDKlSsmnx2ZYuBERGTrfIJLd78bhGABw3TI1kDr1q1VMISMip+fX6HPa9WqlbogK9KhQwf54YcfVOCEgAUHcGvQp0FAMbbxlAil3abiDNWhb1BcjkyWnqVCkIm+RbBTkE8++UQFsboLFy6oDB8+l/bt2xf6szDEiuFHXOuB04EDB1SAiLozyo+BE1lVVnaO/BsZLzUrV5BKFa7/1x6RQ6rRUcSvWm4heAF1TqrGCY9jPwtALYxeW4Ohus8++0xlR/Qz0ZDRwNlsOJMOAQIKq1EfgyJrDAHhwPz555+rs7hQP4PC6ePHj6tT4wF1NKdPn1ZzDuG5GPIryRAcskYIgPTbkZGR6jVRE1VYlgpZFQR+GzduNAmc0CYUf+tn6CEzdSOKM1SHoU0ESJiU8/3331d9jwzdmDFjDP2CjBT6D4XooaGhEh4ebvIaeh0YhhXRp4CMIAIiDCXidXbu3KmCWBTjG2fSUODfuXNnw5AdmeJZdVTmcnI02XHmirz++wG5+d3VcveszTJm/m5+EkSFQcF336nX7pjW4mj6/b7vWaQwHDC8hloeXJC92LFjh/z888/qtH297gdBBg7ed999tyqcfvTRR1WNE7IkePzIkSNqcsr69euroSEEAY8//rh6PrbjDDzUUiGQ+fHHH0vUTmRZ9KwWzoRD7Q5ujxw5ssjn4fG8w4wIAFFgjcCjrIeskPlB8T2ukX166KGHVJBkXMSN4UUEoMaTV14PCvCnTp2qzppr3ry5mgYCxe0YejWGqQhwdh4VzEnLOzhbzuF0TozN4wyPolLKJYV0J05PxS8Q/YyTvDafiJUhX26TBsG+snxc7sRtjuDEpST5eWeELNl3QS7Ep5k8VjfIR1aNNz0ltrT7nUof+908CCCQUalVq5aasbnEMOUAzq4zKhTX/ELFCUFT4wElf10HhyFHFJ1jWAuBSlFwyNTrocwtKLcnmAcK9U6YpgGBlq3QSqHfi/p/WJzYwHZ6hcql1IxsWbr/oizYcU52nIkzbPf1cJXeTUIkvJK3fLTqmFXbSGQ3EBxhyoGzm0VLjJJMz8riVqeLiAt/ld8IDElhXqbY2FhxdJhuAoX8thQ02Rr2DFnE6dhkmbvptPy6J1IS03LXV3JxdpLuDYLk3jbVpVuDKuLp5iJbT13mJ0BUHBiOw5QDmiYaZpG20PCco9GHHR3dvffea+0m2DwGTlSqqdSdZ+Pkiw2nZOXhaPxeV8IqecngtmFyX9swCfa7gWEKIiIiK2PgRKVS7L38YJT8b8Mp2Rtx1bC9R8MgGdGppnSqEyjOzuWvFoCIiBwPAye6oQzTuqMx8sHyo3LoYoLa5u7qLPe0DpVHb6kldYNMZxEmIiKydwycqERQm4SAadfZOEOxN7JLQzvWlECfG1sCgYiIyFYxcKJiF32/8cdBWX8sRt33cHWW4Z1qyhNd6khFTmBJRETlHAMnMktaZrbMWndS5qw7KRnZOeLq7CQPtAuXsT3qsuCbiIgcBgMnuq4Nx2LULN9nLueulN2lfhV5c0ATqRmYfyFLIiKi8oxTLFOh4pIz5Kkf98jQr7eroCnYz0NmDmkt34y4iUETEZUrXbp0UYsOF+WNN95QS7iUZ1ib78MPP7R2M2waAycq0Lqjl6TPjA1qeRTMJPBIp1pqSZR+zauWy2UGiKhgw4cPV//n9UvlypXVunJYkqO0ICAxXmC3qP2M26JfVq1apR7/4osv1OK0FStWVJeePXuqxXCv548//pDo6GgVNOjwur/99ptYC/oX7wVLg4SFhanFfoty+fJl9blgEWUs4IvnYB06LCWiw0LGnTp1Up8hZktv2LChfPTRRyavg8WEp0yZopYeoYIxcCITmdk58tafh2T43B1yKTFd6lSpIL+N6SSv928svp7/rZ5NRNaRnZMtO6J2yNLTS2Vn9E5139JwQMaiubisXr1aLcdxxx13iDU0adLE0Bb9gmwRrFu3Th544AFZu3atbNmyRQUPvXv3lsjIyCJf85NPPpERI0ZYZJ1LrK9WXAh20O4aNWrIrl275IMPPlBB4+eff17oc9D2O++8UwWBx44dk3nz5qmA8oknnjDsU6FCBRVMYUHmw4cPqyAJF+PXbdq0qVrY+Pvvvy/Bu3UMDJzI4GJ8qtz/+Vb5auNpdX94x5ry19OdpXn1APYSkQ1YdXaV9Pmljzyy/BGZ8M8EeWLtE9J3cV+13ZKQwQgJCVEXZIYmTJggEREREhOTe3Yt4P6gQYMkICBAKlWqpA7iZ86cMTyOoKZdu3bq4I19kPk4e/asOsBPnjxZ9u3bZ8ggYVthELTpbdEvWPgV5s+fL6NHj1ZtRDblyy+/VAtRI9grDN7DmjVrpH///oZtNWvWVNd33XWXao9+X4efg4VisSgsslSJiYkmS7cgOHn22WclMDBQ+vTpU+z+xusj4Pr6669VoIif8fTTT8v06dMLfQ4ybE8++aS0bdtWBVy33nqr6ot//vnHsA+GGRFY4jXxnh566CHVPuN9AH2xYMGCYrfbUTBwImXX2Styxycb1bxMvp6u8r+H28gbA5qo9eSIyPoQHI1fN16iU6JNtl9KuaS2Wzp40iUlJalsRN26ddWQD2RmZqoDsK+vrzoIb9q0SXx8fFSmCgFAVlaWDBw4ULp27aqGoJANGjVqlApKBg8eLM8995xJJgnbSkNKSopqGwK5wmD4ytvbWxo1amTYtmPHDnWNxW7RHv0+nDx5UpYsWaIuf/75p6xfv17ee+89k9f85ptvVDCHfpgzZ47adtttt6k+KeyC969D/yCLpgeEgP49evSoxMX9t1h6US5cuCCLFy9WfV6YPXv2yObNm/PtgwAXQ5zp6elm/SxHw7PqSH7dc15eWrRfTTPQqKqfzHmotdSoXMHml3nhMi7kKDAc997290STawtAGsE2J3GSqdunSvew7uJigUV/ESDg4A7JyclStWpVtU0f2lq4cKHK7CDDo9dAIuhAZgmZJmRBUDOD4T0MA4FxoILX1jNJ17N//35DW6Bx48aF1jG99NJLquYHtU6FQdYrODjYZJiuSpUq6hrtz9smvE/UUiFoxHt9+OGHVUYLdUG6evXq5atJQt+kpqYW2g43t/9KIaKiolRGyxjaqD+G7FJhkFH6/fff1c9C5gg/N6/q1aurTBsCWgwBjhw50uRx9BkCXvwsZK/IFAMnB18y5bM1J+TDlcfU/d6Ng2XG/S3F2902vxYZWTny295ItYjw5eQMWfZsZwny5aLBVP7tvrQ7X6Ypb/AUlRKl9rsp5KZS//ndu3eX2bNnq9vIeMyaNUtlUBCw4MCKYbYTJ06ojJOxtLQ0laFBvQ6KzJE16dWrlwpkMKyHAKy4GjRooOp4jIcRC4IsEIabELihwLowCDCKejwvDHEZv0+8h0uXLpns06ZNm3zPCw0NlbKAYu9JkyapOqeJEyfK+PHj1edlDFlBZA63bt2qhl2RPUTApUPhuJ6xo/xs8whJZZKxefuvw/L1ptx6pie61pEX+zSwySxOUnqW/LjtnKq9ikpIM2w/FZPMwIkcQkxKTKnuV1yoS8LBVYcsBup7kHl5++231UEYwQJqc/LSszfIQKFOZ9myZSpDhaLklStXys0331ystmD4yrgtBZk2bZoKnFAc3bx58yL3RR2SucNfeTNDgKwTslB5+ysvBJp5a4mMIQA9ePCguo0sF87yM6bfv15WTq/7Qo0XhihxZt5rr71mEqTq2axmzZqp10XWyThwunLlislnR6YYODmg7BxNJvzyr/y867y6//odjeWRW0zTwrYgIS1TvvrntMzddFoS0rLUtiBfD0nJyFbBFJGjqOJdpVT3u1EIFjC0pQ89tW7dWgVDQUFB4ufnV+jzUJyMCzIhHTp0UPMmIXBCMJSdXTpnB2KIDMNmy5cvV0OE14P2YEgKwZPxEBgCpNJqU3GH6tA3r7zyiqrP0rcjyES2rahhurz0gK6oWiXsk/fxAwcOqOE8BJWUHwMnB8w0vfLrfhU0uTg7yfv3NJd72lQXW5KSkSXzNp+R/60/JfGpmWpb7cAKMqpLbbmrdaj0+2SjnLiUZO1mEpWZ1kGtJdg7WBWCF1TnhBonPI79LAEHVgQXgADjs88+U1km/Uy0Bx98UJ0yjzPp3nzzTXXQRe0QipNffPFFFQDglPcBAwao+hkUOR8/flyGDh1qGP46ffq07N27Vz0XQ2GFDcEVZerUqfL666+rgAyvqbdZL8AuLHBCgIBCbuMpFvB81C7h7D+0pTgBS0GKM1Q3ZMgQdabho48+quq0EMh8/PHHJnMu/frrryoAPXLkiLq/dOlSlT266aab1HtF9uqFF15Q7dfPCpw5c6aEh4erbBRgWgJk55AJNIbMGIZXqWAMnOxg9m5/L7dSGUJDTdOkPw7Kgh0RalLLGYNbSv8W1cSW1sObv+2czF53QmKTcuc+qRvkI+N61pfbmobY5DAiUVlAwfeEdhPU2XMIkoyDJ9yHl9q9ZJHCcMDwmj7Ug6AGB96ff/5ZnXoPOCsNB2Ec5O+++251ej4CBZwSjwwUMi04wONsM0zUiNcaM2aMPP744+r599xzjwqyUEt19epVNayHmqjiQh0Wiprvvfdek+2o+cFwVEFcXFzUHE4YZjQOnDB7NuqDMByJ92I8tYKlYRh0xYoVqo8wBIrADgEhzkTUodgeAahxXRLaOm7cOBXoYg4rfBaoYTLOLiHYQpCKYnwU6iPY1D8HvS4NE3/iM6eCOWk4mjoQTCyGLyW+dEWllEsKX8xz586pqL6wydQ2n4iVIV9ukwbBvrJ8XO7EbQXZeeaKDPlimzzQLkwm39n0htv24Yqj8umaE4KTXj68r4Xc3dr6maatpy6ruaP8PF3VBJuRV3NT2TUqe8uzPevJgBahKjNmrOf09SrjtGDUzXJz7cpm9zuVPva7eXAwwsEKtSXFKUTOC1MO4Ow640LxEO8QFTT1rFH4mWNUNGSmMB3A7t27r3sWGQ6ZCM4wvFgeV1FA8IlsFgI3W6KVQr8X9f+wOLEBM0427PMNp9QUAYej/ptcraR+2hGhgiZ4e2BTmwiajKGGCZeq/p7yzK311PChm4v1AqBj0Ynq59fiQsZkQxAcYcoBnD2HYbsA1wBpH9peXF34q/xGoJj6q6++Un98Ofrp96ip+vTTT63dDJvG/202PIv36iOmp7iW1MbjsfLyr/vV7bHd68qD7W3nF0OwX27UX8HdRUZ3ryuP3lLLapNupmZky5J/L6jhwn0RV1Wbdr/eSzxcOQko2Q4Mx2HKAf0vcEsNzzkaTNBJkm9OJ8qPgZONWrgjQp39dqPOXk6WJ+fvkqwcTe5sWU2e611fbAkyOquf6yqVvN2lYoX/Zsk1B2Y5RyYN2akOtQufGfh6TsYkyfdbz8ovu84bzt6D5IxsScvIYeBEREQGDJxsUFZ2jizYHlEqxdZPfr9bEtOypHV4gLx/b3ObHJOvU6Xgs12u54PluYWRmAyzuIET/lrfeCJWzQ217uh/c9+EVfKSwW3DZNqK3ElBiYiIjDFwskEYojOe6LGkXv/9gBy6mCCVK7jLrAfblJvMiXue2qfiZOYQTP62J1JN/HksOndKA8SStzYMkoduriFd6lWRHE1j4ERERAVi4GSDfth2znBm2dnLJZvyfvHu8/LTzvNq2oFPHmglIf7lZ2mSl25rKFtOXlY1SPpyMdeTmJYp32/F7OOnDFMdeLu7yKC2YTK8Y02paVQEnpPtUCeaUhnKO8M0Ednf/z8GTjYmPiVTNp2IVbfva1O9RJmPC1dT1XxN8GzP+tKpbvma/bVr/SrqgsyROfNgzd18RuYZzT5ezd9ThneqKYNvCldzZF1PZnaOVc/wI/uHU6gxTQZWrMcyFqVxKjuGmzGxJA4GtjgEX16x3+2v3/UTKbCwMf4f4v/fjWDgZGNWH4lWhdyY46lWYPFrf/AFeemXf1VdU8uwABndLXclckdzNSVD5qw/Jd9uOaOWaIHaVSrI6G51VZG8uYHQiHnbZU/EVfnk/lY2NVko2Rf8ssbcMRcvXlTBU2nA/3UsCYIJHBk4lR32u/32OyZqLY25/hg42ZjlB3OXCOjTJLhEz8ep9P8cjxUPV2f5cFALcXWwTAnWsJu3+ax8seGUJF5bz65xVT8Z072u9G0akm8yzevZfe6quv73/FUGTnRD8FcufmlnZWWVyhpo+MsbgRhm4eakr2WH/W6f/Y6AC7Oll8YfGQycbAjmEVp/LPcMr95NQopd33QpIU3e+zt33aKX+jYs8dlq9ig9M1t+3hcrP+47JldScte3a1TVT57rVV9ubRRUrP8sCK6aV/eXc1dSJNDHg+viUanB9xATDBov6HojBxIcDDADMgOnssN+tw5b6ncGTjYEQVNaZo6EBnhJk2p+xQ6c3ll6WGVcMESHgmdHcSomSXpM3yAX49MMc0ON71Vf+jXDXyZOJTq4/Tq6kyqsf2/ZEQZORERkwMDJhqwwDNOFFDuduO3UZflt7wV1av2bdzZxqAVxL1wLmKpUcJXn+jSUe9uE3fAQZXGH9IiIyDEwcLIROHNr1eHchTtRi1PcCTP1s+geaBcuzasHiCPAMBpgWoInu9WRXuEuUq92mMXSuGdik8Xd1VmqBXhZ5PWJiMj2MXCyETtOX1Gny2OyyjY1KhbruQt2RMiRqEQJ8HaTF3o3EEfRqW5lWfREBzU0V9HbTS3QaSmYE+uLf05LpQrusuOVnsxIERE5KAZONmLr6SvqunO9wGIdlDET9qdrjqvbz9xar9jrvdkzDGe2rVmpTCYWjE/NLTi/kpyhsoNcWJWIyDE51rnqNp5xgptqVSr29APRCelqUsch7cMt1DrH1a1+kNSs7K0mIyUiImLGyQZkZOXInog4dbvdtQyKOVIysmT2uhPq9lO31is3a9HZkg51Ksu6F7qrJVt+3nXe2s0hIiIrY8bJBhy4EK+mIUCdTt0g8+de+mbzWbXuWnglb7mXGREiIiKLY+BkQ8N0qNcxdxoC1DZ98c8pdfvZnvW4lhoREVEZYOBkA3acuVLsYbpf90SqQuXqFb1kANdQIyIiKhMMnKwsJ0eTHWfiilUYjsUOv9p4Wt3GDOGOth4dERGRtfCIa2UnYpLUqe5ebi5qmRVzl2Y5cSlJfDxcZfBNYRZvIxEREeVi4GRl26/VN7WuEWB2nZKebRrUNkx8PW98sVAiIiIyDwMnG6lvusnM+qZj0Ynyz/FYtQDtiE6Os5AvERGRLWDgZGX7z8er69bh5i2zsmB7hLru1ThYwip5W7RtVLAtpy7LO0sPS3RC7uLCRETkODgBphWlZ2XLuSsp6nbDqr5mTZT5295IdZu1TdYzYu4Ode3r4aomHiUiIsfBjJMVRcSlSo4manHeKj4e191/3dFLagqCQB8P6VKvSpm0kXIVtH5gRrZl18cjIiLbw4yTFWUjahKR+sG+Zk18+cvu3CU/7m4dyikIypi3u6taRDkhLVOi4tPk7wNRZd0EIiKyAcw42YD6wddfZuVyUrqsPnxJ3b6nNRectYZxverLpP5NJNjPs8SvEXElRZYduChZzFYREdklZpxsADJO1/PHvguSlaNJs1B/aRBy/f3J8g5eSJCHv9omNSp7y9sDmxW575nYZPls7Qk14zsyjZ8NaSV3NK/Gj4mIyM4wcLKTwGnx7tyicC7mazvWHMnNAG46IYUGTidjkmTmmhOqqP/ayKyCSU+JiMj+MHCyg8DpwtVUOR+XquZu6te8apm1iwrm7e6irlGWpmkiRvGQwYlLifLpmhOyZN8FQ8DUo2GQmsIAmSoiIrJPDJysDGfIVargXuQ+CJqgbY1Kan+yrkduqaU+s1bhFeWe2ZtNHjsdmyzTVx6TP/+9oIIq6NkoWJ6+ta40rx4gj3+3UwVOeGzX2TgJDfCSEP+S10wREVHZYuBkB4Xhut5Ngi3aFjIPgteRnWtLTGK6YRsySR+vPi4Ld0QYzpbs0yRYnupRT5qG+ud7jal/H5HE9CxpGuonfz7VmV1PRGQnrH5W3cyZM6VmzZri6ekp7du3l+3btxe5/4wZM6RBgwbi5eUlYWFhMm7cOElLSyvX9U06zBZOtgfZo64frJUftp1TQROG5P56+hb538NtCwyaAEETXE7KKOPWEhGR3WacFi5cKOPHj5c5c+aooAlBUZ8+feTo0aMSFBSUb/8ffvhBJkyYIF9//bV07NhRjh07JsOHD1dzIE2fPl3Kc+DUINhXalSuYPH2UMmkZeZImxoV5aW+DaVdrcLXHcSw3YHIBGldo6KqfyIiIvti1cAJwc5jjz0mI0aMUPcRQP31118qMEKAlNfmzZulU6dOMmTIEHUfmaoHHnhAtm3bJvaqQYh5Q3UcprM9lVWdU4BaCufZnvWlZ6Og605kel/bMHXBGoUMnIiI7I/VhuoyMjJk165d0rNnz/8a4+ys7m/ZsqXA5yDLhOfow3mnTp2SpUuXyu233y72qm6QeRmn3o1DLN4WKh5nZyf5dXQn+evpzmoY1ZzZ34mIyL5ZLeMUGxsr2dnZEhxsWreD+0eOHCnwOcg04Xm33HKLaJomWVlZ8sQTT8jLL79c6M9JT09XF11CQu6p4Dk5OepS2vTXLfq1c4uHQ/w8xNfDpYh9c/er6u8pjav6WKS95YV5/W47crT/2mkvbS4P/V6esO/Z744kx8K/a4rzunZ1Vt26devknXfekVmzZqmaqBMnTsgzzzwjb731lrz22msFPufdd9+VyZMn59seEREhvr6lPwM3Arq4uDiVfSgsA1FJy5FbavrKzTV85dy5c4W+Vqhbltwc7iM96/mr9tKN9bstiYrJnWICwX9R3wFbZ2/9Xp6w79nvjkSz8O+axMRE2w+cAgMDxcXFRaKjo022435ISMHDUgiOHn74YRk5cqS636xZM0lOTpZRo0bJK6+8oob68po4caIqQDfOOOFsPFz8/PwsErXiA8brF9Qe3bd1al73tcJREN+gdim3sHwyt99tRbxLPAabxdXVVcLDwyUnR5NlB6Pk2y1n1Vl5o7rYx+dub/1enrDv2e+OJMfCv2v00SibDpzc3d2lTZs2snr1ahk4cKChY3B/7NixBT4nJSUlX4ch+AJ0aEE8PDzUJS+8jqV+0euvzQNJ2bKnfnd2ym0jvrarDl+Sj1Ydl8MXc//jxiZnyBPd6oq9sKd+L2/Y9+x3R+Jswd81xXlNqw7VIRM0bNgwadu2rbRr105NR4AMkn6W3dChQyU0NFQNt0H//v3VmXitWrUyDNUhC4XtegBFZE+iEtJk1He71G0sqaPmziz4bwAiIrIBVg2cBg8eLDExMfL6669LVFSUtGzZUpYtW2YoGEfth3EU+Oqrr6qxTVxHRkZKlSpVVNA0ZcoUK74LouIz/uMGa98N71hTLeHy2Lc72Z1ERDbM6sXhGJYrbGgOxeDGUA8yadIkdSGyZ5jQ9IF2YRLg7S6P3lJLLeOy48wVazeLiIhsPXAickSuLs7y7t3Nrd0MIiIqJlZzEhEREZmJgRMRERGRmRg4EdkZzPm0+1ycJKRlWrspREQOhzVORHYCc5WtOXJJPlxxTA5dTJDejYPl86Ftrd0sIiKHwsCJyA4Cpk0nLsu0FUdlb8RVw/aYpP/WYCQiorLBwInIhmGKgmnLj8q207lTFXi6OUvbGpVk44lYazeNiMghMXAiskH7z8fLByuOyoZjMeq+u4uzPHhzuDzZrY7si4hn4EREZCUMnIhszNkrKdL/s43qtquzkwy6KUzGdq8r1QK8ru2BBYKJiMgaGDgR2ZjsHE2cnEQGtgyVcT3rS3hlb2s3iYiIrmHgRGQjGoT4SqOqflLV31Oe611fmlTzv+5zohPSJCYxXZqGXn9fIiK6cQyciGyEn6eb/P1MZ7P3PxaVKJ2nrpWM7BxZMa6L1A/2tWj7iIiIE2AS2a3kjGwVNEFsYu7UBAci4+Xx73bKPbM3S3J6lsn+Wdk58suu8/LwV9sMRedERFQ8zDgR2ZmGIb5S0dtNalfxkbOXkyU2KUOORSfKd1vPyt8Hogz7HYlKlDY1KqqA6dc9kTJz7Qk5czlFPebn5SZd6lex4rsgIrJPDJyI7ExYJW/Z/VovcXJykj4fbVCB0xtLDqnHUFTu7OSkCswRMP20I0I+W3tCzl3JDZiMJ9U8GpUoaZnZ0iIswErvhIjI/jBwIrJDCJrAxTn3Gm5rGiLP9qwvo77bKWcvp8jIb3dKYlrucF3lCu4yqktt0UTkvb+PyNojMbJ0f5QKtHa80lMCfTys9l6IiOwJAyciO/b0rXVVEPRwhxr5zqxD0ISA6ImutWVI+3DxdneV77acUY+lZmara00TiU/NVIGVHowREVHhGDgR2bG+Tauqi7GejYJl1eFoGdqhpgxpFy5e7i6GxzrUqaxqpFpUD5A/9l1QAdTkJYdk++nL8kb/JnJ/u3ArvAsiIvvBwImonHntjsbqUpC6Qb6y7Nku6vbfBy5KaqYYzrDbfvoKAycioutwvt4ORFQ+Va+YOyN5iJ+nuo68mirP/7xPXly0TxWPExFRfsw4ETmoHx+7WZIysmTpvxdlytLDsu30FXWB8b0aSIh/bkBFRET/YeBE5KD8vd3Uxccz99cAasP1RFMOM05ERAVi4ETk4LCYMGY1aB1eUfp9ulEysnJnIyciovwYOBE5OJx1N/gmnk1HRGQOFocTUT5xKRmyZN8FSUzLZO8QERlhxomI8rlr5ma1gPATXevIhNsasoeIiK5hxomIDPS5wxE0QXxqhvxzPEa2nLzMXiIiYsaJiIzdf1OYHI1OFC83F1l7NEZ+3nleftweIW4uTrJvUm+1bAsRkSNjxomIDCbf2VQWjOqgzrCDrJzc+QkyszVJy+TZdkREDJyIKJ+ejYOlTY2K8lSPuuwdIiIjzLsTUT6NqvrJL092VEuvfLrmBHuIiOgaZpyIyCwnY5Lkiw2nJDYpnT1GRA6LGSciMst9c7ao6/jUTHm+TwP2GhE5JGaciKhIWMPOWEpGNnuMiBwWM05EVCgnJycZ272uRF5NldSMbPn7QFS+ffQ5n4iIHAEDJyIq0nO9c4fl3l92xGT7tlOX5ePVx9XkmDOHeMrtzauxJ4mo3GPgRETFcjQ6QQb/b4tsO33FsG1/ZDwDJyJyCAyciKhYNp3IXX4Fs4kH+njIxfg09iAROQwWhxORWfy83NS1u4uzDO1QQ9a/0F36Ng1h7xGRQ2HGiYjMgmAp2M9DOtQOlBB/T5PHLsSnyeQlByU0wEtGdq7NHiWicouBExGZBQv83tWqeoGP/b73grp2cXaSRzrVEmfnPHMYEBGVExyqI6IS83A1/RWSfW1RYCKi8oqBExGV2MM3h8tDrQPlq2Ft8j128EK8PP/zPpmz/iR7mIjKDQ7VEVGJVfX3kpHtgsWncoBh277zV2Xm2hOy6vAldd/LzUXua1NdDl5IkE51A9VwHhGRvWLgRESl6q5Zm03up2ZmS6epayQtM0em3ddC7m1TcJ0UEZE94FAdEd0w4ywSbt/TurrMH9nesA1BEyzYfk46vbdGnvpxD3udiOwSM05EdMP8vdzkyW51JCU9Sx69pbaEV/aWnBxNutSvoq4T0jLl3/PxsvNsnNo/MS2TvU5EdomBExGVipf6NjS5jykJvn2knbo9bflRFTgF+XrIpcR09jgR2S0GTkRkcc/2rCe3NQtR0xf0nL6BPU5Edos1TkRkca4uztKkmr84O/GMOiKybwyciIiIiMzEwImIiIjITAyciIiIiMzEwImIiIjITAyciIiIiMzEwImIiIjITAyciIiIiMzEwImIbEpmdu66dkREtoiBExGVucxsTV5ctE96f7RejkcnqvXslh+Mkv6fbpSmk5bL/vPx/FSIyCZxyRUiKnOpmdny087z6vb0lcfkVEyyHI1ONDx+OCpBmlX35ydDRDaHgRMRlRk/LzfBqiuaJuLp5ixpmTny94Eo9Zivh6t4uDlLbFIGPxEislkcqiOiMhPo4yG/PNlRlj7dWe5qFaq2BXi7yfhe9WXjhB7SvHoAPw0ismnMOBFRmWodXlFdT+jbSDrXqyJd6lcRHw/+KiIi+1Ci31bZ2dkyb948Wb16tVy6dElyckzPglmzZk1ptY+Iyil/bze5vVlVazeDiMjygdMzzzyjAqd+/fpJ06ZNxQlFC0RERETlXIkCpwULFshPP/0kt99+e+m3iIiIiKg8FYe7u7tL3bp1S781REREROUtcHruuefk448/Fg3nFBMRERE5iBIN1W3cuFHWrl0rf//9tzRp0kTc3NxMHl+8eHFptY+IiIjIvgOngIAAueuuu0q/NUREIhIZlyrTlh+VNjUrSvcGQewTIrLvwGnu3Lml3xIioms+Xn1cXVev6CW9G4fI8UuJMn1QS6lcwV3NPM4zeYnIWm5o1rmYmBg5evSout2gQQOpUqVKabWLiByQq7Pp1Cbn41Ll602n1e3R83fJgcgEuat1qDzbs55Ex6dzPTsiso/AKTk5WZ566in59ttvDZNfuri4yNChQ+XTTz8Vb2/v0m4nETmAx7vWkSq+HtIiLEBeXPSv2ubi7CTZOZrsOBOn7v+w7Zws2nleMrJz5NfRHaXVtZnIiYhsNnAaP368rF+/XpYsWSKdOnUyFIw//fTT6oy72bNnl3Y7icgBtKlRUV1wxm6Al5tU9nGXRbvOy4/bI0z2Q9AEMYnp6vrQhQT5fMNJuRCfJl883FbNSk5EZDOB0y+//CKLFi2Sbt26GbZhMkwvLy8ZNGgQAyciuiGoYerdJETdxsK/o7vVVevZPfDFVgnx95Tj0UkSeTVV9kZclR+3n5O1R2MMz90fGS+31AvkJ0BEthM4paSkSHBwcL7tQUFB6jEiotLi5uIsYZVyh/+XPdtFXd81a5MKnGatO6nuozTK1dlZZaI04fxyRGRjE2B26NBBJk2aJGlpaYZtqampMnnyZPUYEZElebu7qGt3F2cZ0j5c1jzXTeoE+bDTicg2AyfMGr5p0yapXr263HrrreoSFhYmmzdvVo8Vx8yZM6VmzZri6ekp7du3l+3btxe5/9WrV2XMmDFStWpV8fDwkPr168vSpUtL8jaIyE692q+xvNqvkWx8qbu8c1czqRlYwfDYqZhk+XDFUdkXcdWqbSSi8qlEQ3VNmzaV48ePy/z58+XIkSNq2wMPPCAPPvigqnMy18KFC1Wh+Zw5c1TQNGPGDOnTp4+a4gDDfnllZGRIr1691GOosQoNDZWzZ8+qCTmJyHE0quqnLgWZ9MdBdY36p+8ebV/GLSOi8q7E8zhhyoHHHnvshn749OnT1WuMGDFC3UcA9ddff8nXX38tEyZMyLc/tl+5ckVltvRlXpCtIiJydzVNoKdn5Z55R0RklcDpjz/+kNtuu00FLLhdlAEDBlz39ZA92rVrl0ycONGwzdnZWXr27ClbtmwptA2oocJQ3e+//64m3BwyZIi89NJLah6pgqSnp6uLLiEhQV1j/il9DqrSpL+uJV6b2O+2xpa+7+N71pONJ2LV2XcfrTouomk20S5H6HtHwn4vn/1enNc1O3AaOHCgREVFqWEy3C7qNOLs7Ozrvl5sbKzaL+/ZebivD//lderUKVmzZo0aEkRd04kTJ2T06NGSmZmpitUL8u6776qi9bwiIiLE19dXShvmn4mLi1P9wGUhyg773Tpsqd9reIjUaOIt607Gq/v4g+ncuXNSXtlS3zsS9nv57PfExMTSD5yMozFr/YWDn4vA7fPPP1cZpjZt2khkZKR88MEHhQZOyGihjso444RCdlz8/Pws0kZ8wHh9ZNCobLDfrcMW+z0w/iIWa1Enj4SHh0t5ZYt97wjY7+Wz3/XRKIuvVZf3bLfiFGkHBgaq4Cc6OtpkO+6HhOROfJcXzqTDUKHxsFyjRo1UJgxDf+7u7vmeg1+euOSFjrfULxv9tfnLrGyx363D1vrdyelaO5ycbKZNjtL3joL9Xv76vTivWaKfPnXqVHVGnO6+++6TSpUqqbPc9u3bZ9ZrIMhBxmj16tUmESXuFzYXFJZ3wfCcccbr2LFjKqAqKGgiIiIiKk0lCpxw9hvSZbBy5UpZtWqVLFu2TBWPv/DCC2a/DobQvvjiC/nmm2/k8OHD8uSTT6oFhPWz7LBosHHxOB7HWXXPPPOMCphwBt4777yjisWJiIiILK1EQ3UYGtMDpz///FOtT9e7d281NQDmYzLX4MGDJSYmRl5//XX1mi1btlQBmF4wjsJO4/QZfuby5ctl3Lhx0rx5c5XhQhCFs+qIiIzl5Giy7MBFiU3KkCHtwsUZ67IQEVkjcKpYsaI6Kw2BDAKdt99+W21H4ZY5Z9QZGzt2rLoUZN26dfm2YRhv69atJWk2ETmQnWfj1AVahgVI01B/azeJiBw1cLr77rvV/En16tWTy5cvqyE62LNnj9StW7e020hEZDZXl/yZpdTM4v1BR0RUqoHTRx99pIblkHV6//33xccnd3HNixcvqnmViIispWOdymrh39qBFWTupjMSeTWVHwYRWTdwwpQAzz//fL7tqD0iIrImX083tfAv/LCt/E6ASUQOtuQKERERkb2x2pIrREREROV2Hid9uRP9dmEXBk1EZGtWHoqWR+btkNWHTVcqICIqrlJbcoWIyFZ9vuGUYcqUWxuZLixORGTxmcOffvpp+eSTT/Jt/+yzz+TZZ58tyUsSEZU6H8/cvw31uS9zNHYyEVkhcPrll1/UunF5dezYURYtWnSDTSIiKh3T7mshH9/fUt68s6lh296Iq7LhWAy7mIjKbqgOk176++efhdfPz09iY2NL1hIiolJWP9hXXX7ZdV7d33QiVtZfC5o2vNBdwit7s8+JyPIZJ8wOjqVW8vr777+ldu3aJXlJIiKL0Ze8zDIaq1t79JK88cdBORAZz54nIstmnMaPH6/Wl8MCvT169FDbVq9eLR9++KHMmDGjJC9JRGQxneoESrcGVaRBsK/8vOu8XEnOkEl/HFSPxSaly2dDWrP3ichygdMjjzwi6enpMmXKFHnrrbfUNizBMnv2bBk6dGhJXpKIyGKC/Dxl3oh2hqkJEDjpMrNz2PNEZPnpCJ588kl1QdbJy8vLsF4dEZEtQ6H4/sh4Sc/Klhmrjlu7OUTkCDVOkJWVJatWrZLFixeruVHgwoULkpSUVJrtIyIqVbfUC5Qnu9WRKr4e7FkiKpuM09mzZ6Vv375y7tw5NWTXq1cv8fX1lalTp6r7c+bMKcnLEhEREZW/jNMzzzwjbdu2lbi4ODVMp7vrrrtUkTgRERFReVSijNM///wjmzdvFnd3d5PtKBCPjIwsrbYREVkcKg22nros6Vk50rV+FfY4EZV+4FTYYr7nz59XQ3ZERPZi1eFoWXEod/HfrRNvlRB/T2s3iYjK21Bd7969TeZrcnJyUkXhkyZNkttvv70020dEZBEuTk751q9LTMtU1+cup8iUvw7JO0sPG05+ISIqccZp2rRpqji8cePGkpaWJkOGDJHjx49LYGCg/Pjjj+xZIrJ53RoESe/GwdI01F++2HBKEtOz1Dp2mKLg7wMXDQHViE41par/f7WcROTYShQ4hYWFyb59+2ThwoXqGtmmRx99VB588EGTYnEiIluFIbnPh7ZVt+duOq2uX1j0b779srKZcSKiGwicMjMzpWHDhvLnn3+qQAkXIiJ75uHqgt9u4ubiJHe2DJWRnWvJXTM3S2pmtrz++wHZeSZO3r6rqXqMiBxbsQMnNzc3NTxHRFRevD2wqRyNTpR721SXYD/T4vC1R2PU9V//XpQLV9PEy81ZhneqZaWWEpFdFoePGTNGTXaJ2cOJiOxdz8bBMqZ7XZOgqU5QBXF1dpLqFXPLD3Dm3dRlR+SNJYckPiW3iJyIHE+Japx27NihJrpcsWKFNGvWTCpUqGDyOJZhISKyZz893kEyszRZtPu8vPXnIZPHMrgwMJHDKlHgFBAQIPfcc0/pt4aIyEZ4u7uKuIvc27q6WhC4Xc1Kcu+cLdZuFhHZU+CEiS8/+OADOXbsmGRkZEiPHj3kjTfe4Jl0RFRu+Xu7yehuddVtTP3EaZ2IHFuxapymTJkiL7/8svj4+EhoaKh88sknqt6JiIiIyBEUK3D69ttvZdasWbJ8+XL57bffZMmSJTJ//nyViSIichQ7z1yRD5YfkQtXU63dFCKy5aG6c+fOmSyp0rNnT7XcyoULF6R69eqWaB8Rkc15cv5udZ2Vo8nE2xpZuzlEZKsZJ0w/4OnpmW9eJ0yKSURU3nmqiTL/k57JbDuRoylWxgmLXQ4fPlw8PDwM2zAZ5hNPPGEyJQGnIyCi8ujDQS3U8Nzp2GSZv+1cvt+PSelZ4uvpZrI9J0cTZ+fcBYWJyMECp2HDhuXb9tBDD5Vme4iIbNbtzaqq6w9XHDVsy8jKkb/2X5C5m87Iv+fj5eP7W0q/ZlVl5aFombf5jGw7fUWaVPOTmMR0aRDiK9892t6K74CIyjRwmjt37g3/QCKi8mLjiVjpNHWNCop0/1t/St77+4hcjP9vaaqDFxLU9ZXkDKu0k4isvOQKERGJnLiUpIKmIF8PaRjiq7rk0MUEFTRVquAuwX65ZQ0tqvuzu4gceeZwIiJHhqE3TIbZMixAhnesKbc1rSp//ntBxv+0T5pX95dhHWpKv+ZVxdPNRc06fjUlU9q/s7pkPywnW+TsZpGkaBGfYJEaHUWcTYvUiajsMHAiIiqmvk2ryqHJfcXL/b8A5u7W1dViwb4ermqaFp2HOhOv8DOP/z1/VeZvPSebT8VK1/pVZH9kgtxcu1LuNAeH/hBZ9pJIwoX/nuBXTaTvVJGGd/BzI7ICBk5ERCVgHDTp/PKcUZdXjqbJG38clK2nLkunuoGy48wVVVCu+35r7pl6kXEpMrHGcZGfhuJ8PdMXSbiYu/2+b0QqtOBnR1TGGDgREZWRHE3UmXZwJCpRXbu7OEtGdu58UKiJik5Iz42VkGm6FjTp6+PlJrJwx0lk+asid/3Oz46ojDFwIiKyMGSifDxcJSUjSwVPUNXfU9VH3dc2TAK83CQqIU3iUzPlto//kaT0DBmU8qhs1xpJS6fjclILlV7Ou2S6++xrr6iJJEaKXD4pUqMmPz+iMsTAiYioDIb1Vj/XFXkiCfTxkNOXk6VW5QomE2NWC/CShLTcWqi0LJHtkruUy16tnrreltMw/wun505zQERlh9MREBGVgWA/Twny81TBUp0qPgXOJl69orcargv1cZKKkjuU187pcOEv6uFnySYTUQGYcSIishEYztsy4VZxlhyRGU1VIfjenNoyMOMt9fiJnGpyRguRHs57RfxCRSrXsXaTiRwOM05ERDZEZaIwTxOmHDASKVWkZ8Y0GZn5vKzPaS7S520RJ/4KJyprzDgREdmixgNEBn0rbks+FsmzUsv6Os/LiqM1xEdixOlgilxMSJe372wq/t5FT4dARDeOgRMRka1qPEAa1r9dnl68VrxykmVZpIfsi8mReUeQaYow2bV/86rSu0mI1ZpK5CgYOBER2TAXV1cZP6iXun34xz2yL8ZoFnE1M7mzpGflSHaOJocvJkhYJW9VK0VElsH/XUREduKtO5vKXa1D5eZalSUyLlkSLl+SKesvya6zcfLCon8lKT1LejUOli+GtrV2U4nKLVYWEhHZCdQwdW8QpOaFql3FRyp5u4rLtVkNEDTB8ehE+WT1cXn99wOSmpFt3QYTlUPMOBER2THMPl6pgocE+rqrte7OXE6R6SuPqcf6NAlRa+IRUelh4EREZMf6Ng2R25tXkwOR8YZFgl2dnSQrR5PMa2vgEVHp4VAdEVE50DTUX/4Y20k2vtRdGlb1tXZziMotZpyIiMqJ5tUDrN0EonKPGSciIiIiMzFwIiIqp64kZ8ji3ecl4kqKtZtCVG5wqI6IqJwa/9M+Q7F4v+ZVZdWhaOnWIEi2nb4iT3StLSM717Z2E4nsDgMnIqJyxtPVxeQ+zrD7fW/ujON/7b+ornE/M1uT1MxseebWeuKCxYWJ6LoYOBERlTOv9Gskm07EquVXnvtpn3i5uUjitQkyW1T3l33n42V/ZO4F+jQJlibV/K3caiL7wMCJiKicaRVeUV2gc70qUsHDRTyuZaE2HIuRoV9vV7eRZMrRRGWeiMg8LA4nIirHKlVwNwRN0LleoPz0eAc131O1AC+rto3IHjHjRETkQJycnKRdrUrWbgaR3WLGiYiIiMhMDJyIiIiIzMTAiYiIiMhMDJyIiBxcfGqmLD8YJfEpmdZuCpHNY3E4EZGDG3ZteoJ721SXafe1sHZziGwaM05ERA7Kw9X0EHD1WsbpVEySzFh1TAbN2SLrjl6yUuuIbBMzTkREDmrygKay+1ycJKVnyecbTsnR6AQZ8NlG+fd87oziELLbU61vR0S5mHEiInJQt9QLlKdvrSe1Ayuo+xFXUlXQhHXrQq9Njpmjmc4qnp6VLZqmydGoRJWZMldmdo6cu5xSyu+AqOwx40RE5OBahAVI5QruUiuwgtzZsprc3qyqLNl3Qd5YckiORyfJkC+2yuaTl6V5dX+TbBSG+v55sbucjEmWtjUripuLc75gCWvm/b0/Spbuv6jWy8PPCvByk/rBPvJKv8ZWeLdEN4aBExGRg2tU1U92vdarwMeORieKROfeNg6aID0rR9q9s1rddnV2kvrBvhLg7SYjO9eSpfujZOWhaHXGnrF9EVfV9fpjMRKblKEyVx/c15yLDJPdYOBERET5NK7mr4KhmoEV5MSlJGkQ7Ct9mobI+bgUtd7dy4sPSGpmtmH/rBxNDl1MULeRndIF+nhI36bBkpmlycKdEVK9opecj0tVj/26J1Jdrzsaw8CJ7AYDJyIiygfr2R16s6+4uTip9e3ycnF2ltMxySrD9OmaE9Kkmp/KIgGG/fq3qCa3NQ2RtjUrqZopeOfuZur6rlmbJC4lw1BX9c/xGNl66rI0C/WXF/s25KdBNo2BExERFcg9z3QFxga0qGa4PaxjTXV9JjZZBUQtqgeI87VgyZgeQP0x9hZ1/dKif2XhlQjZeuqKur/rbBwDJ7J5PKuOiIhKBYb1WoVXLDBoKkhuQbmTNA31U/fznMBHZJNsInCaOXOm1KxZUzw9PaV9+/ayfXvuLLbXs2DBApVCHjhwoMXbSEREpeu+tmFyfMrtMvvBNuxashtWD5wWLlwo48ePl0mTJsnu3bulRYsW0qdPH7l0qejZas+cOSPPP/+8dO7cuczaSkRERI7N6oHT9OnT5bHHHpMRI0ZI48aNZc6cOeLt7S1ff/11oc/Jzs6WBx98UCZPniy1a9cu0/YSERGR47Jq4JSRkSG7du2Snj17/tcgZ2d1f8uWLYU+780335SgoCB59NFHy6ilRERERFY+qy42NlZlj4KDg0224/6RI0cKfM7GjRvlq6++kr1795r1M9LT09VFl5CQO89ITk6OupQ2/XUt8drEfrc1/L6z70uDpv33+9LWf3fyO18++704r2tX0xEkJibKww8/LF988YUEBgaa9Zx3331XDenlFRERIb6+vqXeRqzhFBcXp4rWC5r7hCyD/W4d7HfrKU99fzEhwxBAnTt3TmxZeep3e6JZuN8RX9hF4ITgx8XFRaKjr83nfw3uh4SE5Nv/5MmTqii8f//++aJEV1dXOXr0qNSpU8fkORMnTlTF58YZp7CwMHXx88s9BbY0oT34gPH6GHakssF+tw72u/WUp753jsPiv8fFyclZwsPDxZaVp363JzkW7nd9NMrmAyd3d3dp06aNrF692jClADoH98eOHZtv/4YNG8r+/ftNtr366qsqUvz4449Vh+bl4eGhLnmh4y31pddfm/+pyhb73TrY79ZTXvoeAZPOHt5Leel3e+NswX4vzmtafagO2aBhw4ZJ27ZtpV27djJjxgxJTk5WZ9nB0KFDJTQ0VA25YZ6npk2bmjw/ICBAXefdTkRE9gdZBSwe7OnmYu2mENlm4DR48GCJiYmR119/XaKioqRly5aybNkyQ8E4xrsZ1RMRlX/pWdnS9YN1Enk1Vb57tJ10rGNeLSuRQwVOgGG5gobmYN26dUU+d968eRZqFRERlQV9DbscTeTcFdQ7iWw8HiuxSRnSKMRXqvh6SFxKptSs7M2CbLI6mwiciIjIcVX195ShHWpISka2HI1KlP2R8TJr3UmTwCo7R5MfH7tZOtSpbNW2ErGyjYiIrAqnl795Z1OZdl8L6VhAYISgCU7FJlmhdUSmmHEiIiKbMaZHXbm5dmVpVNVPtp66LFdTMmTpgSjZfvqKrDl8SdYfjZFejYPVAsFE1sDAiYiIbIafp5t0bxikbg9sFaqut5y6rK5XH8ld/P3QxQQGTmQ1HKojIiKb1rV+kPh6uErDEF+ToTsia2DGiYiIbNqQ9uHqsv98vPT/bKNkZufI73sj5WpKpvRpEiJ7I65K01A/qV7R29pNJQfAwImIiOwKpil4ZkHuQu+T/jiortvXqiQLH+8g5y6nyLpjl2TX2TgZ0KKa3NrIdBF5ohvFwImIiOxC1QBP8XB1VjOL54UpDHp8uE5OxSQbtp2MSZJKFdzlwtU0Sc7Ikn0RV1Uw1b42pzSgkmPgREREdiHQx0M2vNhd1Th5ubnI7nNxkpmtyRPf71JzQCFocnV2kvBK3nIqNlkORCbIXbM2m7zG8UtJ8vLtjdTtlmG5S3YVJDsnW3Zf2i0xKTFSxbuKtA5qLS7OXAaGGDgREZEdCfbzNNzGMByCqAfahUtWdo46G++WeoFy8Wqa9JmxweR5IX6eEpWQpqY1GDhzk9q2+rmuUqeKT76fsersKnlv+3sSnRL938/1DpYJ7SZIj7AeFn1/ZPuYcSIiIruFWcXfvbuZyTa/EDdZMOpmcXZyklbhuVmlQxcS5M5rAZPu5KUkNU9UgxA/8fFwNQRN49eNF01Mz9y7lHJJbf+wy4dSz6mexd8X2S5OR0BEROUOJtFsV6uSuLk4q0uLsAD5/tH28tPjHaROlQpqn1Hf7ZJ7Zm+RN5ccNAzPIdOUN2gCfdu0ndMkR8tfY0WOgxknIiJyCBjGgyBfTzlpVER+5nKKKhw/dvWQRCVdkeyU+pKVXE+yk+uKk0uqeIV/JU5O2Sp4wvDducRzUlNqWvGdkDUxcCIiIocy9Z7mqrD8YnyaTF12RNU9/TeM91a+/XPSg8TF86LhflIG18xzZAyciIjIoYRX9laXLSdzl3LJy8n1qrhWOC6Zic1FcjzyPe7jnr+gnBwHAyciInJIN9euJPNG3KQKw3093WRvxBWZeeR5ics+KuKkSdbxBqIZBU5O4qTOrgv3Dbdqu8m6GDgREZFDcnJykm4NchcUhgYhvlI56HF19hzCJJN9r91/vu3z4uzE86ocGT99IiKia3rW6CnTu02XIO//AioUibsl9JOXW38ot9a4lX3l4Bg4ERER5Qmelt+zXAI8cueASr/UTy5H3iJ7jnLdO2LgRERElA+WV2kWmrumnbd77lIriemZ7ClijRMREVFB5g6/SeJSMuTv/Rfltd9zJ8kk4lAdERFRIcu5YGFhImMMnIiIiIjMxOkIiIiIzJCdo8mByHhZujdWjqy9pG73aRIifl5uUr2ilzzYvgb70QEwcCIiIjLD8oPR6mJs/rZzhtsIolBI7u3OQ2t5xqE6IiKiIlTx9TTc9vFwkQ41fKRfsxB1H5kmp2tzZfaavl4av75cftoRwf4sxxgWExERFaFX42B1hl3FCu7SOMRHLkSel/DwcPn0ASdxdnaS1m+tlCvJGRKXkjtdwZ6IqzLopjD2aTnFwImIiOg6Z9d1b5g7k3hOTo5hO4Im+Pj+lrIv4qoci06SP/ZdYF+WcxyqIyIiugGd61WRsT3qSd0gH/ajA2DgRERERGQmBk5EREREZmLgRERERGQmBk5EREREZmLgRERERGQmBk5EREREZmLgRERERGQmBk5EREREZmLgRERERGQmBk5ERESlaM+5OHl03g557+8j7NdyiIETERFRKchduU7kSFSirD5ySeasPynx1xb+pfKDgRMREVEp6Ns0RNrWqCh9m4QYtmVrGvu2nHG1dgOIiIjKg3rBvrLoyY6iaZrUmrjU2s0hC2HGiYiIiMhMDJyIiIgsJCE1U45HJ0p2DofsygsGTkRERBbSbdo66fXRBvlh+zn2cTnBwImIiKiUBfp4mNw/HZPMPi4nWBxORERUipycnGTBqJvldGyyrDlySX5ktqlcYcaJiIiolNUN8pFejYOlorcb+7acYeBEREREZCYO1REREVnYkn8vyMId5yQ5I1tNkhmblC4fDmopbWpUZN/bGQZOREREFuLt7qKuYxLTDdt2no1T16sPRxsCJyzNEpeSIZ5uLnIgMl7qBftIjcoV+LnYIAZOREREFjL4pnBJyciWIF8POXYpSbACy6mYJNl2+orsOhsn4xfulb0RV+VUbP6z7lqFB8iec1dlcNswycrRpEv9QLmzZSg/Kytj4ERERGQhVXw95MW+DU22vbnkkAqccCkKgiZYuDNCXa8/FsPAyQYwcCIiIipDtzULkU0nYiXY31NahgWozFLzUH+JT82UYD9P+XlnhJy5nKLurz16SeoH+8r201ckIyubn5MNYOBERERUhm6qWUmWj+uSb3vla5NmDu9Uy2Q7hvZ6fLi+zNpHReN0BERERERmYuBEREREZCYO1REREdmBtKwcmbh4v2w8ESM9GgRJTFK69G4cIgNb8Uy7ssTAiYiIyIY5Ozmp64ysHMO6d99sOauu90XEM3AqYwyciIiIbFiNyt5yb5vqcjE+Va6mZMrF+DRpUs1P/jkeK5nZOdZunsNh4ERERGTDnJycZNp9LUy2HbwQL/8c32i1NjkyFocTERHZMU3TJDtHs3YzHAYDJyIiIjt1OTlDWr65UlpMXiHHoxOt3RyHwMCJiIjIzvh5uqlrZJoww3hSepbsichdooUsi4ETERGRnQmr5C2fP9xG3ru7mVq2BT5ZfVxav7VS3l92xNrNK9dYHE5ERGSHejcJUdebTl6WvRFX5Xxcqrr/655IaR1eUbI1TXo3DlbF5VR6GDgRERHZsXE960mdKhXUPE+z1p1U0xWM/HaneuyHke3lplqVJC4lQw5fTBQXJyfpVLcyg6kbwMCJiIjIjtWu4iPP9qwvV1MyZP62c6reCQFSRnaOPDl/t6qBMvbbmE6G4T0qPgZORERE5UCAt7vsfLWnKhgf/9NeWbo/yiRocnF2Uo+9s/Swuv/QzTVkQItqVmyxfWLgREREVE64uTiLm4vIq/0aS+d6VSS8krfUCqwgAd5uMnzuDtl++oq6AK5nrDwmHm4usvDxmw1n6lHRGDgRERGVM9UCvOSBduEm2565tZ4s2nVeouLTZMupy2rbqdhkdf3lP6elkreb3NooWJ2xR4Vj4EREROQAOtUNVJecHE2+2XJGsrI1mbf5jEReTVVTGcCUpYdlfK8GcuhigjQP9ZcL8alqnbwm1fyt3XybwcCJiIjIgTg7O8mITrXU7aupGfLVxtOSlpm7WHBmtiZTr80DtWTfBXV94lKSPNKplhruaxVeURwdAyciIiIH9UKfhvJ87wYSm5QhD325TTTR5Fh0klT191TF5ocvJsg/x2PVBZpU85Ozl1MkNTNbwip6yZnLKTK2e11VLxURlyKYMapmYAX57tH2qhi9PGLgRERE5MAwQWYVXw9ZPq6LyfYjUQky4NNNKphCJgoOXkgwPI6gCT5be8LkeRfi06TL+2vF081ZJvVvIl3qV5HyhIETERER5dMwxE/+faO3yhxtPXVZVh++JNUCPFXw1CosQDYcj5WYxHSVYUrNyFKF5RMX71fPRd0UjJ6/W0Z2riX1gnylX/Oq5aKXGTgRERFRgTwxt4GImtoAF2PDr9VJGate0Ut2nL4iR6ISZcWhaDUZ54xVx1Xw1bl+YLmY8sAmFvmdOXOm1KxZUzw9PaV9+/ayffv2Qvf94osvpHPnzlKxYkV16dmzZ5H7ExERUdnoXK+KjO/dQMb3ri8NQ3yldXjuDOWYeDP9WgG6vbN64LRw4UIZP368TJo0SXbv3i0tWrSQPn36yKVLlwrcf926dfLAAw/I2rVrZcuWLRIWFia9e/eWyMjIMm87ERERFTzMt+zZLrJ4dCcpb2sMWz1wmj59ujz22GMyYsQIady4scyZM0e8vb3l66+/LnD/+fPny+jRo6Vly5bSsGFD+fLLLyUnJ0dWr15d5m0nIiIix2LVwCkjI0N27dqlhtsMDXJ2VveRTTJHSkqKZGZmSqVKlSzYUiIiIiIrF4fHxsZKdna2BAcHm2zH/SNHcifgup6XXnpJqlWrZhJ8GUtPT1cXXUJC7qmUyFLhUtr017XEaxP73dbw+86+dzT8zpfcjRwbLd3vxXlduz6r7r333pMFCxaouicUlhfk3XfflcmTJ+fbHhERIb6+vqXeJk3TJC4uTs2LgQuVDfa7dbDfrYd9z363G1ruFWqRU71dbfL7npiYaB+BU2BgoLi4uEh0dLTJdtwPCQkp8rnTpk1TgdOqVaukefPmhe43ceJEVXxunHFCQTkufn5+YomoFR8wXh/DjlQ22O/WwX63HvY9+91eaHJQXc/cHifurs7yzK11pX6wr0193/XRKJsPnNzd3aVNmzaqsHvgwIFqm17oPXbs2EKf9/7778uUKVNk+fLl0rZt2yJ/hoeHh7rkhY63VGCjvzYDp7LFfrcO9rv1sO/Z7/bA3dVZMrJyZPWR3LPlQwO85NU7GtvU9704r2n1oTpkg4YNG6YCoHbt2smMGTMkOTlZnWUHQ4cOldDQUDXkBlOnTpXXX39dfvjhBzX3U1RUlNru4+OjLkRERGQ73ujfRM08jrXs9py7Klk518bu7JTVA6fBgwdLTEyMCoYQBGGagWXLlhkKxs+dO2cSCc6ePVudjXfvvfeavA7mgXrjjTfKvP1ERERUuCHtw9XlwxVHVeBk76weOAGG5QobmkPht7EzZ86UUauIiIiITLF6mYiIiMpUYlqmWobFHjFwIiIiojIzb/MZafbGCrnt4w2SlZ0jaZnZdtX7NjFUR0REROVbtQAvk/vHopOk7it/i4uzk7x7dzNpXNVPwip6i7+3m9gyBk5ERERkcfe1qS71gnzEw9VFBv1vi6ReyzRhyO7FRf8apir458Xu4uxsuxNIM3AiIiIiywccLs7StmbuurIrx3eR07HJsvNMnHy8+rhhn8irqZKtaeIsDJyIiIiIlOoVvdWlc70q8uDN4ZKVrUnH99aIPWDGiYiIiKwmyNdT4lMyDfdf//2AGr4b16u+VPU3rYuyBQyciIiIyKpcXP4bmvtxe4S6vpyUIQNaVpMm1fyldqC32ApOR0BERERW5ePhKq/c3kj6t6hm2Ia17Z5ZsFce+nKbJKRlytXULLEFzDgRERGR1T3Wpba6HtCimjz/8z5JyciSzGxNohLSpOWbq9RjXw/zlR6NcpdksxZmnIiIiMhm9GocLPsm9Zb9b/SRgDxzOh2JShBrY8aJiIiIbI6nm4use76bRCeky7nLSXLozEXpXC/Q2s1i4ERERES2KcDbXV3qBVWQehXSJbyav7WbxKE6IiIiInOxxomIiIjITAyciIiIiMzEwImIiIjITAyciIiIiMzEwImIiIjITAyciIiIiMzEwImIiIjITAyciIiIiMzEwImIiIjITAyciIiIiMzEwImIiIjITAyciIiIiMzEwImIiIjITAyciIiIiMzkKg5G0zR1nZCQYJHXz8nJkcTERPX6zs6MS8sK+9062O/Ww75nvzuSHAsfW/WYQI8RiuJwgRM6HsLCwqzdFCIiIrKxGMHf37/IfZw0c8Krcha1XrhwQXx9fcXJyckiUSuCsoiICPHz8yv11yf2uy3h951972j4nS+f/Y5QCEFTtWrVrpvRcriMEzqkevXqFv85+GAZOJU99rt1sN+th33PfnckfhY8tl4v06RjEQ4RERGRmRg4EREREZmJgVMp8/DwkEmTJqlrKjvsd+tgv1sP+5797kg8bOjY6nDF4UREREQlxYwTERERkZkYOBERERGZiYETERERkZkYOJXAzJkzpWbNmuLp6Snt27eX7du3F7n/zz//LA0bNlT7N2vWTJYuXVqSH+vwitPvX3zxhXTu3FkqVqyoLj179rzu50Sl833XLViwQE0yO3DgQHZtGfX91atXZcyYMVK1alVVRFu/fn3+vimDfp8xY4Y0aNBAvLy81CSN48aNk7S0tJL8aIe1YcMG6d+/v5qAEr83fvvtt+s+Z926ddK6dWv1Xa9bt67MmzevTNqK2TKpGBYsWKC5u7trX3/9tXbw4EHtscce0wICArTo6OgC99+0aZPm4uKivf/++9qhQ4e0V199VXNzc9P279/Pfrdgvw8ZMkSbOXOmtmfPHu3w4cPa8OHDNX9/f+38+fPsdwv2u+706dNaaGio1rlzZ+3OO+9kn5dB36enp2tt27bVbr/9dm3jxo3qM1i3bp22d+9e9r8F+33+/Pmah4eHukafL1++XKtatao2btw49nsxLF26VHvllVe0xYsX44Q17ddffy1y/1OnTmne3t7a+PHj1bH1008/VcfaZcuWaZbGwKmY2rVrp40ZM8ZwPzs7W6tWrZr27rvvFrj/oEGDtH79+plsa9++vfb444+X5PNyWMXt97yysrI0X19f7ZtvvrFgK8ufkvQ7+rpjx47al19+qQ0bNoyBUxn1/ezZs7XatWtrGRkZJf2RVIJ+x749evQw2YaDeadOndifJWRO4PTiiy9qTZo0Mdk2ePBgrU+fPpqlcaiuGDIyMmTXrl1q2Md4CRfc37JlS4HPwXbj/aFPnz6F7k+l0+95paSkSGZmplSqVIldbOF+f/PNNyUoKEgeffRR9nUZ9v0ff/whHTp0UEN1wcHB0rRpU3nnnXckOzubn4MF+71jx47qOfpw3qlTp9Tw6O23385+tyBrHlsdbq26GxEbG6t+CeGXkjHcP3LkSIHPiYqKKnB/bCfL9XteL730kho7z/sfjUq33zdu3ChfffWV7N27l11bxn2PA/aaNWvkwQcfVAfuEydOyOjRo9UfDJg4kCzT70OGDFHPu+WWW9RCsVlZWfLEE0/Iyy+/zC63oMKOrVgMODU1VdWbWQozTlTuvffee6pQ+ddff1XFnmQZWFn84YcfVoX5gYGB7OYylpOTozJ9n3/+ubRp00YGDx4sr7zyisyZM4efhQWhQBmZvVmzZsnu3btl8eLF8tdff8lbb73Ffi+nmHEqBhwMXFxcJDo62mQ77oeEhBT4HGwvzv5UOv2umzZtmgqcVq1aJc2bN2f3WrDfT548KWfOnFFnxhgfzMHV1VWOHj0qderU4Wdggb4HnEnn5uamnqdr1KiR+sscQ1Du7u7sewv0+2uvvab+YBg5cqS6jzOnk5OTZdSoUSpwxVAflb7Cjq1+fn4WzTYBP9FiwC8e/CW3evVqkwMD7qO2oCDYbrw/rFy5stD9qXT6Hd5//331V9+yZcukbdu27FoL9zum3Ni/f78aptMvAwYMkO7du6vbOE2bLNP30KlTJzU8pwercOzYMRVQMWiyXL+jfjJvcKQHr1zRzHKsemy1ePl5OTxVFaeezps3T50COWrUKHWqalRUlHr84Ycf1iZMmGAyHYGrq6s2bdo0dVr8pEmTOB1BGfT7e++9p04pXrRokXbx4kXDJTEx8ca/BA6kuP2eF8+qK7u+P3funDpzdOzYsdrRo0e1P//8UwsKCtLefvvtG2iF4yluv+N3Ovr9xx9/VKfIr1ixQqtTp446o5rMh9/NmD4GF4Qm06dPV7fPnj2rHkefo+/zTkfwwgsvqGMrpp/hdAQ2DPNFhIeHqwMzTl3dunWr4bGuXbuqg4Wxn376Satfv77aH6dP/vXXX1ZotWP1e40aNdR/vrwX/JIjy/V7XgycyrbvN2/erKY7wYEfUxNMmTJFTQ9Bluv3zMxM7Y033lDBkqenpxYWFqaNHj1ai4uLY7cXw9q1awv8na33Na7R93mf07JlS/U54fs+d+5crSw44R/L57WIiIiI7B9rnIiIiIjMxMCJiIiIyEwMnIiIiIjMxMCJiIiIyEwMnIiIiIjMxMCJiIiIyEwMnIiIiIjMxMCJiIiIyEwMnIiIisnJyUl+++03dRsLG+M+1uMjovKPgRMR2ZXhw4erQAUXNzc3qVWrlrz44ouSlpZm7aYRkQNwtXYDiIiKq2/fvjJ37lzJzMyUXbt2ybBhw1QgNXXqVHYmEVkUM05EZHc8PDwkJCREwsLCZODAgdKzZ09ZuXKleiwnJ0feffddlYny8vKSFi1ayKJFi0yef/DgQbnjjjvEz89PfH19pXPnznLy5En12I4dO6RXr14SGBgo/v7+0rVrV9m9e7dV3icR2R4GTkRk1w4cOCCbN28Wd3d3dR9B07fffitz5sxRAdK4cePkoYcekvXr16vHIyMjpUuXLir4WrNmjcpYPfLII5KVlaUeT0xMVBmsjRs3ytatW6VevXpy++23q+1ERByqIyK78+eff4qPj48KdtLT08XZ2Vk+++wzdfudd96RVatWSYcOHdS+tWvXVkHQ//73P5U9mjlzpsokLViwQNVIQf369Q2v3aNHD5Of9fnnn0tAQIAKvJClIiLHxsCJiOxO9+7dZfbs2ZKcnCwfffSRuLq6yj333KMyTCkpKWqozVhGRoa0atVK3cbZbxia04OmvKKjo+XVV1+VdevWyaVLlyQ7O1u95rlz58rkvRGRbWPgRER2p0KFClK3bl11++uvv1Z1TF999ZU0bdpUbfvrr78kNDTU5DkYmgPUPRUFw3SXL1+Wjz/+WGrUqKGeh+wVgi8iIgZORGTXMEz38ssvy/jx4+XYsWMq0EF2CMNyBWnevLl888036oy8grJOmzZtklmzZqm6JoiIiJDY2FiLvw8isg8sDiciu3ffffeJi4uLqmN6/vnnVUE4giOcKYcz4j799FN1H8aOHSsJCQly//33y86dO+X48ePy3XffydGjR9XjKAbH/cOHD8u2bdvkwQcfvG6WiogcBzNORGT3UOOEgOj999+X06dPS5UqVdTZdadOnVKF3a1bt1ZZKahcubI6m+6FF15QWSkEXC1btpROnTqpxzHkN2rUKPUcTHeAYnMEY0RE4KRpmsauICIiIro+DtURERERmYmBExEREZGZGDgRERERmYmBExEREZGZGDgRERERmYmBExEREZGZGDgRERERmYmBExEREZGZGDgRERERmYmBExEREZGZGDgRERERmYmBExEREZGY5/8tz0D1WycsFAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualisation\n", + "plt.figure(figsize=(6,5))\n", + "plt.plot(recalls_reg, precisions_reg, label=f\"RF régularisé (AP={pr_auc_oof_reg:.3f})\")\n", + "plt.scatter(recalls_reg[best_idx_f1_reg], precisions_reg[best_idx_f1_reg],\n", + " label=f\"Best F1 (thr={best_thr_f1_reg:.2f})\", color=\"tab:orange\")\n", + "plt.scatter(recalls_reg[best_idx_f2_reg], precisions_reg[best_idx_f2_reg],\n", + " label=f\"Best F2 (thr={best_thr_f2_reg:.2f})\", color=\"tab:green\")\n", + "plt.xlabel(\"Recall\")\n", + "plt.ylabel(\"Precision\")\n", + "plt.title(\"Courbe PR – RF régularisé (OOF)\")\n", + "plt.grid(alpha=0.4); plt.legend(); plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "e1a3f804-e3bc-4bca-90c8-f0b72a9ab8c2", + "metadata": {}, + "outputs": [], + "source": [ + "# On fige le seuil choisi (F2)\n", + "chosen_thr = float(best_thr_f2_reg) " + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "7a8ab875-9a11-4e22-b105-2666f19a98e3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Pipeline(steps=[('prep',\n",
+       "                 ColumnTransformer(transformers=[('ord',\n",
+       "                                                  OrdinalEncoder(categories=[['Aucun',\n",
+       "                                                                              'Occasionnel',\n",
+       "                                                                              'Frequent']],\n",
+       "                                                                 handle_unknown='use_encoded_value',\n",
+       "                                                                 unknown_value=-1),\n",
+       "                                                  ['frequence_deplacement']),\n",
+       "                                                 ('ohe',\n",
+       "                                                  OneHotEncoder(handle_unknown='ignore',\n",
+       "                                                                sparse_output=False),\n",
+       "                                                  ['genre', 'statut_marital',\n",
+       "                                                   'departement', 'poste',\n",
+       "                                                   'domaine_etude']),\n",
+       "                                                 ('num', 'pass...\n",
+       "                                                   'nombre_participation_pee',\n",
+       "                                                   'nb_formations_suivies',\n",
+       "                                                   'distance_domicile_travail',\n",
+       "                                                   'niveau_education',\n",
+       "                                                   'annees_depuis_la_derniere_promotion',\n",
+       "                                                   'annes_sous_responsable_actuel',\n",
+       "                                                   'satisfaction_globale',\n",
+       "                                                   'exp_moins_3_years'])])),\n",
+       "                ('clf',\n",
+       "                 RandomForestClassifier(class_weight='balanced', max_depth=8,\n",
+       "                                        min_samples_leaf=5,\n",
+       "                                        min_samples_split=10, n_estimators=200,\n",
+       "                                        n_jobs=-1, random_state=42))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Pipeline(steps=[('prep',\n", + " ColumnTransformer(transformers=[('ord',\n", + " OrdinalEncoder(categories=[['Aucun',\n", + " 'Occasionnel',\n", + " 'Frequent']],\n", + " handle_unknown='use_encoded_value',\n", + " unknown_value=-1),\n", + " ['frequence_deplacement']),\n", + " ('ohe',\n", + " OneHotEncoder(handle_unknown='ignore',\n", + " sparse_output=False),\n", + " ['genre', 'statut_marital',\n", + " 'departement', 'poste',\n", + " 'domaine_etude']),\n", + " ('num', 'pass...\n", + " 'nombre_participation_pee',\n", + " 'nb_formations_suivies',\n", + " 'distance_domicile_travail',\n", + " 'niveau_education',\n", + " 'annees_depuis_la_derniere_promotion',\n", + " 'annes_sous_responsable_actuel',\n", + " 'satisfaction_globale',\n", + " 'exp_moins_3_years'])])),\n", + " ('clf',\n", + " RandomForestClassifier(class_weight='balanced', max_depth=8,\n", + " min_samples_leaf=5,\n", + " min_samples_split=10, n_estimators=200,\n", + " n_jobs=-1, random_state=42))])" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Fit final sur tout X_train / y_train\n", + "pipe_rf_reg.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "ff104181-815f-4d6c-80cd-3c6fc02028fd", + "metadata": {}, + "outputs": [], + "source": [ + "# Probabilités sur TRAIN & TEST\n", + "proba_train_reg = pipe_rf_reg.predict_proba(X_train)[:, 1]\n", + "proba_test_reg = pipe_rf_reg.predict_proba(X_test)[:, 1]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "0dd59342-d607-43b2-b106-2213ccaa9dce", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== TRAIN – RF régularisé (F2) (seuil=0.330) ===\n", + "Accuracy : 0.832 | Précision : 0.490 | Rappel : 0.995 | F1 : 0.656\n", + "ROC AUC : 0.982 | PR AUC : 0.911\n", + "\n", + "Classification report :\n", + " precision recall f1-score support\n", + "\n", + " 0 0.999 0.800 0.889 986\n", + " 1 0.490 0.995 0.656 190\n", + "\n", + " accuracy 0.832 1176\n", + " macro avg 0.744 0.897 0.772 1176\n", + "weighted avg 0.916 0.832 0.851 1176\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Évaluation avec seuil optimisé\n", + "scores_train_reg = eval_with_threshold(\n", + " y_train, proba_train_reg,\n", + " threshold=chosen_thr,\n", + " set_name=\"TRAIN – RF régularisé (F2)\",\n", + " plot_cm=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "ff3d5325-ea84-44e2-b871-1e082152eda4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== TEST – RF régularisé (F2) (seuil=0.330) ===\n", + "Accuracy : 0.718 | Précision : 0.327 | Rappel : 0.723 | F1 : 0.450\n", + "ROC AUC : 0.765 | PR AUC : 0.395\n", + "\n", + "Classification report :\n", + " precision recall f1-score support\n", + "\n", + " 0 0.932 0.717 0.810 247\n", + " 1 0.327 0.723 0.450 47\n", + "\n", + " accuracy 0.718 294\n", + " macro avg 0.629 0.720 0.630 294\n", + "weighted avg 0.835 0.718 0.753 294\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scores_test_reg = eval_with_threshold(\n", + " y_test, proba_test_reg,\n", + " threshold=chosen_thr,\n", + " set_name=\"TEST – RF régularisé (F2)\",\n", + " plot_cm=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "5951a76f-0bb9-4fa1-adbe-e65d767f2138", + "metadata": {}, + "source": [ + "**Conclusion**\n", + "\n", + "Un léger feature engineering a été réalisé (construction d’un score de satisfaction globale et d’un indicateur d’expérience < 3 ans).\n", + "\n", + "Problème initial : RF de base très performant sur TRAIN (≈ 1.0 partout) mais écart important avec TEST → signe d’overfit.\n", + "\n", + "Action : régularisation du RF\n", + "\n", + "max_depth=8, min_samples_leaf=5, min_samples_split=10, class_weight=\"balanced\".\n", + "\n", + "Validation croisée : scores moyens stables (ROC AUC, PR AUC, accuracy) → le modèle généralise correctement.\n", + "\n", + "Courbe PR + seuil optimisé (F2) : choix d’un seuil donnant un bon rappel au détriment de la précision (aligné avec l’objectif métier : ne pas rater des démissions).\n", + "\n", + "Résultat final (TEST) :\n", + "\n", + "Accuracy ≈ 0.80,\n", + "\n", + "Rappel classe 1 ≈ 0.64,\n", + "\n", + "PR AUC ≈ 0.43,\n", + "\n", + "meilleure balance rappel/précision qu’avec le dummy et la régression logistique, et moins d’overfit qu’avec le RF initial." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "ad409563-9097-410c-a1b8-c0feb01571a7", + "metadata": {}, + "outputs": [], + "source": [ + "# === Sauvegarde du pipeline + métadonnées ===\n", + "from pathlib import Path\n", + "import json, joblib\n", + "import sklearn\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "3920497f-02a7-494e-a805-318ff3dc844c", + "metadata": {}, + "outputs": [], + "source": [ + "# répertoire artefacts depuis notebooks/\n", + "ARTIF_DIR = Path(\"../models\")\n", + "ARTIF_DIR.mkdir(parents=True, exist_ok=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "e1d4dfcd-debb-432f-9fd8-7f3cbaeb3190", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['../models/model.joblib']" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# modèle\n", + "joblib.dump(pipe_rf_reg, ARTIF_DIR / \"model.joblib\")" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "e1112259-2298-4ac2-ade3-c101b93aa017", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Sauvegardé: ../models/model.joblib\n" + ] + } + ], + "source": [ + "print(\" Sauvegardé: ../models/model.joblib\")" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "7fc2e200-4eda-43e7-bf7e-fb13d1e38128", + "metadata": {}, + "outputs": [], + "source": [ + "# métriques \n", + "y_pred_test = (proba_test_reg >= chosen_thr).astype(int)\n", + "f1_t = f1_score(y_test, y_pred_test)\n", + "roc_t = roc_auc_score(y_test, proba_test_reg)\n", + "pr_t = average_precision_score(y_test, proba_test_reg)\n" + ] + }, + { + "cell_type": "markdown", + "id": "77608b64-789f-4361-b59c-82fd54e53043", + "metadata": {}, + "source": [ + "## SMOTE" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "96ebf184-fe73-4204-9d6b-078877817fac", + "metadata": {}, + "outputs": [], + "source": [ + "# Random Forest pour SMOTE (sans class_weight)\n", + "rf_sm = RandomForestClassifier(\n", + " n_estimators=200,\n", + " class_weight=\"balanced\",\n", + " max_depth=8, \n", + " min_samples_leaf=5, \n", + " min_samples_split=10, \n", + " max_features=\"sqrt\",\n", + " n_jobs=-1,\n", + " random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b2d1387f-d018-4f5a-b7c3-5057cdf360e9", + "metadata": {}, + "outputs": [], + "source": [ + "# Pipeline : préprocessing -> SMOTE -> RF\n", + "pipe_rf_sm = ImbPipeline(steps=[\n", + " (\"prep\", preproc),\n", + " (\"smote\", SMOTE(k_neighbors=5, random_state=42)),\n", + " (\"clf\", rf_sm),\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "915df103-0547-4e45-bf13-5a317668e202", + "metadata": {}, + "outputs": [], + "source": [ + "cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab446272-460a-49c4-aa37-c4747bd48e44", + "metadata": {}, + "outputs": [], + "source": [ + "scores_ap = cross_val_score(\n", + " pipe_rf_sm,\n", + " X_train,\n", + " y_train,\n", + " cv=cv,\n", + " scoring=\"average_precision\",\n", + " n_jobs=-1\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cab84661-2fd6-463d-8b9b-dc061d1cf5b3", + "metadata": {}, + "outputs": [], + "source": [ + "print(\n", + " \"RF + SMOTE | PR AUC mean:\",\n", + " scores_ap.mean().round(3),\n", + " \"±\",\n", + " scores_ap.std().round(3)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8ada688d-f0be-47ef-990c-db9e6f6522f1", + "metadata": {}, + "source": [ + "OOF + courbe PR pour le pipeline SMOTE" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1f5795c-7c92-408e-8f0c-56a3c060cb2e", + "metadata": {}, + "outputs": [], + "source": [ + "# Probabilités OOF (Out-Of-Fold) avec SMOTE + RF\n", + "oof_proba_sm = cross_val_predict(\n", + " pipe_rf_sm,\n", + " X_train,\n", + " y_train,\n", + " cv=cv,\n", + " method=\"predict_proba\",\n", + " n_jobs=-1\n", + ")[:, 1] " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2746ae90-0230-4a09-b6dc-777642a2b410", + "metadata": {}, + "outputs": [], + "source": [ + "oof_true = y_train.values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "491e4fae-4722-4b2f-8af2-5efd3b52a3b8", + "metadata": {}, + "outputs": [], + "source": [ + "# Courbe précision–rappel\n", + "precisions_sm, recalls_sm, thresholds_sm = precision_recall_curve(oof_true, oof_proba_sm)\n", + "pr_auc_oof_sm = average_precision_score(oof_true, oof_proba_sm)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "962ff16c-4223-4f57-b27a-31ccb7fa60fa", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Average Precision (PR AUC) OOF RF+SMOTE =\", round(pr_auc_oof_sm, 3))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a88fcfd9-56b7-4e5d-ab52-6d9094e83389", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot de la courbe PR\n", + "plt.figure(figsize=(6, 5))\n", + "plt.plot(recalls_sm, precisions_sm, label=f\"RF+SMOTE (AP={pr_auc_oof_sm:.3f})\")\n", + "plt.xlabel(\"Recall\")\n", + "plt.ylabel(\"Precision\")\n", + "plt.title(\"Courbe précision–rappel – RF + SMOTE (OOF)\")\n", + "plt.grid(alpha=0.4)\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0e344998-af3e-41f0-9fcc-526b2ebc9f38", + "metadata": {}, + "source": [ + "Recherche le meilleur seuil (F1 et F2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e1c8d8a-513a-4cad-9029-fa572af83d5e", + "metadata": {}, + "outputs": [], + "source": [ + "# Calcul F1 et F2 pour chaque point de la courbe PR\n", + "beta2 = 2.0\n", + "\n", + "f1_scores_sm = 2 * (precisions_sm * recalls_sm) / (precisions_sm + recalls_sm + 1e-8)\n", + "f2_scores_sm = (1 + beta2**2) * (precisions_sm * recalls_sm) / (beta2**2 * precisions_sm + recalls_sm + 1e-8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1e0a47e-7094-41bd-abbf-6d3b5627580d", + "metadata": {}, + "outputs": [], + "source": [ + "# precision_recall_curve retourne len(thresholds) = len(precisions) - 1\n", + "best_idx_f1_sm = np.nanargmax(f1_scores_sm[:-1])\n", + "best_thr_f1_sm = thresholds_sm[best_idx_f1_sm]\n", + "\n", + "best_idx_f2_sm = np.nanargmax(f2_scores_sm[:-1])\n", + "best_thr_f2_sm = thresholds_sm[best_idx_f2_sm]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "689304d0-e375-4946-ac76-01490798fc56", + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"Seuil F1 optimal RF+SMOTE : {best_thr_f1_sm:.3f} | \"\n", + " f\"P={precisions_sm[best_idx_f1_sm]:.3f} R={recalls_sm[best_idx_f1_sm]:.3f} F1={f1_scores_sm[best_idx_f1_sm]:.3f}\")\n", + "print(f\"Seuil F2 optimal RF+SMOTE : {best_thr_f2_sm:.3f} | \"\n", + " f\"P={precisions_sm[best_idx_f2_sm]:.3f} R={recalls_sm[best_idx_f2_sm]:.3f} F2={f2_scores_sm[best_idx_f2_sm]:.3f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "250a080b-8cad-4383-b014-b42fa4b55df8", + "metadata": {}, + "outputs": [], + "source": [ + "# Visualiser les points F1/F2 sur la courbe\n", + "plt.figure(figsize=(6, 5))\n", + "plt.plot(recalls_sm, precisions_sm, label=f\"RF+SMOTE (AP={pr_auc_oof_sm:.3f})\")\n", + "plt.scatter(recalls_sm[best_idx_f1_sm], precisions_sm[best_idx_f1_sm],\n", + " label=f\"Best F1 (thr={best_thr_f1_sm:.2f})\", color=\"tab:orange\")\n", + "plt.scatter(recalls_sm[best_idx_f2_sm], precisions_sm[best_idx_f2_sm],\n", + " label=f\"Best F2 (thr={best_thr_f2_sm:.2f})\", color=\"tab:green\")\n", + "plt.xlabel(\"Recall\")\n", + "plt.ylabel(\"Precision\")\n", + "plt.title(\"Courbe PR – RF+SMOTE avec seuils F1/F2\")\n", + "plt.grid(alpha=0.4)\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "66e538b1-2c4c-449a-b15a-7df1c3dd1384", + "metadata": {}, + "outputs": [], + "source": [ + "chosen_thr_sm = best_thr_f2_sm " + ] + }, + { + "cell_type": "markdown", + "id": "b64803b7-ab48-417d-addf-389cb17c7269", + "metadata": {}, + "source": [ + "Fit final sur tout le TRAIN avec RF+SMOTE" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "43a11ffb-924a-4b5e-ad51-a3f0957db07f", + "metadata": {}, + "outputs": [], + "source": [ + "# Fit final du pipeline RF+SMOTE sur tout X_train\n", + "pipe_rf_sm.fit(X_train, y_train)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49f9ddf8-3e5d-40a2-84ee-67b99a9b2670", + "metadata": {}, + "outputs": [], + "source": [ + "# Probabilités sur TRAIN & TEST\n", + "proba_train_sm = pipe_rf_sm.predict_proba(X_train)[:, 1]\n", + "proba_test_sm = pipe_rf_sm.predict_proba(X_test)[:, 1]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b457cc52-224c-4e28-a8bf-f434191126d3", + "metadata": {}, + "outputs": [], + "source": [ + "# Évaluation TRAIN / TEST avec le même seuil\n", + "scores_train_sm = eval_with_threshold(\n", + " y_train, proba_train_sm,\n", + " threshold=chosen_thr_sm,\n", + " set_name=\"TRAIN – RF SMOTE (F2)\",\n", + " plot_cm=True\n", + ")\n", + "\n", + "scores_test_sm = eval_with_threshold(\n", + " y_test, proba_test_sm,\n", + " threshold=chosen_thr_sm,\n", + " set_name=\"TEST – RF SMOTE (F2)\",\n", + " plot_cm=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9beec6db-2def-4a5a-921d-734b6e346005", + "metadata": {}, + "source": [ + "**Conclusion** \n", + "“Avec SMOTE et un seuil calibré via la F2, nous captons environ 78 % des démissions, au prix d’un plus grand nombre d’alertes fausses, ce qui peut être acceptable si le coût d’une action préventive est faible.”\n", + "____________________\n", + "\n", + "Nous avons testé une version RF + SMOTE.\n", + "Les performances sont comparables / légèrement meilleures en rappel, mais pour l’interprétation et la mise en production, nous retenons le RF tuné sans SMOTE + seuil F2, plus simple et plus stable." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c8bc518d-608d-4e1e-84fb-f24174378dde", + "metadata": {}, + "outputs": [], + "source": [ + "# Pour comparer : seuil standard 0.5\n", + "\n", + "scores_train_sm_05 = eval_with_threshold(\n", + " y_train, proba_train_sm,\n", + " threshold=0.5,\n", + " set_name=\"TRAIN – RF+SMOTE seuil 0.5\",\n", + " plot_cm=True\n", + ")\n", + "\n", + "scores_test_sm_05 = eval_with_threshold(\n", + " y_test, proba_test_sm,\n", + " threshold=0.5,\n", + " set_name=\"TEST – RF+SMOTE seuil 0.5\",\n", + " plot_cm=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "4859b00d-596a-4cba-890f-797c368eb716", + "metadata": {}, + "source": [ + "**Conclusion** \n", + "Nous privilégions le seuil optimisé (≈ 0.26, basé sur F2),\n", + "car il donne un rappel beaucoup plus élevé (66 % vs 32 %) sur les “partants”, au prix d’une baisse modérée de l’accuracy et de la précision.\n", + "C’est cohérent avec le métier : mieux vaut déclencher plus d’alertes que rater des démissions." + ] + }, + { + "cell_type": "markdown", + "id": "3512a24f-6064-44c8-a70a-a4903b108236", + "metadata": {}, + "source": [ + "# Optimisation du modèle" + ] + }, + { + "cell_type": "markdown", + "id": "fb5961c8-2555-42c8-a385-9c45f6894586", + "metadata": {}, + "source": [ + "### Fine-tuning Random Forest avec GridSearchCV\n", + "\n", + "Ici on fait GridSearch uniquement sur X_train / y_train, avec StratifiedKFold et un scoring adapté au déséquilibre (average_precision = AP = PR AUC)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d469793-6c91-4c88-8e72-7e00c9fc11c2", + "metadata": {}, + "outputs": [], + "source": [ + "# Modèle de base RF (non encore tuné)\n", + "rf_base = RandomForestClassifier(\n", + " class_weight=\"balanced\",\n", + " random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1312171f-60ad-4890-930e-5e2d0d8cdb3b", + "metadata": {}, + "outputs": [], + "source": [ + "# Pipeline : préprocessing + modèle\n", + "pipe_rf = Pipeline(steps=[\n", + " (\"prep\", preproc),\n", + " (\"clf\", rf_base)\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ffd11b45-3998-4b45-a48f-59adfc766dec", + "metadata": {}, + "outputs": [], + "source": [ + "# Grille de recherche \n", + "param_grid = {\n", + " \"clf__n_estimators\": [50, 75, 100, 125, 150, 175],\n", + " \"clf__max_depth\": [2, 5, 12, 15, 20],\n", + " \"clf__min_samples_split\":[2,4,6,8,10],\n", + " \"clf__min_samples_leaf\":[1, 2, 3, 5],\n", + " \"clf__max_features\": [\"sqrt\", 0.5, 0.7],\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "77bc9eb8-d218-4d2b-be60-0355102d0ef2", + "metadata": {}, + "outputs": [], + "source": [ + "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c327073e-570b-4473-abef-c232ca22618c", + "metadata": {}, + "outputs": [], + "source": [ + "# GridSearchCV, on optimise la PR AUC\n", + "rf_search = GridSearchCV(\n", + " estimator=pipe_rf,\n", + " param_grid=param_grid,\n", + " scoring=\"average_precision\", \n", + " cv=cv,\n", + " n_jobs=-1,\n", + " verbose=1,\n", + " refit=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38f5e8b9-2c53-48d5-87a5-ac3c849c4f0e", + "metadata": {}, + "outputs": [], + "source": [ + "# Fit sur TRAIN uniquement\n", + "rf_search.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce3a127a-a371-4a74-af14-0e8d3256d223", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Meilleurs hyperparamètres RF :\")\n", + "print(rf_search.best_params_)\n", + "print(\"Best PR AUC (moyenne CV) :\", rf_search.best_score_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "199011a8-6980-4177-86fb-e08891d44028", + "metadata": {}, + "outputs": [], + "source": [ + "rf_tuned_pipe = rf_search.best_estimator_\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b6e2d78-7000-43e2-b875-d2dab73fcf2d", + "metadata": {}, + "outputs": [], + "source": [ + "# Probabilités OOF (Out-Of-Fold)\n", + "oof_proba = cross_val_predict(\n", + " rf_tuned_pipe,\n", + " X_train,\n", + " y_train,\n", + " cv=cv,\n", + " method=\"predict_proba\",\n", + " n_jobs=-1\n", + ")[:, 1] " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "01e8ca8b-a0e7-41d1-bd4b-d73f768b53ec", + "metadata": {}, + "outputs": [], + "source": [ + "oof_true = y_train.values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d19e3db1-5b0a-4211-af72-7afac7c8831c", + "metadata": {}, + "outputs": [], + "source": [ + "# Courbe précision–rappel\n", + "precisions, recalls, thresholds = precision_recall_curve(oof_true, oof_proba)\n", + "pr_auc_oof = average_precision_score(oof_true, oof_proba)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2160ca85-cf15-43e6-ba51-c7c00a07697c", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Average Precision (PR AUC) OOF RF tuné' =\", round(pr_auc_oof, 3))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5b8c1d3-c941-4a64-b873-d4b8baee396c", + "metadata": {}, + "outputs": [], + "source": [ + "# Calcul F1 et F2 pour chaque point de la courbe PR\n", + "beta2 = 2.0\n", + "\n", + "f1_scores = 2 * (precisions * recalls) / (precisions + recalls + 1e-8)\n", + "f2_scores = (1 + beta2**2) * (precisions * recalls) / (beta2**2 * precisions + recalls + 1e-8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7ab2f57a-1c1b-468e-bda3-651add95fb8f", + "metadata": {}, + "outputs": [], + "source": [ + "# precision_recall_curve retourne len(thresholds) = len(precisions) - 1\n", + "best_idx_f1 = np.nanargmax(f1_scores[:-1])\n", + "best_thr_f1 = thresholds[best_idx_f1]\n", + "\n", + "best_idx_f2 = np.nanargmax(f2_scores[:-1])\n", + "best_thr_f2 = thresholds[best_idx_f2]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2811fce2-d96e-45da-b4d0-bb0f9c84444a", + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"Seuil F1 optimal RF tuné : {best_thr_f1:.3f} | \"\n", + " f\"P={precisions[best_idx_f1]:.3f} R={recalls[best_idx_f1]:.3f} F1={f1_scores[best_idx_f1]:.3f}\")\n", + "print(f\"Seuil F2 optimal RF tuné : {best_thr_f2:.3f} | \"\n", + " f\"P={precisions[best_idx_f2]:.3f} R={recalls[best_idx_f2]:.3f} F2={f2_scores[best_idx_f2]:.3f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec87e70c-040e-4cc5-98cf-d958fab39c8c", + "metadata": {}, + "outputs": [], + "source": [ + "# Visualiser les points F1/F2 sur la courbe\n", + "plt.figure(figsize=(6, 5))\n", + "plt.plot(recalls, precisions, label=f\"RF tuné (AP={pr_auc_oof:.3f})\")\n", + "plt.scatter(recalls[best_idx_f1], precisions[best_idx_f1],\n", + " label=f\"Best F1 (thr={best_thr_f1:.2f})\", color=\"tab:orange\")\n", + "plt.scatter(recalls[best_idx_f2], precisions[best_idx_f2],\n", + " label=f\"Best F2 (thr={best_thr_f2:.2f})\", color=\"tab:green\")\n", + "plt.xlabel(\"Recall\")\n", + "plt.ylabel(\"Precision\")\n", + "plt.title(\"Courbe PR – RF tuné avec seuils F1/F2\")\n", + "plt.grid(alpha=0.4)\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "757760b3-34e1-4bc3-9907-3df25dd5d37d", + "metadata": {}, + "outputs": [], + "source": [ + "# On fige le seuil choisi (F2)\n", + "chosen_thr = float(best_thr_f2) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "079eac54-16f5-4659-8756-9d95f3d6c967", + "metadata": {}, + "outputs": [], + "source": [ + "# Fit final du pipeline RF tuné sur tout X_train\n", + "rf_tuned_pipe.fit(X_train, y_train)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "422dcb88-66e7-4772-abd8-9002eac79069", + "metadata": {}, + "outputs": [], + "source": [ + "# Probabilités sur TRAIN et TEST\n", + "proba_train_tuned = rf_tuned_pipe.predict_proba(X_train)[:, 1]\n", + "proba_test_tuned = rf_tuned_pipe.predict_proba(X_test)[:, 1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "812b758e-1d50-45e2-93de-5063c31acb4f", + "metadata": {}, + "outputs": [], + "source": [ + "# Évaluation finale avec le seuil optimisé\n", + "scores_train_rf_tuned_F2 = eval_with_threshold(\n", + " y_train,\n", + " proba_train_tuned,\n", + " threshold=chosen_thr,\n", + " set_name=\"TRAIN – RF tuné (seuil F2)\",\n", + " plot_cm=True\n", + ")\n", + "\n", + "scores_test_rf_tuned_F2 = eval_with_threshold(\n", + " y_test,\n", + " proba_test_tuned,\n", + " threshold=chosen_thr,\n", + " set_name=\"TEST – RF tuné (seuil F2)\",\n", + " plot_cm=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "e417cd44-858a-438a-9cd4-b15679183564", + "metadata": {}, + "source": [ + "### Importance globale des features" + ] + }, + { + "cell_type": "markdown", + "id": "c669cf88-b8eb-4cf1-a51a-90ef0e4205b9", + "metadata": {}, + "source": [ + "### 1. Préparer les noms de variables alignés avec le RF" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5b3adf26-94d3-4d0c-af96-b9ea3cc57fb5", + "metadata": {}, + "outputs": [], + "source": [ + "# Séparation préprocesseur et RF dans le pipeline régularisé\n", + "prep_reg = pipe_rf_reg.named_steps[\"prep\"]\n", + "rf_reg = pipe_rf_reg.named_steps[\"clf\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f1c4d02-dc88-4e22-87c6-a4f2ddbdd917", + "metadata": {}, + "outputs": [], + "source": [ + "# Transformer X_train\n", + "X_train_reg_preproc = prep_reg.transform(X_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "631f63e7-4cae-4367-a265-e5f224b762b3", + "metadata": {}, + "outputs": [], + "source": [ + "# Liste de toutes les colonnes dans l'ordre d'entrée du modèle\n", + "num_features = list(num_cols)\n", + "other_features = [c for c in X_train.columns if c not in num_cols]\n", + "feature_names = num_features + other_features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0d419df-659d-4741-bd7d-b1ea4f6fd8d6", + "metadata": {}, + "outputs": [], + "source": [ + "X_train_reg_df = pd.DataFrame(\n", + " X_train_reg_preproc,\n", + " columns=feature_names,\n", + " index=X_train.index\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5753504b-f942-49d7-a3af-22a3018a653f", + "metadata": {}, + "outputs": [], + "source": [ + "print(X_train_reg_df.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "fbb6b2db-d584-4263-b2a6-3df7ac72ba80", + "metadata": {}, + "source": [ + "### 2. Importance native du Random Forest" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f6b0352-bf3b-4d46-b347-2bdca43f7722", + "metadata": {}, + "outputs": [], + "source": [ + "rf_reg.fit(X_train_reg_df, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "080c9794-37a8-4595-a74f-f46eec5d4448", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"n_features_in_ :\", rf_reg.n_features_in_) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4df6d4e1-0f7e-450a-a3c1-0e91331ac424", + "metadata": {}, + "outputs": [], + "source": [ + "rf_importances = rf_reg.feature_importances_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e9903fa5-2e1b-4d70-a603-19acc3194ec3", + "metadata": {}, + "outputs": [], + "source": [ + "df_rf_importance = (\n", + " pd.DataFrame({\n", + " \"feature\": feature_names,\n", + " \"importance\": rf_importances\n", + " })\n", + " .sort_values(\"importance\", ascending=False)\n", + ")\n", + "\n", + "plt.figure(figsize=(8,6))\n", + "top = df_rf_importance.head(15)\n", + "plt.barh(top[\"feature\"][::-1], top[\"importance\"][::-1])\n", + "plt.xlabel(\"Importance (RF native)\")\n", + "plt.title(\"Importance globale – RF régularisé\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "724ab2ca-fc3a-40e1-a150-c4f5a28c39b7", + "metadata": {}, + "source": [ + "_____________\n", + "\n", + "On voit que dans ce modèle RF, les variables les plus déterminantes sont par exemple revenu_mensuel, age, heure_supplementaires, satisfaction_globale, etc.\n", + "\n", + "C’est une importance globale, sans direction : on ne sait pas encore si un haut revenu augmente ou diminue le risque, juste que le modèle l’utilise beaucoup.\n", + "\n", + "_________\n" + ] + }, + { + "cell_type": "markdown", + "id": "791be325-93b2-4128-b012-363d8eefe36d", + "metadata": {}, + "source": [ + "### 3. Permutation Importance (sur TEST)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c7db3711-f882-4591-a63a-6f69f5c2dc2f", + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "warnings.filterwarnings(\n", + " \"ignore\",\n", + " message=\"X does not have valid feature names, but RandomForestClassifier was fitted with feature names\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ddb1fda8-e9b7-44b4-8d4e-a9599516f2ad", + "metadata": {}, + "outputs": [], + "source": [ + "result = permutation_importance(\n", + " pipe_rf_reg,\n", + " X_test,\n", + " y_test,\n", + " n_repeats=20,\n", + " scoring=\"average_precision\", \n", + " n_jobs=-1,\n", + " random_state=42\n", + ")\n", + "\n", + "df_perm = (\n", + " pd.DataFrame({\n", + " \"feature\": feature_names,\n", + " \"importance_mean\": result.importances_mean,\n", + " \"importance_std\": result.importances_std\n", + " })\n", + " .sort_values(\"importance_mean\", ascending=False)\n", + ")\n", + "\n", + "plt.figure(figsize=(8,6))\n", + "top = df_perm.head(15)\n", + "plt.barh(top[\"feature\"][::-1], top[\"importance_mean\"][::-1], xerr=top[\"importance_std\"][::-1])\n", + "plt.xlabel(\"Permutation importance (PR AUC)\")\n", + "plt.title(\"Importance globale – permutation (TEST)\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "a7b6383a-216f-4a03-852d-e60f276ffc3b", + "metadata": {}, + "source": [ + "______\n", + "\n", + "`nombre_participation_pee` est très important : le modèle perd beaucoup de performance quand on le perturbe.\n", + "\n", + "D’autres variables ont une importance plus faible ou proche de 0 → elles n’apportent presque rien au modèle.\n", + "\n", + "______" + ] + }, + { + "cell_type": "markdown", + "id": "8862f642-6a74-4d16-9713-146e9499cc4c", + "metadata": {}, + "source": [ + "### SHAP – importance globale (beeswarm + bar)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "89e13f37-6a9d-406e-9f8c-54b51bbbac26", + "metadata": {}, + "outputs": [], + "source": [ + "# Explainer SHAP (TreeExplainer sur RF)\n", + "explainer = shap.TreeExplainer(rf_reg)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bcda4bc3-03bc-431a-afa0-c3c673786e61", + "metadata": {}, + "outputs": [], + "source": [ + "# SHAP values sur TOUT X_train_reg_df\n", + "shap_values = explainer.shap_values(X_train_reg_df) # liste [classe 0, classe 1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c6fafed6-702a-42f9-8986-79b13f2c9487", + "metadata": {}, + "outputs": [], + "source": [ + "# Vérif des shapes\n", + "print(\"len(shap_values) :\", len(shap_values))\n", + "print(\"shap_values[0].shape :\", shap_values[0].shape)\n", + "print(\"shap_values[1].shape :\", shap_values[1].shape)\n", + "print(\"X_train_reg_df.shape :\", X_train_reg_df.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2836adba-acc5-4a1b-bbe8-f2fd3eb0209b", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"type(shap_values):\", type(shap_values))\n", + "print(\"shap_values.shape:\", np.array(shap_values).shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "97527c87-b907-4e28-9934-3fed627ed69a", + "metadata": {}, + "outputs": [], + "source": [ + "shap_class1 = shap_values[:, :, 1] # (1102, 40)\n", + "\n", + "print(shap_class1.shape) \n", + "print(X_train_reg_df.shape) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3191aa41-59b2-4f98-beaa-aa6320153d7d", + "metadata": {}, + "outputs": [], + "source": [ + "# Beeswarm : vue globale, signe + couleur = sens de l'effet\n", + "shap.summary_plot(\n", + " shap_class1,\n", + " X_train_reg_df,\n", + " feature_names=feature_names\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "1d739cd9-6c70-4c77-92d7-249f251f4124", + "metadata": {}, + "source": [ + "_____\n", + "\n", + "`heure_supplementaires` :\n", + "\n", + "beaucoup de points rouges à droite → des heures sup élevées augmentent le risque.\n", + "\n", + "les bleus sont plutôt à gauche → peu d’heures sup le diminuent.\n", + "\n", + "`satisfaction_globale` :\n", + "\n", + "rouges (satisfaction élevée) à gauche → haute satisfaction protège.\n", + "\n", + "bleus (faible satisfaction) à droite → faible satisfaction augmente le risque\n", + "\n", + "___" + ] + }, + { + "cell_type": "markdown", + "id": "f978a06b-2d38-4f6d-af84-6e524aaabdfa", + "metadata": {}, + "source": [ + "### SHAP local (waterfall) sur les données complètes" + ] + }, + { + "cell_type": "markdown", + "id": "fcd50cd6-6bca-4b87-8f13-2438dd5a0f40", + "metadata": {}, + "source": [ + "SHAP – importance locale (waterfall plot)\n", + "\n", + "On regarde par exemple :\n", + "\n", + "1 employé parti (y=1),\n", + "\n", + "1 employé resté (y=0)." + ] + }, + { + "cell_type": "markdown", + "id": "0109b5ae-8103-414e-bdb9-61bc6161b4f8", + "metadata": {}, + "source": [ + "Interprétation (en langage métier) :\n", + "\n", + "La base value = probabilité moyenne de départ.\n", + "\n", + "Les barres rouges = variables qui augmentent le risque de départ pour cet individu.\n", + "\n", + "Les barres bleues = variables qui réduisent le risque (facteurs protecteurs)." + ] + }, + { + "cell_type": "markdown", + "id": "8d7ed609-318c-46e9-873b-cbf65bc8fc53", + "metadata": {}, + "source": [ + "Exemple pour un individu de la classe 1 (client attrition) :" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3dcbb2b-8ea6-4c50-aa53-faf897861a4e", + "metadata": {}, + "outputs": [], + "source": [ + "# index d'un client en attrition\n", + "idx_1 = y_train[y_train == 1].index[0]\n", + "\n", + "# récupérer sa ligne et ses shap values\n", + "x_i = X_train_reg_df.loc[idx_1, :]\n", + "shap_i = shap_class1[X_train_reg_df.index.get_loc(idx_1), :]\n", + "\n", + "shap.plots._waterfall.waterfall_legacy(\n", + " explainer.expected_value[1], # base value pour la classe 1\n", + " shap_i,\n", + " feature_names=feature_names\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "e25f642e-42df-41fd-b930-77c6e047afa9", + "metadata": {}, + "source": [ + "______\n", + "\n", + "`heure_supplementaires, exp_moins_3_years, age, revenu_mensuel` poussent tous la proba vers la droite → ce sont les raisons principales pour lesquelles cet individu a un risque élevé.\n", + "\n", + "`poste_Assistant de Direction, 31 other features` en bleu tirent un peu la proba vers le bas (facteurs protecteurs), mais pas assez pour compenser.\n", + "\n", + "____\n" + ] + }, + { + "cell_type": "markdown", + "id": "d405d129-bd4d-44d0-8b92-0dd13aa5fa6c", + "metadata": {}, + "source": [ + "Example pour un individu de la classe 0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1a5e330-2a99-40eb-a468-e8a0cd6eff47", + "metadata": {}, + "outputs": [], + "source": [ + "idx_0 = y_train[y_train == 0].index[0]\n", + "\n", + "x_i0 = X_train_reg_df.loc[idx_0, :]\n", + "shap_i0 = shap_class1[X_train_reg_df.index.get_loc(idx_0), :]\n", + "\n", + "shap.plots._waterfall.waterfall_legacy(\n", + " explainer.expected_value[1],\n", + " shap_i0,\n", + " feature_names=feature_names\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "c2bd924e-668a-43a4-a33d-d1d08afcf69f", + "metadata": {}, + "source": [ + "_____\n", + "\n", + "`heure_supplementaires, revenu_mensuel, annees_sous_responsable_actuel` réduisent fortement la probabilité, d’où un risque faible.\n", + "\n", + "`satisfaction_globale, nombre_partiicpation_pee, statut_marital_célebataire` remontent un peu la proba, mais pas assez pour dépasser 0.5\n", + "\n", + "__" + ] + }, + { + "cell_type": "markdown", + "id": "ac33d577-3b45-4d50-ae5f-c87ae8f35fab", + "metadata": {}, + "source": [ + "# Conclusion et plan d’action\n", + "\n", + "À mesure que l’entreprise accumulera de nouvelles données sur ses collaborateurs (nouveaux arrivants, départs récents, évolutions de carrière), l’algorithme pourra être régulièrement réentraîné. L’enrichissement progressif de la base de données permettra, en théorie, d’améliorer la précision des prédictions et d’identifier plus finement les employés à haut risque de départ, sur la base de la probabilité estimée pour chaque individu.\n", + "\n", + "Sur cette probabilité, chaque employé peut se voir attribuer **une note de risque de départ** (**faible, moyen, élevé**). Cette segmentation permet de prioriser les actions de fidélisation :\n", + "\n", + "**Risque faible** : suivi classique et actions RH générales (communication, engagement, qualité de vie au travail).\n", + "\n", + "**Risque moyen** : mise en place d’actions préventives ciblées (ajustement de la charge de travail, perspectives de développement, reconnaissance).\n", + "\n", + "**Risque élevé** : actions rapides et personnalisées, incluant des entretiens individuels avec un représentant RH et, si nécessaire, une rencontre avec le supérieur hiérarchique afin de discuter des conditions de travail et d’identifier des mesures correctives.\n", + "\n", + "Les analyses montrent que certains facteurs jouent un rôle particulièrement important dans la décision de quitter l’entreprise :\n", + "\n", + "**Revenu mensuel** : les salariés mieux rémunérés sont moins susceptibles de partir. Il est donc essentiel de vérifier régulièrement la compétitivité des salaires par rapport au marché local.\n", + "\n", + "**Heures supplémentaires** : un volume élevé d’heures supplémentaires augmente le risque de départ. Une meilleure planification des projets, un dimensionnement adéquat des équipes et un pilotage de la charge de travail sont nécessaires pour limiter ce phénomène.\n", + "\n", + "**Âge** : les employés jeunes (environ 25–35 ans) présentent un risque de départ plus élevé. Il convient de clarifier la vision à long terme de l’entreprise, de montrer les perspectives d’évolution et de proposer des parcours de carrière attractifs.\n", + "\n", + "**Distance domicile–travail** : une distance importante accroît le risque de départ. Des solutions telles que le transport collectif, une indemnité de déplacement ou, lorsque c’est possible, des modalités de travail flexible peuvent être envisagées, sans pour autant discriminer sur le lieu de résidence.\n", + "\n", + "**Années totales d’expérience** : les collaborateurs plus expérimentés sont globalement plus stables. Ceux ayant entre 5 et 8 ans d’expérience doivent toutefois être suivis avec attention, car ils peuvent être plus sollicités par le marché.\n", + "\n", + " **Ancienneté dans l’entreprise** : la fidélité à l’entreprise réduit le risque de départ, mais un point de vigilance apparaît autour des deux premières années, période critique où un accompagnement renforcé est recommandé.\n", + "\n", + "**Années avec le responsable actuel** : un pic de démissions survient environ six mois après un changement de manager. Le suivi des équipes récemment réorganisées, ainsi que l’identification des responsables ayant connu de nombreux départs sur la dernière année, permet d’agir sur la qualité du management et de l’environnement de travail.\n", + "\n", + "En combinant ces indicateurs avec la note de risque fournie par le modèle, l’entreprise peut structurer un plan stratégique de fidélisation plus ciblé, proactif et mesurable. L’objectif n’est pas seulement de prédire les départs, mais surtout de créer les conditions d’un engagement durable : politiques de rémunération compétitives, gestion raisonnée de la charge de travail, perspectives de carrière claires, soutien adapté aux contraintes personnelles (transport, équilibre vie pro/vie perso) et amélioration continue des pratiques managériales." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d637c1f6-53ba-477d-aa4e-feb051db0b33", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "97c7cfaa-23f7-442f-a0a6-aa14cd17475c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Data science (Classification automatique d'informations)", + "language": "python", + "name": "classification_informations" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}