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еще изменения не все залилось почему то??

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Полина Чубыкина 2024-11-09 13:27:56 +04:00
parent 760d359e84
commit beb0ee0ab8
1 changed files with 58 additions and 65 deletions
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lab_4/lab4.ipynb
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@ -586,39 +586,12 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Проведем деление на выборки и создание ориентира" "Проведем деление на выборки"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 63,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Размер обучающей выборки: (743, 16)\n",
"Размер тестовой выборки: (186, 16)\n"
]
}
],
"source": [
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"features = ['BMI', 'Smoking', 'AlcoholDrinking', 'Stroke', 'PhysicalHealth', 'MentalHealth', 'DiffWalking', 'Sex', 'AgeCategory', 'Race', 'Diabetic', 'PhysicalActivity', 'GenHealth', 'Asthma', 'KidneyDisease', 'SkinCancer']\n",
"target = 'HeartDisease'\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(data[features], data[target], test_size=0.2, random_state=42)\n",
"\n",
"print(\"Размер обучающей выборки:\", X_train.shape)\n",
"print(\"Размер тестовой выборки:\", X_test.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -630,7 +603,60 @@
"1 796\n", "1 796\n",
"Name: count, dtype: int64\n", "Name: count, dtype: int64\n",
"Размер обучающей выборки: (1273, 49)\n", "Размер обучающей выборки: (1273, 49)\n",
"Размер тестовой выборки: (319, 49)\n", "Размер тестовой выборки: (319, 49)\n"
]
}
],
"source": [
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"features = ['BMI', 'Smoking', 'AlcoholDrinking', 'Stroke', 'PhysicalHealth', 'MentalHealth', 'DiffWalking', 'Sex', 'AgeCategory', 'Race', 'Diabetic', 'PhysicalActivity', 'GenHealth', 'Asthma', 'KidneyDisease', 'SkinCancer']\n",
"target = 'HeartDisease'\n",
"\n",
"label_encoder = LabelEncoder()\n",
"data[target] = label_encoder.fit_transform(data[target])\n",
"\n",
"categorical_features = ['Smoking', 'AlcoholDrinking', 'Stroke', 'DiffWalking', 'Sex', 'AgeCategory', 'Race', 'Diabetic', 'PhysicalActivity', 'GenHealth', 'Asthma', 'KidneyDisease', 'SkinCancer']\n",
"numeric_features = ['BMI', 'PhysicalHealth', 'MentalHealth']\n",
"\n",
"categorical_transformer = Pipeline(steps=[\n",
" ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
"])\n",
"\n",
"numeric_transformer = Pipeline(steps=[\n",
" ('scaler', StandardScaler())\n",
"])\n",
"\n",
"preprocessor = ColumnTransformer(\n",
" transformers=[\n",
" ('num', numeric_transformer, numeric_features),\n",
" ('cat', categorical_transformer, categorical_features)\n",
" ])\n",
"\n",
"X = preprocessor.fit_transform(data[features])\n",
"y = data[target]\n",
"\n",
"smote = SMOTE(random_state=42)\n",
"X_resampled, y_resampled = smote.fit_resample(X, y)\n",
"\n",
"print(pd.Series(y_resampled).value_counts())\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=42)\n",
"\n",
"print(\"Размер обучающей выборки:\", X_train.shape)\n",
"print(\"Размер тестовой выборки:\", X_test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Лучшие гиперпараметры для логистической регрессии:\n", "Лучшие гиперпараметры для логистической регрессии:\n",
"{'classifier__C': np.float64(0.7272998688284025), 'classifier__penalty': 'l1', 'classifier__solver': 'liblinear'}\n", "{'classifier__C': np.float64(0.7272998688284025), 'classifier__penalty': 'l1', 'classifier__solver': 'liblinear'}\n",
"Accuracy: 0.7398\n", "Accuracy: 0.7398\n",
@ -639,14 +665,14 @@
"F1-Score: 0.7398\n", "F1-Score: 0.7398\n",
"ROC-AUC: 0.8338\n", "ROC-AUC: 0.8338\n",
"Лучшие гиперпараметры для случайного леса:\n", "Лучшие гиперпараметры для случайного леса:\n",
"{'classifier__bootstrap': True, 'classifier__max_depth': np.int64(32), 'classifier__min_samples_leaf': 1, 'classifier__min_samples_split': 6, 'classifier__n_estimators': 317}\n", "{'classifier__bootstrap': True, 'classifier__max_depth': np.int64(25), 'classifier__min_samples_leaf': 1, 'classifier__min_samples_split': 6, 'classifier__n_estimators': 317}\n",
"Accuracy: 0.9122\n", "Accuracy: 0.9122\n",
"Precision: 0.9571\n", "Precision: 0.9571\n",
"Recall: 0.8590\n", "Recall: 0.8590\n",
"F1-Score: 0.9054\n", "F1-Score: 0.9054\n",
"ROC-AUC: 0.9773\n", "ROC-AUC: 0.9773\n",
"Лучшие гиперпараметры для градиентного бустинга:\n", "Лучшие гиперпараметры для градиентного бустинга:\n",
"{'classifier__learning_rate': np.float64(0.17269984907963387), 'classifier__max_depth': np.int64(96), 'classifier__min_samples_leaf': 8, 'classifier__min_samples_split': 8, 'classifier__n_estimators': 294, 'classifier__subsample': np.float64(0.8288064461501716)}\n", "{'classifier__learning_rate': np.float64(0.17269984907963387), 'classifier__max_depth': np.int64(52), 'classifier__min_samples_leaf': 8, 'classifier__min_samples_split': 8, 'classifier__n_estimators': 294, 'classifier__subsample': np.float64(0.8288064461501716)}\n",
"Accuracy: 0.9185\n", "Accuracy: 0.9185\n",
"Precision: 0.9577\n", "Precision: 0.9577\n",
"Recall: 0.8718\n", "Recall: 0.8718\n",
@ -692,39 +718,6 @@
"from scipy.stats import uniform, randint\n", "from scipy.stats import uniform, randint\n",
"from sklearn.model_selection import RandomizedSearchCV\n", "from sklearn.model_selection import RandomizedSearchCV\n",
"\n", "\n",
"features = ['BMI', 'Smoking', 'AlcoholDrinking', 'Stroke', 'PhysicalHealth', 'MentalHealth', 'DiffWalking', 'Sex', 'AgeCategory', 'Race', 'Diabetic', 'PhysicalActivity', 'GenHealth', 'Asthma', 'KidneyDisease', 'SkinCancer']\n",
"target = 'HeartDisease'\n",
"\n",
"label_encoder = LabelEncoder()\n",
"data[target] = label_encoder.fit_transform(data[target])\n",
"\n",
"categorical_features = ['Smoking', 'AlcoholDrinking', 'Stroke', 'DiffWalking', 'Sex', 'AgeCategory', 'Race', 'Diabetic', 'PhysicalActivity', 'GenHealth', 'Asthma', 'KidneyDisease', 'SkinCancer']\n",
"numeric_features = ['BMI', 'PhysicalHealth', 'MentalHealth']\n",
"\n",
"categorical_transformer = Pipeline(steps=[\n",
" ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
"])\n",
"\n",
"numeric_transformer = Pipeline(steps=[\n",
" ('scaler', StandardScaler())\n",
"])\n",
"\n",
"preprocessor = ColumnTransformer(\n",
" transformers=[\n",
" ('num', numeric_transformer, numeric_features),\n",
" ('cat', categorical_transformer, categorical_features)\n",
" ])\n",
"\n",
"X = preprocessor.fit_transform(data[features])\n",
"y = data[target]\n",
"\n",
"smote = SMOTE(random_state=42)\n",
"X_resampled, y_resampled = smote.fit_resample(X, y)\n",
"\n",
"print(pd.Series(y_resampled).value_counts())\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=42)\n",
"\n",
"def evaluate_model(model, X_test, y_test):\n", "def evaluate_model(model, X_test, y_test):\n",
" y_pred = model.predict(X_test)\n", " y_pred = model.predict(X_test)\n",
" y_pred_proba = model.predict_proba(X_test)[:, 1]\n", " y_pred_proba = model.predict_proba(X_test)[:, 1]\n",