2 lines (1 with data), 465.1 kB

{"cells":[{"cell_type":"code","execution_count":3,"metadata":{"execution":{"iopub.execute_input":"2024-06-14T01:34:44.941368Z","iopub.status.busy":"2024-06-14T01:34:44.940988Z","iopub.status.idle":"2024-06-14T01:34:48.319440Z","shell.execute_reply":"2024-06-14T01:34:48.318388Z","shell.execute_reply.started":"2024-06-14T01:34:44.941338Z"},"trusted":true},"outputs":[],"source":["# Configuration du chemin d'accès\n","import sys\n","import os\n","sys.path.append(os.path.abspath(os.path.join('..', 'src')))\n","\n","# Configuration du chemin d'accès\n","import sys\n","import os\n","sys.path.append(os.path.abspath(os.path.join('..', 'src')))\n","\n","# PANDAS\n","import pandas as pd \n","pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None) \n","\n","# WARNINGS\n","import warnings\n","warnings.filterwarnings('ignore')\n","\n","# NUMPY\n","import numpy as np\n","\n","# STATS\n","import scipy.stats as stats\n","from scipy.stats import norm, skew\n","import scipy as sp\n","from scipy.stats import chi2_contingency\n","\n","# MATPLOTLIB\n","import matplotlib as mlp\n","import matplotlib.pyplot as plt\n","plt.style.use('fivethirtyeight') \n","%matplotlib inline\n","\n","# PANDAS\n","import pandas as pd \n","pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None) \n","\n","# SEABORN\n","import seaborn as sns\n","\n","# SCIKIT-LEARN: MODELES\n","from sklearn.linear_model import LogisticRegression # Régression logistique\n","from sklearn.svm import SVC # Support Vector Classifier\n","from sklearn.ensemble import RandomForestClassifier # Random Forest\n","from sklearn.ensemble import GradientBoostingClassifier # Gradient Boosting\n","from sklearn.ensemble import AdaBoostClassifier # AdaBoost\n","from sklearn.ensemble import BaggingClassifier # Bagging\n","\n","\n","# SCIKIT-LEARN: VALIDATION CROISEE + OPTIMISATION\n","from sklearn.model_selection import train_test_split # Séparer en données train et test\n","from sklearn.model_selection import cross_val_score # Validation croisée pour comparison entre modèles\n","from sklearn.model_selection import validation_curve # Courbe de validation : visulaisr des scores lors du choix d'un hyper-paramètre\n","from sklearn.model_selection import GridSearchCV # Tester plusieurs hyper-paramètres\n","from sklearn.model_selection import learning_curve # Courbe d'apprentissage : visualisation des scores du train et du validation sets en fonction des quanitiés des données\n","from sklearn.impute import SimpleImputer\n","from sklearn.preprocessing import OrdinalEncoder\n","\n"," ## YellowBrick\n","from yellowbrick.model_selection import LearningCurve\n","from yellowbrick.model_selection import ValidationCurve\n","\n","## EVALUATION\n","from sklearn.metrics import accuracy_score\n","from sklearn.metrics import f1_score\n","from sklearn.metrics import confusion_matrix\n","from sklearn.metrics import ConfusionMatrixDisplay\n","from sklearn.metrics import classification_report\n","\n","# SCHIKIT-LEARN: PIPELINE AND TRANSFORMATEURll\n","from sklearn.pipeline import make_pipeline\n","from sklearn.compose import make_column_transformer"]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2024-06-14T01:34:48.321891Z","iopub.status.busy":"2024-06-14T01:34:48.321438Z","iopub.status.idle":"2024-06-14T01:34:48.867603Z","shell.execute_reply":"2024-06-14T01:34:48.866423Z","shell.execute_reply.started":"2024-06-14T01:34:48.321863Z"},"trusted":true},"outputs":[],"source":["data = pd.read_csv('/Users/massil/Desktop/ai28-projet-ml/data/smoking_driking_dataset_Ver01.csv'\n","                   , nrows=100000\n","                )\n","df_smoking_drinking = data.copy()"]},{"cell_type":"code","execution_count":5,"metadata":{"execution":{"iopub.execute_input":"2024-06-14T01:34:48.869204Z","iopub.status.busy":"2024-06-14T01:34:48.868855Z","iopub.status.idle":"2024-06-14T01:34:48.876935Z","shell.execute_reply":"2024-06-14T01:34:48.875744Z","shell.execute_reply.started":"2024-06-14T01:34:48.869175Z"},"trusted":true},"outputs":[],"source":["def preprocess_data(data, target):\n","    X = data.drop(columns=[target])\n","    y = data[target]\n","    return X, y\n","\n","def split_data(data, target, test_size=0.2, val_size=0.1):\n","    X, y = preprocess_data(data, target)\n","    X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=test_size + val_size, stratify=y)\n","    X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=test_size / (test_size + val_size), stratify=y_temp)\n","    return X_train, X_val, X_test, y_train, y_val, y_test"]},{"cell_type":"code","execution_count":6,"metadata":{"execution":{"iopub.execute_input":"2024-06-14T01:34:48.878856Z","iopub.status.busy":"2024-06-14T01:34:48.878428Z","iopub.status.idle":"2024-06-14T01:34:49.012012Z","shell.execute_reply":"2024-06-14T01:34:49.010873Z","shell.execute_reply.started":"2024-06-14T01:34:48.878818Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["X_train shape: (69999, 23)\n","X_val shape: (10000, 23)\n","X_test shape: (20001, 23)\n"]}],"source":["X_train, X_val, X_test, y_train, y_val, y_test = split_data(df_smoking_drinking, 'SMK_stat_type_cd')\n","\n","print('X_train shape:', X_train.shape)\n","print('X_val shape:', X_val.shape)\n","print('X_test shape:', X_test.shape)"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["# Séparation variables continue/catégorielles"]},{"cell_type":"code","execution_count":7,"metadata":{"execution":{"iopub.execute_input":"2024-06-14T01:34:49.015335Z","iopub.status.busy":"2024-06-14T01:34:49.014979Z","iopub.status.idle":"2024-06-14T01:34:49.031371Z","shell.execute_reply":"2024-06-14T01:34:49.030272Z","shell.execute_reply.started":"2024-06-14T01:34:49.015305Z"},"trusted":true},"outputs":[],"source":["# Normalisation des variables continues\n","from sklearn.preprocessing import StandardScaler\n","\n","cont_features = df_smoking_drinking.select_dtypes('float64').columns\n","cont_features = cont_features.drop('SMK_stat_type_cd')\n","\n","cat_features = df_smoking_drinking.select_dtypes(include = ['int64', 'object']).columns\n","cat_features = cat_features.drop(['DRK_YN'])"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## SVM - Pipeline"]},{"cell_type":"code","execution_count":8,"metadata":{"execution":{"iopub.execute_input":"2024-06-14T01:34:49.033204Z","iopub.status.busy":"2024-06-14T01:34:49.032829Z","iopub.status.idle":"2024-06-14T01:34:49.040878Z","shell.execute_reply":"2024-06-14T01:34:49.039638Z","shell.execute_reply.started":"2024-06-14T01:34:49.033173Z"},"trusted":true},"outputs":[],"source":["def create_model_pipeline(cat_features, cont_features, model_class, **model_params):\n","    # Créer les pipelines pour les caractéristiques catégorielles et numériques\n","    categorical_pipeline = make_pipeline(\n","        SimpleImputer(strategy='most_frequent'),\n","        OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1)  # Gérer les catégories inconnues\n","    )\n","\n","    numeric_pipeline = make_pipeline(\n","        SimpleImputer(),\n","        StandardScaler()\n","    )\n","\n","    # Créer le préprocesseur\n","    preprocessor_robust = make_column_transformer(\n","        (categorical_pipeline, cat_features),\n","        (numeric_pipeline, cont_features)\n","    )\n","\n","    # Créer le pipeline avec le modèle spécifié et ses paramètres\n","    model_pipeline = make_pipeline(preprocessor_robust, model_class(**model_params))\n","    return model_pipeline\n"]},{"cell_type":"code","execution_count":9,"metadata":{"execution":{"iopub.execute_input":"2024-06-14T01:34:49.042757Z","iopub.status.busy":"2024-06-14T01:34:49.042386Z","iopub.status.idle":"2024-06-14T01:34:49.059278Z","shell.execute_reply":"2024-06-14T01:34:49.057979Z","shell.execute_reply.started":"2024-06-14T01:34:49.042727Z"},"trusted":true},"outputs":[],"source":["# Create the pipeline for SVC with a linear kernel\n","svc_pipe_linear = create_model_pipeline(\n","    cat_features, cont_features, SVC, kernel='linear', C=1.0, class_weight='balanced')\n","\n","# Create the pipeline for SVC with an RBF kernel\n","svc_pipe_rbf = create_model_pipeline(\n","    cat_features, cont_features, SVC, kernel='rbf', C=1.0, gamma='scale',class_weight='balanced')\n","\n","# Create the pipeline for SVC with a polynomial kernel\n","svc_pipe_poly = create_model_pipeline(\n","    cat_features, cont_features, SVC, kernel='poly', C=1.0, degree=3, class_weight='balanced')"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["### SVM - Entraînement"]},{"cell_type":"code","execution_count":10,"metadata":{"execution":{"iopub.execute_input":"2024-06-14T01:34:49.061025Z","iopub.status.busy":"2024-06-14T01:34:49.060627Z","iopub.status.idle":"2024-06-14T01:34:49.071219Z","shell.execute_reply":"2024-06-14T01:34:49.070153Z","shell.execute_reply.started":"2024-06-14T01:34:49.060996Z"},"trusted":true},"outputs":[],"source":["def train_and_evaluate_model(model, X_train, y_train, X_test, y_test, model_name):\n","    print('\\n\\n')\n","    print(f\"--- Evaluation du modèle : {model_name} ---\")\n","\n","    # Entraînement du modèle\n","    model.fit(X_train, y_train)\n","\n","    # Prédiction sur le jeu de test\n","    y_test_pred = model.predict(X_test)\n","\n","    # Évaluation du modèle\n","    accuracy = accuracy_score(y_test, y_test_pred)\n","    f1 = f1_score(y_test, y_test_pred, average='weighted')\n","    print('Accuracy:', accuracy)\n","    print('F1:', f1)\n","\n","    # Matrice de confusion\n","    cm = confusion_matrix(y_test, y_test_pred)\n","    disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n","    fig, ax = plt.subplots(figsize=(10, 10))\n","    plt.title(f\"Confusion Matrix for {model_name}\")  # Add model name to the title\n","    disp.plot(ax=ax, values_format='d')  # Utiliser le format 'd' pour afficher les nombres entiers\n","\n","\n","    # Rapport de classification\n","    print(classification_report(y_test, y_test_pred))\n","\n","    # Score du modèle\n","    score = model.score(X_test, y_test)\n","    print('Score :', score)\n","    \n","    return accuracy, f1, score"]},{"cell_type":"code","execution_count":11,"metadata":{"execution":{"iopub.execute_input":"2024-06-14T01:34:49.072985Z","iopub.status.busy":"2024-06-14T01:34:49.072579Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","\n","\n","--- Evaluation du modèle : SVC linear kernel ---\n","Accuracy: 0.6824658767061647\n","F1: 0.7032159820322408\n","              precision    recall  f1-score   support\n","\n","         1.0       0.94      0.73      0.82     12112\n","         2.0       0.42      0.59      0.49      3551\n","         3.0       0.48      0.62      0.54      4338\n","\n","    accuracy                           0.68     20001\n","   macro avg       0.61      0.65      0.62     20001\n","weighted avg       0.75      0.68      0.70     20001\n","\n","Score : 0.6824658767061647\n","\n","\n","\n","--- Evaluation du modèle : SVC RBF kernel ---\n","Accuracy: 0.6861156942152893\n","F1: 0.7062575857154125\n","              precision    recall  f1-score   support\n","\n","         1.0       0.94      0.73      0.82     12112\n","         2.0       0.43      0.59      0.50      3551\n","         3.0       0.49      0.64      0.55      4338\n","\n","    accuracy                           0.69     20001\n","   macro avg       0.62      0.65      0.62     20001\n","weighted avg       0.75      0.69      0.71     20001\n","\n","Score : 0.6861156942152893\n","\n","\n","\n","--- Evaluation du modèle : SVC polynomial kernel ---\n","Accuracy: 0.6873156342182891\n","F1: 0.7067236808380714\n","              precision    recall  f1-score   support\n","\n","         1.0       0.94      0.73      0.82     12112\n","         2.0       0.43      0.55      0.49      3551\n","         3.0       0.48      0.67      0.56      4338\n","\n","    accuracy                           0.69     20001\n","   macro avg       0.62      0.65      0.62     20001\n","weighted avg       0.75      0.69      0.71     20001\n","\n","Score : 0.6873156342182891\n"]},{"data":{"text/plain":["(0.6873156342182891, 0.7067236808380714, 0.6873156342182891)"]},"execution_count":11,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 1000x1000 with 2 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","text/plain":["<Figure size 1000x1000 with 2 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","text/plain":["<Figure size 1000x1000 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["#424min = 7H\n","train_and_evaluate_model(svc_pipe_linear, X_train, y_train, X_test, y_test, model_name='SVC linear kernel')\n","train_and_evaluate_model(svc_pipe_rbf, X_train, y_train, X_test, y_test, model_name='SVC RBF kernel')\n","train_and_evaluate_model(svc_pipe_poly, X_train, y_train, X_test, y_test, model_name='SVC polynomial kernel')\n"]},{"cell_type":"code","execution_count":12,"metadata":{"trusted":true},"outputs":[],"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","from yellowbrick.model_selection import LearningCurve\n","\n","def plot_learning_curve(pipeline, X_train, y_train, model_name=\"Model\", cv=3, scoring='accuracy'):\n","    # Extract the last estimator from the pipeline\n","    model_step_name = list(pipeline.named_steps.keys())[-1]\n","    model = pipeline.named_steps[model_step_name]\n","    \n","    plt.figure()\n","    visualizer = LearningCurve(\n","        model,\n","        cv=cv,\n","        scoring=scoring,\n","        train_sizes=np.linspace(0.1, 1.0, 10),\n","        n_jobs=-1\n","    )\n","    visualizer.fit(pipeline[:-1].fit_transform(X_train, y_train), y_train)\n","    visualizer.finalize()\n","    visualizer.show(title=f\"Learning Curve for {model_name}\")\n","    plt.show()\n"]},{"cell_type":"code","execution_count":13,"metadata":{"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure 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","text/plain":["<Figure 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","text/plain":["<Figure size 800x550 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["CPU times: user 3.94 s, sys: 1.63 s, total: 5.56 s\n","Wall time: 5h 51min 28s\n"]}],"source":["%%time\n","plot_learning_curve(svc_pipe_linear, X_train, y_train, model_name=\"SVC avec noyau linéaire\")\n","plot_learning_curve(svc_pipe_rbf, X_train, y_train, model_name=\"SVC avec noyau RBF\")\n","plot_learning_curve(svc_pipe_poly, X_train, y_train, model_name=\"SVC avec noyau polynomial\")"]},{"cell_type":"code","execution_count":14,"metadata":{"trusted":true},"outputs":[],"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","from yellowbrick.model_selection import ValidationCurve\n","\n","def plot_validation_curve(pipeline, X_train, y_train, param_name, param_range, model_name=\"Model\", cv=3, scoring='accuracy'):\n","    # Extract the last estimator from the pipeline\n","    model_step_name = list(pipeline.named_steps.keys())[-1]\n","    model = pipeline.named_steps[model_step_name]\n","    \n","    plt.figure()\n","    visualizer = ValidationCurve(\n","        model, param_name=param_name, param_range=param_range,\n","        cv=cv, scoring=scoring, n_jobs=-1\n","    )\n","    visualizer.fit(pipeline[:-1].fit_transform(X_train, y_train), y_train)\n","    visualizer.finalize()\n","    visualizer.show(title=f\"Validation Curve for {model_name} with parameter {param_name}\")\n","    plt.show()\n"]},{"cell_type":"code","execution_count":15,"metadata":{"trusted":true},"outputs":[{"ename":"KeyboardInterrupt","evalue":"","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","File \u001b[0;32m<timed exec>:3\u001b[0m\n","Cell \u001b[0;32mIn[14], line 15\u001b[0m, in \u001b[0;36mplot_validation_curve\u001b[0;34m(pipeline, X_train, y_train, param_name, param_range, model_name, cv, scoring)\u001b[0m\n\u001b[1;32m     10\u001b[0m plt\u001b[39m.\u001b[39mfigure()\n\u001b[1;32m     11\u001b[0m visualizer \u001b[39m=\u001b[39m ValidationCurve(\n\u001b[1;32m     12\u001b[0m     model, param_name\u001b[39m=\u001b[39mparam_name, param_range\u001b[39m=\u001b[39mparam_range,\n\u001b[1;32m     13\u001b[0m     cv\u001b[39m=\u001b[39mcv, scoring\u001b[39m=\u001b[39mscoring, n_jobs\u001b[39m=\u001b[39m\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m\n\u001b[1;32m     14\u001b[0m )\n\u001b[0;32m---> 15\u001b[0m visualizer\u001b[39m.\u001b[39mfit(pipeline[:\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m]\u001b[39m.\u001b[39mfit_transform(X_train, y_train), y_train)\n\u001b[1;32m     16\u001b[0m visualizer\u001b[39m.\u001b[39mfinalize()\n\u001b[1;32m     17\u001b[0m visualizer\u001b[39m.\u001b[39mshow(title\u001b[39m=\u001b[39m\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mValidation Curve for \u001b[39m\u001b[39m{\u001b[39;00mmodel_name\u001b[39m}\u001b[39;00m\u001b[39m with parameter \u001b[39m\u001b[39m{\u001b[39;00mparam_name\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n","File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/yellowbrick/model_selection/validation_curve.py:233\u001b[0m, in \u001b[0;36mValidationCurve.fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    219\u001b[0m skvc_kwargs \u001b[39m=\u001b[39m {\n\u001b[1;32m    220\u001b[0m     key: \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_params()[key]\n\u001b[1;32m    221\u001b[0m     \u001b[39mfor\u001b[39;00m key \u001b[39min\u001b[39;00m (\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    229\u001b[0m     )\n\u001b[1;32m    230\u001b[0m }\n\u001b[1;32m    232\u001b[0m \u001b[39m# compute the validation curve and store scores\u001b[39;00m\n\u001b[0;32m--> 233\u001b[0m curve \u001b[39m=\u001b[39m sk_validation_curve(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mestimator, X, y, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mskvc_kwargs)\n\u001b[1;32m    234\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrain_scores_, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtest_scores_ \u001b[39m=\u001b[39m curve\n\u001b[1;32m    236\u001b[0m \u001b[39m# compute the mean and standard deviation of the training data\u001b[39;00m\n","File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:211\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    205\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    206\u001b[0m     \u001b[39mwith\u001b[39;00m config_context(\n\u001b[1;32m    207\u001b[0m         skip_parameter_validation\u001b[39m=\u001b[39m(\n\u001b[1;32m    208\u001b[0m             prefer_skip_nested_validation \u001b[39mor\u001b[39;00m global_skip_validation\n\u001b[1;32m    209\u001b[0m         )\n\u001b[1;32m    210\u001b[0m     ):\n\u001b[0;32m--> 211\u001b[0m         \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m    212\u001b[0m \u001b[39mexcept\u001b[39;00m InvalidParameterError \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m    213\u001b[0m     \u001b[39m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[1;32m    214\u001b[0m     \u001b[39m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[1;32m    215\u001b[0m     \u001b[39m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[1;32m    216\u001b[0m     \u001b[39m# message to avoid confusion.\u001b[39;00m\n\u001b[1;32m    217\u001b[0m     msg \u001b[39m=\u001b[39m re\u001b[39m.\u001b[39msub(\n\u001b[1;32m    218\u001b[0m         \u001b[39mr\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mparameter of \u001b[39m\u001b[39m\\\u001b[39m\u001b[39mw+ must be\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m    219\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mparameter of \u001b[39m\u001b[39m{\u001b[39;00mfunc\u001b[39m.\u001b[39m\u001b[39m__qualname__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m must be\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m    220\u001b[0m         \u001b[39mstr\u001b[39m(e),\n\u001b[1;32m    221\u001b[0m     )\n","File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:1985\u001b[0m, in \u001b[0;36mvalidation_curve\u001b[0;34m(estimator, X, y, param_name, param_range, groups, cv, scoring, n_jobs, pre_dispatch, verbose, error_score, fit_params)\u001b[0m\n\u001b[1;32m   1982\u001b[0m scorer \u001b[39m=\u001b[39m check_scoring(estimator, scoring\u001b[39m=\u001b[39mscoring)\n\u001b[1;32m   1984\u001b[0m parallel \u001b[39m=\u001b[39m Parallel(n_jobs\u001b[39m=\u001b[39mn_jobs, pre_dispatch\u001b[39m=\u001b[39mpre_dispatch, verbose\u001b[39m=\u001b[39mverbose)\n\u001b[0;32m-> 1985\u001b[0m results \u001b[39m=\u001b[39m parallel(\n\u001b[1;32m   1986\u001b[0m     delayed(_fit_and_score)(\n\u001b[1;32m   1987\u001b[0m         clone(estimator),\n\u001b[1;32m   1988\u001b[0m         X,\n\u001b[1;32m   1989\u001b[0m         y,\n\u001b[1;32m   1990\u001b[0m         scorer,\n\u001b[1;32m   1991\u001b[0m         train,\n\u001b[1;32m   1992\u001b[0m         test,\n\u001b[1;32m   1993\u001b[0m         verbose,\n\u001b[1;32m   1994\u001b[0m         parameters\u001b[39m=\u001b[39m{param_name: v},\n\u001b[1;32m   1995\u001b[0m         fit_params\u001b[39m=\u001b[39mfit_params,\n\u001b[1;32m   1996\u001b[0m         return_train_score\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,\n\u001b[1;32m   1997\u001b[0m         error_score\u001b[39m=\u001b[39merror_score,\n\u001b[1;32m   1998\u001b[0m     )\n\u001b[1;32m   1999\u001b[0m     \u001b[39m# NOTE do not change order of iteration to allow one time cv splitters\u001b[39;00m\n\u001b[1;32m   2000\u001b[0m     \u001b[39mfor\u001b[39;00m train, test \u001b[39min\u001b[39;00m cv\u001b[39m.\u001b[39msplit(X, y, groups)\n\u001b[1;32m   2001\u001b[0m     \u001b[39mfor\u001b[39;00m v \u001b[39min\u001b[39;00m param_range\n\u001b[1;32m   2002\u001b[0m )\n\u001b[1;32m   2003\u001b[0m n_params \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(param_range)\n\u001b[1;32m   2005\u001b[0m results \u001b[39m=\u001b[39m _aggregate_score_dicts(results)\n","File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/utils/parallel.py:65\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m     60\u001b[0m config \u001b[39m=\u001b[39m get_config()\n\u001b[1;32m     61\u001b[0m iterable_with_config \u001b[39m=\u001b[39m (\n\u001b[1;32m     62\u001b[0m     (_with_config(delayed_func, config), args, kwargs)\n\u001b[1;32m     63\u001b[0m     \u001b[39mfor\u001b[39;00m delayed_func, args, kwargs \u001b[39min\u001b[39;00m iterable\n\u001b[1;32m     64\u001b[0m )\n\u001b[0;32m---> 65\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39m\u001b[39m__call__\u001b[39m(iterable_with_config)\n","File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/joblib/parallel.py:1098\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1095\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_iterating \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[1;32m   1097\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend\u001b[39m.\u001b[39mretrieval_context():\n\u001b[0;32m-> 1098\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mretrieve()\n\u001b[1;32m   1099\u001b[0m \u001b[39m# Make sure that we get a last message telling us we are done\u001b[39;00m\n\u001b[1;32m   1100\u001b[0m elapsed_time \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime() \u001b[39m-\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_start_time\n","File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/joblib/parallel.py:975\u001b[0m, in \u001b[0;36mParallel.retrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    973\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    974\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mgetattr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend, \u001b[39m'\u001b[39m\u001b[39msupports_timeout\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mFalse\u001b[39;00m):\n\u001b[0;32m--> 975\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_output\u001b[39m.\u001b[39mextend(job\u001b[39m.\u001b[39mget(timeout\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtimeout))\n\u001b[1;32m    976\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    977\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_output\u001b[39m.\u001b[39mextend(job\u001b[39m.\u001b[39mget())\n","File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/joblib/_parallel_backends.py:567\u001b[0m, in \u001b[0;36mLokyBackend.wrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    564\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Wrapper for Future.result to implement the same behaviour as\u001b[39;00m\n\u001b[1;32m    565\u001b[0m \u001b[39mAsyncResults.get from multiprocessing.\"\"\"\u001b[39;00m\n\u001b[1;32m    566\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 567\u001b[0m     \u001b[39mreturn\u001b[39;00m future\u001b[39m.\u001b[39mresult(timeout\u001b[39m=\u001b[39mtimeout)\n\u001b[1;32m    568\u001b[0m \u001b[39mexcept\u001b[39;00m CfTimeoutError \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m    569\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mTimeoutError\u001b[39;00m \u001b[39mfrom\u001b[39;00m \u001b[39me\u001b[39;00m\n","File \u001b[0;32m~/anaconda3/lib/python3.11/concurrent/futures/_base.py:451\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    448\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_state \u001b[39m==\u001b[39m FINISHED:\n\u001b[1;32m    449\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__get_result()\n\u001b[0;32m--> 451\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_condition\u001b[39m.\u001b[39mwait(timeout)\n\u001b[1;32m    453\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_state \u001b[39min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n\u001b[1;32m    454\u001b[0m     \u001b[39mraise\u001b[39;00m CancelledError()\n","File \u001b[0;32m~/anaconda3/lib/python3.11/threading.py:320\u001b[0m, in \u001b[0;36mCondition.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    318\u001b[0m \u001b[39mtry\u001b[39;00m:    \u001b[39m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[39;00m\n\u001b[1;32m    319\u001b[0m     \u001b[39mif\u001b[39;00m timeout \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 320\u001b[0m         waiter\u001b[39m.\u001b[39macquire()\n\u001b[1;32m    321\u001b[0m         gotit \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m    322\u001b[0m     \u001b[39melse\u001b[39;00m:\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]},{"data":{"image/png":"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","text/plain":["<Figure size 800x550 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["%%time\n","# Affichage de la courbe de validation pour le paramètre 'C'\n","param_range_C = np.logspace(-3, 2, 6)  # Valeurs pour C\n","plot_validation_curve(svc_pipe_linear, X_train, y_train, param_name='C', param_range=param_range_C, model_name=\"SVC avec noyau linéaire\")\n","\n","# Affichage de la courbe de validation pour le paramètre 'gamma'\n","param_range_gamma = np.logspace(-4, 1, 6)  # Valeurs pour gamma\n","plot_validation_curve(svc_pipe_rbf, X_train, y_train, param_name='gamma', param_range=param_range_gamma, model_name=\"SVC avec noyau RBF\")\n","\n","# Affichage de la courbe de validation pour le paramètre 'degree'\n","param_range_degree = [2, 3, 4, 5, 6]  # Valeurs pour degree\n","plot_validation_curve(svc_pipe_poly, X_train, y_train, param_name='degree', param_range=param_range_degree, model_name=\"SVC avec noyau polynomial\")"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["import optuna\n","\n","def hyperoptimize_svc_model(pipeline, X_train, y_train, X_test, y_test, kernel='linear', n_trials=5, timeout=600):\n","    # Définissez la fonction objective pour Optuna\n","    def objective(trial):\n","        if kernel == 'linear':\n","            C = trial.suggest_loguniform('svc__C', 1e-3, 1e1)\n","            pipeline.set_params(svc__C=C)\n","        elif kernel == 'rbf':\n","            C = trial.suggest_loguniform('svc__C', 1e-3, 1e1)\n","            gamma = trial.suggest_loguniform('svc__gamma', 1e-4, 1e1)\n","            pipeline.set_params(svc__C=C, svc__gamma=gamma)\n","        elif kernel == 'poly':\n","            C = trial.suggest_loguniform('svc__C', 1e-3, 1e1)\n","            degree = trial.suggest_int('svc__degree', 2, 5)\n","            pipeline.set_params(svc__C=C, svc__degree=degree)\n","        else:\n","            raise ValueError(f\"Unsupported kernel type: {kernel}\")\n","\n","        # Apply the preprocessing steps except the last step\n","        X_train_transformed = pipeline[:-1].fit_transform(X_train, y_train)\n","        score = cross_val_score(pipeline.named_steps['svc'], X_train_transformed, y_train, cv=3, scoring='accuracy').mean()\n","        return score\n","\n","    # Créez un objet study et optimisez la fonction objective\n","    study = optuna.create_study(direction='maximize')\n","    study.optimize(objective, n_trials=n_trials, timeout=timeout)\n","\n","    # Affichez les meilleurs hyperparamètres\n","    print('Best parameters:', study.best_params)\n","\n","    # Entraînez le modèle avec les meilleurs hyperparamètres\n","    pipeline.set_params(**study.best_params)\n","    pipeline.fit(X_train, y_train)\n","\n","    # Prédiction sur le jeu de test\n","    y_test_pred = pipeline.predict(X_test)\n","\n","    # Évaluation du modèle\n","    accuracy = accuracy_score(y_test, y_test_pred)\n","    f1 = f1_score(y_test, y_test_pred, average='weighted')\n","    print('Accuracy on test set:', accuracy)\n","    print('F1 score on test set:', f1)\n","\n","    # Matrice de confusion\n","    cm = confusion_matrix(y_test, y_test_pred)\n","    disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n","    disp.plot()\n","    plt.title(f\"Confusion Matrix for SVC with {kernel} kernel\")\n","    plt.show()\n","\n","    # Rapport de classification\n","    print(classification_report(y_test, y_test_pred))\n","\n","    return study.best_params, accuracy, f1\n"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["# Hyper-optimisation pour un noyau linéaire\n","best_params_linear, accuracy_linear, f1_linear = hyperoptimize_svc_model(\n","    svc_pipe_linear, X_train, y_train, X_test, y_test, kernel='linear', n_trials=5, timeout=600)"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["# Hyper-optimisation pour un noyau RBF\n","best_params_rbf, accuracy_rbf, f1_rbf = hyperoptimize_svc_model(\n","    svc_pipe_rbf, X_train, y_train, X_test, y_test, kernel='rbf', n_trials=5, timeout=600)"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["# Hyper-optimisation pour un noyau polynomial\n","best_params_poly, accuracy_poly, f1_poly = hyperoptimize_svc_model(\n","    svc_pipe_poly, X_train, y_train, X_test, y_test, kernel='poly', n_trials=5, timeout=600)"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["Après optimisation, le gain est faible avec un <b><font color='red'>score à 69.7% </font></b>"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## Apprentissage par ensemble - Pipeline"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["# Création du pipeline RandomForest\n","rf_pipeline = create_model_pipeline(\n","    cat_features, cont_features, RandomForestClassifier, n_estimators=300, max_depth=10, random_state=42, class_weight='balanced')\n","\n","# Création du pipeline GradientBoosting\n","gb_pipeline = create_model_pipeline(\n","    cat_features, cont_features, GradientBoostingClassifier, n_estimators=300, learning_rate=0.1, max_depth=3)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["%%time\n","train_and_evaluate_model(rf_pipeline, X_train, y_train, X_test, y_test, model_name='RandomForest')\n","train_and_evaluate_model(gb_pipeline, X_train, y_train, X_test, y_test, model_name='GradientBoosting')"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["# Affichage de la courbe d'apprentissage\n","plot_learning_curve(rf_pipeline, X_train, y_train, model_name=\"RandomForest\")\n","plot_learning_curve(gb_pipeline, X_train, y_train, model_name=\"GradientBoosting\")\n"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["# Affichage de la courbe de validation pour le paramètre 'n_estimators'\n","param_range_n_estimators = np.arange(50, 1501, 50)  # Valeurs pour n_estimators\n","plot_validation_curve(rf_pipeline, X_train, y_train, param_name='n_estimators', param_range=param_range_n_estimators, model_name=\"RandomForest\")\n","\n","# Affichage de la courbe de validation pour le paramètre 'n_estimators'\n","param_range_n_estimators = np.arange(50, 1501, 50)  # Valeurs pour n_estimators\n","plot_validation_curve(gb_pipeline, X_train, y_train, param_name='n_estimators', param_range=param_range_n_estimators, model_name=\"GradientBoosting\")\n"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["import optuna\n","from sklearn.ensemble import GradientBoostingClassifier\n","from sklearn.model_selection import cross_val_score\n","from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, ConfusionMatrixDisplay, classification_report\n","import matplotlib.pyplot as plt\n","\n","def hyperoptimize_gb_model(pipeline, X_train, y_train, X_test, y_test, n_trials=5, timeout=600):\n","    # Définissez la fonction objective pour Optuna\n","    def objective(trial):\n","        n_estimators = trial.suggest_int('gradientboostingclassifier__n_estimators',5, 300)\n","        learning_rate = trial.suggest_loguniform('gradientboostingclassifier__learning_rate', 0.01, 0.3)\n","        max_depth = trial.suggest_int('gradientboostingclassifier__max_depth', 2, 32)\n","        min_samples_split = trial.suggest_int('gradientboostingclassifier__min_samples_split', 2, 10)\n","        min_samples_leaf = trial.suggest_int('gradientboostingclassifier__min_samples_leaf', 1, 10)\n","        \n","        pipeline.set_params(\n","            gradientboostingclassifier__n_estimators=n_estimators,\n","            gradientboostingclassifier__learning_rate=learning_rate,\n","            gradientboostingclassifier__max_depth=max_depth,\n","            gradientboostingclassifier__min_samples_split=min_samples_split,\n","            gradientboostingclassifier__min_samples_leaf=min_samples_leaf\n","        )\n","\n","        # Apply the preprocessing steps except the last step\n","        X_train_transformed = pipeline[:-1].fit_transform(X_train, y_train)\n","        score = cross_val_score(pipeline.named_steps['gradientboostingclassifier'], X_train_transformed, y_train, cv=3, scoring='accuracy').mean()\n","        return score\n","\n","    # Créez un objet study et optimisez la fonction objective\n","    study = optuna.create_study(direction='maximize')\n","    study.optimize(objective, n_trials=n_trials, timeout=timeout)\n","\n","    # Affichez les meilleurs hyperparamètres\n","    print('Best parameters:', study.best_params)\n","\n","    # Entraînez le modèle avec les meilleurs hyperparamètres\n","    pipeline.set_params(**study.best_params)\n","    pipeline.fit(X_train, y_train)\n","\n","    # Prédiction sur le jeu de test\n","    y_test_pred = pipeline.predict(X_test)\n","\n","    # Évaluation du modèle\n","    accuracy = accuracy_score(y_test, y_test_pred)\n","    f1 = f1_score(y_test, y_test_pred, average='weighted')\n","    print('Accuracy on test set:', accuracy)\n","    print('F1 score on test set:', f1)\n","\n","    # Matrice de confusion\n","    cm = confusion_matrix(y_test, y_test_pred)\n","    disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n","    fig, ax = plt.subplots(figsize=(10, 10))\n","    disp.plot(ax=ax, values_format='d')  # Utiliser le format 'd' pour afficher les nombres entiers\n","    plt.title(\"Confusion Matrix for GradientBoostingClassifier\")\n","    plt.show()\n","\n","\n","    # Rapport de classification\n","    print(classification_report(y_test, y_test_pred))\n","\n","    return study.best_params, accuracy, f1\n"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["%%time\n","# Hyper-optimisation pour GradientBoosting\n","best_params_gb, accuracy_gb, f1_gb = hyperoptimize_gb_model(\n","    gb_pipeline, X_train, y_train, X_test, y_test, n_trials=5, timeout=600)"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["import optuna\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.model_selection import cross_val_score\n","from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, ConfusionMatrixDisplay, classification_report\n","import matplotlib.pyplot as plt\n","\n","def hyperoptimize_rf_model(pipeline, X_train, y_train, X_test, y_test, n_trials=5, timeout=600):\n","    # Définissez la fonction objective pour Optuna\n","    def objective(trial):\n","        n_estimators = trial.suggest_int('randomforestclassifier__n_estimators', 50, 300)\n","        max_depth = trial.suggest_int('randomforestclassifier__max_depth', 2, 32)\n","        min_samples_split = trial.suggest_int('randomforestclassifier__min_samples_split', 2, 10)\n","        min_samples_leaf = trial.suggest_int('randomforestclassifier__min_samples_leaf', 1, 10)\n","        \n","        pipeline.set_params(\n","            randomforestclassifier__n_estimators=n_estimators,\n","            randomforestclassifier__max_depth=max_depth,\n","            randomforestclassifier__min_samples_split=min_samples_split,\n","            randomforestclassifier__min_samples_leaf=min_samples_leaf\n","        )\n","\n","        # Apply the preprocessing steps except the last step\n","        X_train_transformed = pipeline[:-1].fit_transform(X_train, y_train)\n","        score = cross_val_score(pipeline.named_steps['randomforestclassifier'], X_train_transformed, y_train, cv=3, scoring='accuracy').mean()\n","        return score\n","\n","    # Créez un objet study et optimisez la fonction objective\n","    study = optuna.create_study(direction='maximize')\n","    study.optimize(objective, n_trials=n_trials, timeout=timeout)\n","\n","    # Affichez les meilleurs hyperparamètres\n","    print('Best parameters:', study.best_params)\n","\n","    # Entraînez le modèle avec les meilleurs hyperparamètres\n","    pipeline.set_params(**study.best_params)\n","    pipeline.fit(X_train, y_train)\n","\n","    # Prédiction sur le jeu de test\n","    y_test_pred = pipeline.predict(X_test)\n","\n","    # Évaluation du modèle\n","    accuracy = accuracy_score(y_test, y_test_pred)\n","    f1 = f1_score(y_test, y_test_pred, average='weighted')\n","    print('Accuracy on test set:', accuracy)\n","    print('F1 score on test set:', f1)\n","\n","    # Matrice de confusion\n","    cm = confusion_matrix(y_test, y_test_pred)\n","    disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n","    fig, ax = plt.subplots(figsize=(10, 10))\n","    disp.plot(ax=ax, values_format='d')  # Utiliser le format 'd' pour afficher les nombres entiers\n","    plt.title(\"Confusion Matrix for RandomForestClassifier\")\n","    plt.show()\n","\n","    # Rapport de classification\n","    print(classification_report(y_test, y_test_pred))\n","\n","    return study.best_params, accuracy, f1\n"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["%%time\n","# Hyper-optimisation pour RandomForest\n","best_params_rf, accuracy_rf, f1_rf = hyperoptimize_rf_model(\n","    rf_pipeline, X_train, y_train, X_test, y_test, n_trials=5, timeout=600)"]}],"metadata":{"kaggle":{"accelerator":"none","dataSources":[{"datasetId":3679617,"sourceId":6386941,"sourceType":"datasetVersion"}],"dockerImageVersionId":30732,"isGpuEnabled":false,"isInternetEnabled":false,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.5"}},"nbformat":4,"nbformat_minor":4}