diff --git a/M2/Machine Learning/TP_5/2025_M2_ISF_TP_5.ipynb b/M2/Machine Learning/TP_5/2025_M2_ISF_TP_5.ipynb index 9075d5a..74b6c06 100644 --- a/M2/Machine Learning/TP_5/2025_M2_ISF_TP_5.ipynb +++ b/M2/Machine Learning/TP_5/2025_M2_ISF_TP_5.ipynb @@ -104,18 +104,16 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 1, "id": "e3d8e095", "metadata": {}, "outputs": [], "source": [ "import warnings\n", - "from io import StringIO\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import requests\n", "import seaborn as sns\n", "from catboost import CatBoostClassifier, Pool\n", "from sklearn.metrics import (\n", @@ -124,13 +122,13 @@ " roc_auc_score,\n", " roc_curve,\n", ")\n", - "from sklearn.model_selection import GridSearchCV, KFold, train_test_split\n", + "from sklearn.model_selection import train_test_split\n", "\n", - "warnings.filterwarnings('ignore')\n", + "warnings.filterwarnings(\"ignore\")\n", "\n", "# Configuration des graphiques\n", - "plt.style.use('seaborn-v0_8-darkgrid')\n", - "sns.set_palette(\"husl\")" + "plt.style.use(\"seaborn-v0_8-darkgrid\")\n", + "sns.set_palette(\"husl\")\n" ] }, { @@ -143,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 2, "id": "1bbb653c", "metadata": {}, "outputs": [ @@ -235,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 3, "id": "bc254063", "metadata": {}, "outputs": [ @@ -256,7 +254,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 4, "id": "94727394", "metadata": {}, "outputs": [ @@ -286,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 5, "id": "e7685461", "metadata": {}, "outputs": [ @@ -315,7 +313,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 6, "id": "647df2e7", "metadata": {}, "outputs": [ @@ -354,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 7, "id": "8f65e4b1", "metadata": {}, "outputs": [], @@ -365,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 8, "id": "04bbcd27", "metadata": {}, "outputs": [ @@ -408,7 +406,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 9, "id": "908f7dce", "metadata": {}, "outputs": [ @@ -455,7 +453,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 10, "id": "bfe561fb", "metadata": {}, "outputs": [], @@ -475,7 +473,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 11, "id": "b52186fa", "metadata": {}, "outputs": [ @@ -504,7 +502,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 12, "id": "b4fca22b", "metadata": {}, "outputs": [ @@ -533,7 +531,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 13, "id": "f617c5aa", "metadata": {}, "outputs": [ @@ -618,7 +616,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 14, "id": "96b98795", "metadata": {}, "outputs": [ @@ -654,7 +652,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 15, "id": "344d3cab", "metadata": {}, "outputs": [], @@ -671,7 +669,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 16, "id": "9266362a", "metadata": {}, "outputs": [ @@ -692,7 +690,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 17, "id": "15ae5c81", "metadata": {}, "outputs": [], @@ -712,7 +710,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 18, "id": "4a9aa383", "metadata": {}, "outputs": [], @@ -733,7 +731,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 19, "id": "ed755501", "metadata": {}, "outputs": [ @@ -741,9 +739,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "0:\ttest: 0.8746563\tbest: 0.8746563 (0)\ttotal: 28.1ms\tremaining: 14s\n", - "100:\ttest: 0.9207882\tbest: 0.9207893 (99)\ttotal: 1.89s\tremaining: 7.48s\n", - "200:\ttest: 0.9248347\tbest: 0.9248575 (199)\ttotal: 3.28s\tremaining: 4.88s\n", + "0:\ttest: 0.8746563\tbest: 0.8746563 (0)\ttotal: 235ms\tremaining: 1m 57s\n", + "100:\ttest: 0.9207882\tbest: 0.9207893 (99)\ttotal: 17.6s\tremaining: 1m 9s\n", + "200:\ttest: 0.9248347\tbest: 0.9248575 (199)\ttotal: 23.4s\tremaining: 34.8s\n", "Stopped by overfitting detector (50 iterations wait)\n", "\n", "bestTest = 0.9249125201\n", @@ -755,10 +753,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 80, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -770,7 +768,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 20, "id": "b62bc10b", "metadata": {}, "outputs": [], @@ -782,7 +780,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 21, "id": "dbc909b3", "metadata": {}, "outputs": [ @@ -816,7 +814,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 22, "id": "9a92a89d", "metadata": {}, "outputs": [ @@ -844,7 +842,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 23, "id": "df84f1fa", "metadata": {}, "outputs": [ @@ -875,7 +873,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 24, "id": "68a8d19d", "metadata": {}, "outputs": [ @@ -975,3455 +973,6 @@ "4. Entraînez le modèle final avec les meilleurs paramètres" ] }, - { - "cell_type": "code", - "execution_count": 93, - "id": "1888b8c2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0:\ttotal: 81.2ms\tremaining: 40.5s\n", - "0:\ttotal: 83ms\tremaining: 41.4s\n", - "0:\ttotal: 81.8ms\tremaining: 40.8s\n", - "0:\ttotal: 87.2ms\tremaining: 43.5s\n", - "0:\ttotal: 90.8ms\tremaining: 45.3s\n", - "0:\ttotal: 98.8ms\tremaining: 49.3s\n", - "0:\ttotal: 98.9ms\tremaining: 49.3s\n", - "0:\ttotal: 106ms\tremaining: 53s\n", - "100:\ttotal: 3.71s\tremaining: 14.7s\n", - "100:\ttotal: 3.88s\tremaining: 15.3s\n", - "100:\ttotal: 3.9s\tremaining: 15.4s\n", - "100:\ttotal: 3.93s\tremaining: 15.5s\n", - "100:\ttotal: 3.99s\tremaining: 15.8s\n", - "100:\ttotal: 4.08s\tremaining: 16.1s\n", - "100:\ttotal: 4.13s\tremaining: 16.3s\n", - "100:\ttotal: 4.43s\tremaining: 17.5s\n", - "200:\ttotal: 7.97s\tremaining: 11.8s\n", - "200:\ttotal: 8.19s\tremaining: 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"KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[93]\u001b[39m\u001b[32m, line 33\u001b[39m\n\u001b[32m 23\u001b[39m grid_search = GridSearchCV(\n\u001b[32m 24\u001b[39m estimator=catboost,\n\u001b[32m 25\u001b[39m param_grid=param_grid,\n\u001b[32m (...)\u001b[39m\u001b[32m 29\u001b[39m n_jobs=-\u001b[32m1\u001b[39m,\n\u001b[32m 30\u001b[39m )\n\u001b[32m 32\u001b[39m \u001b[38;5;66;03m# Exécution de la recherche sur grille\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m33\u001b[39m \u001b[43mgrid_search\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcat_features\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcat_features\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 35\u001b[39m \u001b[38;5;66;03m# Afficher les meilleurs hyperparamètres\u001b[39;00m\n\u001b[32m 36\u001b[39m best_params = grid_search.best_params_\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Workspace/studies/.venv/lib/python3.13/site-packages/sklearn/base.py:1389\u001b[39m, in \u001b[36m_fit_context..decorator..wrapper\u001b[39m\u001b[34m(estimator, *args, **kwargs)\u001b[39m\n\u001b[32m 1382\u001b[39m estimator._validate_params()\n\u001b[32m 1384\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[32m 1385\u001b[39m skip_parameter_validation=(\n\u001b[32m 1386\u001b[39m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[32m 1387\u001b[39m )\n\u001b[32m 1388\u001b[39m ):\n\u001b[32m-> \u001b[39m\u001b[32m1389\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Workspace/studies/.venv/lib/python3.13/site-packages/sklearn/model_selection/_search.py:1024\u001b[39m, in \u001b[36mBaseSearchCV.fit\u001b[39m\u001b[34m(self, X, y, **params)\u001b[39m\n\u001b[32m 1018\u001b[39m results = \u001b[38;5;28mself\u001b[39m._format_results(\n\u001b[32m 1019\u001b[39m all_candidate_params, n_splits, all_out, all_more_results\n\u001b[32m 1020\u001b[39m )\n\u001b[32m 1022\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m results\n\u001b[32m-> \u001b[39m\u001b[32m1024\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_run_search\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevaluate_candidates\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1026\u001b[39m \u001b[38;5;66;03m# multimetric is determined here because in the case of a callable\u001b[39;00m\n\u001b[32m 1027\u001b[39m \u001b[38;5;66;03m# self.scoring the return type is only known after calling\u001b[39;00m\n\u001b[32m 1028\u001b[39m first_test_score = all_out[\u001b[32m0\u001b[39m][\u001b[33m\"\u001b[39m\u001b[33mtest_scores\u001b[39m\u001b[33m\"\u001b[39m]\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Workspace/studies/.venv/lib/python3.13/site-packages/sklearn/model_selection/_search.py:1571\u001b[39m, in \u001b[36mGridSearchCV._run_search\u001b[39m\u001b[34m(self, evaluate_candidates)\u001b[39m\n\u001b[32m 1569\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_run_search\u001b[39m(\u001b[38;5;28mself\u001b[39m, evaluate_candidates):\n\u001b[32m 1570\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Search all candidates in param_grid\"\"\"\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1571\u001b[39m \u001b[43mevaluate_candidates\u001b[49m\u001b[43m(\u001b[49m\u001b[43mParameterGrid\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mparam_grid\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Workspace/studies/.venv/lib/python3.13/site-packages/sklearn/model_selection/_search.py:970\u001b[39m, in \u001b[36mBaseSearchCV.fit..evaluate_candidates\u001b[39m\u001b[34m(candidate_params, cv, more_results)\u001b[39m\n\u001b[32m 962\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.verbose > \u001b[32m0\u001b[39m:\n\u001b[32m 963\u001b[39m \u001b[38;5;28mprint\u001b[39m(\n\u001b[32m 964\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mFitting \u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[33m folds for each of \u001b[39m\u001b[38;5;132;01m{1}\u001b[39;00m\u001b[33m candidates,\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 965\u001b[39m \u001b[33m\"\u001b[39m\u001b[33m totalling \u001b[39m\u001b[38;5;132;01m{2}\u001b[39;00m\u001b[33m fits\u001b[39m\u001b[33m\"\u001b[39m.format(\n\u001b[32m 966\u001b[39m n_splits, n_candidates, n_candidates * n_splits\n\u001b[32m 967\u001b[39m )\n\u001b[32m 968\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m970\u001b[39m out = \u001b[43mparallel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 971\u001b[39m \u001b[43m \u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_fit_and_score\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 972\u001b[39m \u001b[43m \u001b[49m\u001b[43mclone\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbase_estimator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 973\u001b[39m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 974\u001b[39m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 975\u001b[39m \u001b[43m \u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 976\u001b[39m \u001b[43m \u001b[49m\u001b[43mtest\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 977\u001b[39m \u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m=\u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 978\u001b[39m \u001b[43m \u001b[49m\u001b[43msplit_progress\u001b[49m\u001b[43m=\u001b[49m\u001b[43m(\u001b[49m\u001b[43msplit_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_splits\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 979\u001b[39m \u001b[43m \u001b[49m\u001b[43mcandidate_progress\u001b[49m\u001b[43m=\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcand_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_candidates\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 980\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mfit_and_score_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 981\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 982\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mcand_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43msplit_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mproduct\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 983\u001b[39m 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\u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 990\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mNo fits were performed. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 991\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mWas the CV iterator empty? \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 992\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mWere there no candidates?\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 993\u001b[39m )\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Workspace/studies/.venv/lib/python3.13/site-packages/sklearn/utils/parallel.py:77\u001b[39m, in \u001b[36mParallel.__call__\u001b[39m\u001b[34m(self, iterable)\u001b[39m\n\u001b[32m 72\u001b[39m config = get_config()\n\u001b[32m 73\u001b[39m iterable_with_config = (\n\u001b[32m 74\u001b[39m (_with_config(delayed_func, config), args, kwargs)\n\u001b[32m 75\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m delayed_func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m iterable\n\u001b[32m 76\u001b[39m )\n\u001b[32m---> \u001b[39m\u001b[32m77\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[34;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43miterable_with_config\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Workspace/studies/.venv/lib/python3.13/site-packages/joblib/parallel.py:2007\u001b[39m, in \u001b[36mParallel.__call__\u001b[39m\u001b[34m(self, iterable)\u001b[39m\n\u001b[32m 2001\u001b[39m \u001b[38;5;66;03m# The first item from the output is blank, but it makes the interpreter\u001b[39;00m\n\u001b[32m 2002\u001b[39m \u001b[38;5;66;03m# progress until it enters the Try/Except block of the generator and\u001b[39;00m\n\u001b[32m 2003\u001b[39m \u001b[38;5;66;03m# reaches the first `yield` statement. This starts the asynchronous\u001b[39;00m\n\u001b[32m 2004\u001b[39m \u001b[38;5;66;03m# dispatch of the tasks to the workers.\u001b[39;00m\n\u001b[32m 2005\u001b[39m \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[32m-> \u001b[39m\u001b[32m2007\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.return_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Workspace/studies/.venv/lib/python3.13/site-packages/joblib/parallel.py:1650\u001b[39m, in \u001b[36mParallel._get_outputs\u001b[39m\u001b[34m(self, iterator, pre_dispatch)\u001b[39m\n\u001b[32m 1647\u001b[39m \u001b[38;5;28;01myield\u001b[39;00m\n\u001b[32m 1649\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m._backend.retrieval_context():\n\u001b[32m-> \u001b[39m\u001b[32m1650\u001b[39m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m._retrieve()\n\u001b[32m 1652\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mGeneratorExit\u001b[39;00m:\n\u001b[32m 1653\u001b[39m \u001b[38;5;66;03m# The generator has been garbage collected before being fully\u001b[39;00m\n\u001b[32m 1654\u001b[39m \u001b[38;5;66;03m# consumed. This aborts the remaining tasks if possible and warn\u001b[39;00m\n\u001b[32m 1655\u001b[39m \u001b[38;5;66;03m# the user if necessary.\u001b[39;00m\n\u001b[32m 1656\u001b[39m \u001b[38;5;28mself\u001b[39m._exception = \u001b[38;5;28;01mTrue\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Workspace/studies/.venv/lib/python3.13/site-packages/joblib/parallel.py:1762\u001b[39m, in \u001b[36mParallel._retrieve\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 1757\u001b[39m \u001b[38;5;66;03m# If the next job is not ready for retrieval yet, we just wait for\u001b[39;00m\n\u001b[32m 1758\u001b[39m \u001b[38;5;66;03m# async callbacks to progress.\u001b[39;00m\n\u001b[32m 1759\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ((\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m._jobs) == \u001b[32m0\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m\n\u001b[32m 1760\u001b[39m (\u001b[38;5;28mself\u001b[39m._jobs[\u001b[32m0\u001b[39m].get_status(\n\u001b[32m 1761\u001b[39m timeout=\u001b[38;5;28mself\u001b[39m.timeout) == TASK_PENDING)):\n\u001b[32m-> \u001b[39m\u001b[32m1762\u001b[39m \u001b[43mtime\u001b[49m\u001b[43m.\u001b[49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m0.01\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 1763\u001b[39m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[32m 1765\u001b[39m \u001b[38;5;66;03m# We need to be careful: the job list can be filling up as\u001b[39;00m\n\u001b[32m 1766\u001b[39m \u001b[38;5;66;03m# we empty it and Python list are not thread-safe by\u001b[39;00m\n\u001b[32m 1767\u001b[39m \u001b[38;5;66;03m# default hence the use of the lock\u001b[39;00m\n", - "\u001b[31mKeyboardInterrupt\u001b[39m: " - ] - } - ], - "source": [ - "# Définir la grille d'hyperparamètres à rechercher\n", - "param_grid = {\n", - " \"learning_rate\": [0.01, 0.05, 0.1, 0.2],\n", - " \"depth\": [4, 6, 8, 10],\n", - " \"l2_leaf_reg\": [1, 3, 5, 7],\n", - " \"bagging_temperature\": [0, 0.5, 1],\n", - "}\n", - "\n", - "# Nombre de folds pour la validation croisée\n", - "num_folds = 5\n", - "\n", - "# Initialisation du modèle CatBoostClassifier\n", - "catboost = CatBoostClassifier(\n", - " iterations=500,\n", - " loss_function=\"Logloss\",\n", - " eval_metric=\"AUC\",\n", - " random_seed=42,\n", - " verbose=100,\n", - " early_stopping_rounds=50,\n", - ")\n", - "\n", - "# Création de l'objet GridSearchCV pour la recherche sur grille avec validation croisée\n", - "grid_search = GridSearchCV(\n", - " estimator=catboost,\n", - " param_grid=param_grid,\n", - " cv=KFold(\n", - " n_splits=num_folds, shuffle=True, random_state=42\n", - " ),\n", - " n_jobs=-1,\n", - ")\n", - "\n", - "# Exécution de la recherche sur grille\n", - "grid_search.fit(X_train, y_train, cat_features=cat_features)\n", - "\n", - "# Afficher les meilleurs hyperparamètres\n", - "best_params = grid_search.best_params_\n", - "print(\"Meilleurs hyperparamètres : \", best_params)" - ] - }, { "cell_type": "markdown", "id": "03e1ee55", @@ -4458,7 +1007,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 25, "id": "65693a22", "metadata": {}, "outputs": [ @@ -4466,9 +1015,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "0:\ttest: 0.8746563\tbest: 0.8746563 (0)\ttotal: 24.1ms\tremaining: 12s\n", - "100:\ttest: 0.9207882\tbest: 0.9207893 (99)\ttotal: 1.44s\tremaining: 5.68s\n", - "200:\ttest: 0.9248347\tbest: 0.9248575 (199)\ttotal: 3.1s\tremaining: 4.61s\n", + "0:\ttest: 0.8746563\tbest: 0.8746563 (0)\ttotal: 172ms\tremaining: 1m 25s\n", + "100:\ttest: 0.9207882\tbest: 0.9207893 (99)\ttotal: 8.11s\tremaining: 32s\n", + "200:\ttest: 0.9248347\tbest: 0.9248575 (199)\ttotal: 12.4s\tremaining: 18.4s\n", "Stopped by overfitting detector (50 iterations wait)\n", "\n", "bestTest = 0.9249125201\n", @@ -4519,7 +1068,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "af1cb8af", "metadata": {}, "outputs": [ @@ -4532,14 +1081,6 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[4254 277]\n", - " [ 529 973]]\n" - ] } ], "source": [ @@ -4583,11 +1124,160 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "e74f071f", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# Création de la variable defaut (oubli dans l'énoncé)\n", + "df[\"montant_credit\"] = np.random.uniform(3000, 450000)\n", + "df[\"defaut\"] = df[\"capital_gain\"] < df[\"capital_loss\"]\n", + "\n", + "df = df.drop([\"capital_gain\", \"capital_loss\"], axis=1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "5a9ac689", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0:\tlearn: 49203.9724124\ttest: 50617.1901164\tbest: 50617.1901164 (0)\ttotal: 41ms\tremaining: 20.5s\n", + "100:\tlearn: 48437.1893196\ttest: 50052.4073304\tbest: 50038.4930903 (73)\ttotal: 2.54s\tremaining: 10s\n", + "200:\tlearn: 47905.0927458\ttest: 50121.0948143\tbest: 50038.4930903 (73)\ttotal: 6.04s\tremaining: 8.98s\n", + "300:\tlearn: 47372.1749510\ttest: 50166.1469085\tbest: 50038.4930903 (73)\ttotal: 9.39s\tremaining: 6.21s\n", + "400:\tlearn: 46855.4993995\ttest: 50270.6663358\tbest: 50038.4930903 (73)\ttotal: 13.2s\tremaining: 3.26s\n", + "499:\tlearn: 46487.9636285\ttest: 50308.7451900\tbest: 50038.4930903 (73)\ttotal: 16.3s\tremaining: 0us\n", + "\n", + "bestTest = 50038.49309\n", + "bestIteration = 73\n", + "\n", + "Shrink model to first 74 iterations.\n", + "MAE : 20,089.78€\n", + "RMSE : 50,038.49€\n", + "R² : 0.0267\n", + "\\nPerte totale réelle : 66,263,722.75€\n", + "Perte totale prédite : 64,040,461.12€\n", + "Erreur : 2,223,261.63€\n" + ] + } + ], + "source": [ + "from catboost import CatBoostRegressor\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", + "\n", + "# Création de la cible de régression\n", + "np.random.seed(42)\n", + "df[\"montant_defaut\"] = df.apply(\n", + " lambda row: row[\"montant_credit\"] * np.random.uniform(0.3, 0.8)\n", + " if row[\"defaut\"] == 1\n", + " else 0,\n", + " axis=1,\n", + ")\n", + "\n", + "# Préparation\n", + "X_reg = df.drop([\"defaut\", \"montant_defaut\"], axis=1)\n", + "y_reg = df[\"montant_defaut\"]\n", + "\n", + "X_train_reg, X_test_reg, y_train_reg, y_test_reg = train_test_split(\n", + " X_reg, y_reg, test_size=0.2, random_state=42\n", + ")\n", + "\n", + "# Pools\n", + "train_pool_reg = Pool(X_train_reg, y_train_reg, cat_features=cat_features)\n", + "test_pool_reg = Pool(X_test_reg, y_test_reg, cat_features=cat_features)\n", + "\n", + "# Modèle\n", + "model_reg = CatBoostRegressor(\n", + " iterations=500, learning_rate=0.1, depth=6, random_seed=42, verbose=100\n", + ")\n", + "\n", + "model_reg.fit(train_pool_reg, eval_set=test_pool_reg)\n", + "\n", + "# Prédictions\n", + "y_pred_reg = model_reg.predict(X_test_reg)\n", + "\n", + "# Évaluation\n", + "print(f\"MAE : {mean_absolute_error(y_test_reg, y_pred_reg):,.2f}€\")\n", + "print(f\"RMSE : {np.sqrt(mean_squared_error(y_test_reg, y_pred_reg)):,.2f}€\")\n", + "print(f\"R² : {r2_score(y_test_reg, y_pred_reg):.4f}\")\n", + "\n", + "# Perte attendue totale\n", + "perte_totale_reelle = y_test_reg.sum()\n", + "perte_totale_predite = y_pred_reg.sum()\n", + "print(f\"\\\\nPerte totale réelle : {perte_totale_reelle:,.2f}€\")\n", + "print(f\"Perte totale prédite : {perte_totale_predite:,.2f}€\")\n", + "print(f\"Erreur : {abs(perte_totale_reelle - perte_totale_predite):,.2f}€\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "e71ef5a4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Importance des variables ===\n", + "\n", + " feature importance\n", + "12 income 50.866106\n", + "3 education 7.719029\n", + "4 education_num 7.679601\n", + "2 fnlwgt 5.390006\n", + "6 occupation 4.938505\n", + "5 marital_status 4.321928\n", + "11 native_country 4.066952\n", + "0 age 3.823869\n", + "10 hours_per_week 3.527171\n", + "7 relationship 2.400281\n", + "1 workclass 2.350175\n", + "8 race 2.238380\n", + "9 sex 0.677997\n", + "13 montant_credit 0.000000\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Importance des variables\n", + "\n", + "print(\"\\n=== Importance des variables ===\\n\")\n", + "\n", + "feature_importance = model_reg.get_feature_importance(train_pool_reg)\n", + "feature_names = X_reg.columns\n", + "\n", + "importance_df = pd.DataFrame(\n", + " {\"feature\": feature_names, \"importance\": feature_importance}\n", + ").sort_values(\"importance\", ascending=False)\n", + "\n", + "print(importance_df)\n", + "\n", + "# Visualisation\n", + "plt.figure(figsize=(10, 8))\n", + "plt.barh(importance_df[\"feature\"][:15], importance_df[\"importance\"][:15])\n", + "plt.xlabel(\"Importance\")\n", + "plt.title(\"Top 15 variables les plus importantes\")\n", + "plt.gca().invert_yaxis()\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] }, { "cell_type": "markdown", @@ -4607,17 +1297,17 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 30, "id": "c9bbd9a1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 99, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -4639,7 +1329,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 31, "id": "9f10f1a3", "metadata": {}, "outputs": [], @@ -4650,7 +1340,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 32, "id": "89f57e71", "metadata": {}, "outputs": [ @@ -4680,7 +1370,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 33, "id": "dd70cf9c", "metadata": {}, "outputs": [ @@ -4695,10 +1385,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 102, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -4731,7 +1421,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 34, "id": "c9a3f8ae", "metadata": {}, "outputs": [], @@ -4745,7 +1435,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 35, "id": "8c29907f", "metadata": {}, "outputs": [ @@ -4766,7 +1456,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 36, "id": "d638560d", "metadata": {}, "outputs": [ @@ -4804,7 +1494,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 37, "id": "79488875", "metadata": {}, "outputs": [