ещё коммит
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lab_4/lab4.ipynb
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lab_4/lab4.ipynb
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"cell_type": "code",
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"execution_count": 37,
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"text/plain": [
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" prepocessing_num__Networth prepocessing_cat__Country_Argentina \\\n",
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"1909 -0.309917 0.0 \n",
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"... ... ... \n",
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"84 1.342637 0.0 \n",
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"2178 -0.329245 0.0 \n",
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"415 0.134630 0.0 \n",
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" prepocessing_cat__Country_Australia prepocessing_cat__Country_Austria \\\n",
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" prepocessing_cat__Country_Barbados prepocessing_cat__Country_Belgium \\\n",
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||||
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" prepocessing_cat__Country_Belize prepocessing_cat__Country_Brazil \\\n",
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||||
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||||
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|
||||
"415 ... 0.0 \n",
|
||||
"\n",
|
||||
" prepocessing_cat__Industry_Manufacturing \\\n",
|
||||
"1909 0.0 \n",
|
||||
"2099 0.0 \n",
|
||||
"1392 0.0 \n",
|
||||
"627 0.0 \n",
|
||||
"527 1.0 \n",
|
||||
"... ... \n",
|
||||
"84 0.0 \n",
|
||||
"633 0.0 \n",
|
||||
"922 0.0 \n",
|
||||
"2178 0.0 \n",
|
||||
"415 0.0 \n",
|
||||
"\n",
|
||||
" prepocessing_cat__Industry_Media & Entertainment \\\n",
|
||||
"1909 0.0 \n",
|
||||
"2099 0.0 \n",
|
||||
"1392 0.0 \n",
|
||||
"627 0.0 \n",
|
||||
"527 0.0 \n",
|
||||
"... ... \n",
|
||||
"84 0.0 \n",
|
||||
"633 0.0 \n",
|
||||
"922 0.0 \n",
|
||||
"2178 0.0 \n",
|
||||
"415 0.0 \n",
|
||||
"\n",
|
||||
" prepocessing_cat__Industry_Metals & Mining \\\n",
|
||||
"1909 0.0 \n",
|
||||
"2099 0.0 \n",
|
||||
"1392 0.0 \n",
|
||||
"627 0.0 \n",
|
||||
"527 0.0 \n",
|
||||
"... ... \n",
|
||||
"84 0.0 \n",
|
||||
"633 0.0 \n",
|
||||
"922 0.0 \n",
|
||||
"2178 0.0 \n",
|
||||
"415 0.0 \n",
|
||||
"\n",
|
||||
" prepocessing_cat__Industry_Real Estate \\\n",
|
||||
"1909 0.0 \n",
|
||||
"2099 1.0 \n",
|
||||
"1392 0.0 \n",
|
||||
"627 0.0 \n",
|
||||
"527 0.0 \n",
|
||||
"... ... \n",
|
||||
"84 0.0 \n",
|
||||
"633 0.0 \n",
|
||||
"922 1.0 \n",
|
||||
"2178 0.0 \n",
|
||||
"415 1.0 \n",
|
||||
"\n",
|
||||
" prepocessing_cat__Industry_Service prepocessing_cat__Industry_Sports \\\n",
|
||||
"1909 0.0 0.0 \n",
|
||||
"2099 0.0 0.0 \n",
|
||||
"1392 0.0 0.0 \n",
|
||||
"627 0.0 0.0 \n",
|
||||
"527 0.0 0.0 \n",
|
||||
"... ... ... \n",
|
||||
"84 0.0 0.0 \n",
|
||||
"633 0.0 0.0 \n",
|
||||
"922 0.0 0.0 \n",
|
||||
"2178 0.0 0.0 \n",
|
||||
"415 0.0 0.0 \n",
|
||||
"\n",
|
||||
" prepocessing_cat__Industry_Technology \\\n",
|
||||
"1909 0.0 \n",
|
||||
"2099 0.0 \n",
|
||||
"1392 0.0 \n",
|
||||
"627 0.0 \n",
|
||||
"527 0.0 \n",
|
||||
"... ... \n",
|
||||
"84 0.0 \n",
|
||||
"633 0.0 \n",
|
||||
"922 0.0 \n",
|
||||
"2178 0.0 \n",
|
||||
"415 0.0 \n",
|
||||
"\n",
|
||||
" prepocessing_cat__Industry_Telecom \\\n",
|
||||
"1909 0.0 \n",
|
||||
"2099 0.0 \n",
|
||||
"1392 0.0 \n",
|
||||
"627 0.0 \n",
|
||||
"527 0.0 \n",
|
||||
"... ... \n",
|
||||
"84 0.0 \n",
|
||||
"633 0.0 \n",
|
||||
"922 0.0 \n",
|
||||
"2178 0.0 \n",
|
||||
"415 0.0 \n",
|
||||
"\n",
|
||||
" prepocessing_cat__Industry_diversified \n",
|
||||
"1909 0.0 \n",
|
||||
"2099 0.0 \n",
|
||||
"1392 0.0 \n",
|
||||
"627 0.0 \n",
|
||||
"527 0.0 \n",
|
||||
"... ... \n",
|
||||
"84 0.0 \n",
|
||||
"633 0.0 \n",
|
||||
"922 0.0 \n",
|
||||
"2178 0.0 \n",
|
||||
"415 0.0 \n",
|
||||
"\n",
|
||||
"[2080 rows x 860 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"\n",
|
||||
"from sklearn.compose import ColumnTransformer\n",
|
||||
"from sklearn.discriminant_analysis import StandardScaler\n",
|
||||
"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
|
||||
"from sklearn.impute import SimpleImputer\n",
|
||||
"from sklearn.pipeline import Pipeline\n",
|
||||
"from sklearn.preprocessing import OneHotEncoder\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"# Исправляем ColumnTransformer с сохранением имен колонок\n",
|
||||
"columns_to_drop = [\"Age_category\", \"Rank \", \"Name\"]\n",
|
||||
"\n",
|
||||
"columns_to_drop = [\"Rank \", \"Name\"]\n",
|
||||
"num_columns = [\n",
|
||||
" column\n",
|
||||
" for column in df.columns\n",
|
||||
" if column not in columns_to_drop and df[column].dtype != \"object\"\n",
|
||||
" for column in X_train.columns\n",
|
||||
" if column not in columns_to_drop and X_train[column].dtype != \"object\"\n",
|
||||
"]\n",
|
||||
"cat_columns = [\n",
|
||||
" column\n",
|
||||
" for column in df.columns\n",
|
||||
" if column not in columns_to_drop and df[column].dtype == \"object\"\n",
|
||||
" for column in X_train.columns\n",
|
||||
" if column not in columns_to_drop and X_train[column].dtype == \"object\"\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"# Предобработка числовых данных\n",
|
||||
"num_imputer = SimpleImputer(strategy=\"median\")\n",
|
||||
"num_scaler = StandardScaler()\n",
|
||||
"preprocessing_num = Pipeline(\n",
|
||||
@ -772,6 +1279,7 @@
|
||||
" ]\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# Предобработка категориальных данных\n",
|
||||
"cat_imputer = SimpleImputer(strategy=\"constant\", fill_value=\"unknown\")\n",
|
||||
"cat_encoder = OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False, drop=\"first\")\n",
|
||||
"preprocessing_cat = Pipeline(\n",
|
||||
@ -781,76 +1289,182 @@
|
||||
" ]\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# Общая предобработка признаков\n",
|
||||
"features_preprocessing = ColumnTransformer(\n",
|
||||
" verbose_feature_names_out=False,\n",
|
||||
" verbose_feature_names_out=True, # Сохраняем имена колонок\n",
|
||||
" transformers=[\n",
|
||||
" (\"prepocessing_num\", preprocessing_num, num_columns),\n",
|
||||
" (\"prepocessing_cat\", preprocessing_cat, cat_columns),\n",
|
||||
" (\"prepocessing_features\", cat_imputer),\n",
|
||||
" ],\n",
|
||||
" remainder=\"passthrough\"\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"drop_columns = ColumnTransformer(\n",
|
||||
" verbose_feature_names_out=False,\n",
|
||||
" transformers=[\n",
|
||||
" (\"drop_columns\", \"drop\", columns_to_drop),\n",
|
||||
" ],\n",
|
||||
" remainder=\"passthrough\",\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"features_postprocessing = ColumnTransformer(\n",
|
||||
" verbose_feature_names_out=False,\n",
|
||||
" transformers=[\n",
|
||||
" (\"prepocessing_cat\", preprocessing_cat, [\"Cabin_type\"]),\n",
|
||||
" ],\n",
|
||||
" remainder=\"passthrough\",\n",
|
||||
" remainder=\"drop\" # Убираем неиспользуемые столбцы\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# Итоговый конвейер\n",
|
||||
"pipeline_end = Pipeline(\n",
|
||||
" [\n",
|
||||
" (\"features_preprocessing\", features_preprocessing),\n",
|
||||
" (\"drop_columns\", drop_columns),\n",
|
||||
" (\"features_postprocessing\", features_postprocessing),\n",
|
||||
" ]\n",
|
||||
")"
|
||||
")\n",
|
||||
"\n",
|
||||
"# Преобразуем данные\n",
|
||||
"preprocessing_result = pipeline_end.fit_transform(X_train)\n",
|
||||
"\n",
|
||||
"# Создаем DataFrame с правильными именами колонок\n",
|
||||
"preprocessed_df = pd.DataFrame(\n",
|
||||
" preprocessing_result,\n",
|
||||
" columns=pipeline_end.get_feature_names_out(),\n",
|
||||
" index=X_train.index, # Сохраняем индексы\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"preprocessed_df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Формирование набора моделей для классификации\n",
|
||||
"## logistic -- логистическая регрессия\n",
|
||||
"## ridge -- гребневая регрессия\n",
|
||||
"## decision_tree -- дерево решений\n",
|
||||
"## knn -- k-ближайших соседей\n",
|
||||
"## naive_bayes -- наивный Байесовский классификатор\n",
|
||||
"## gradient_boosting -- метод градиентного бустинга (набор деревьев решений)\n",
|
||||
"## random_forest -- метод случайного леса (набор деревьев решений)\n",
|
||||
"## mlp -- многослойный персептрон (нейронная сеть)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"execution_count": 38,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn import ensemble, linear_model, naive_bayes, neighbors, neural_network, tree\n",
|
||||
"\n",
|
||||
"class_models = {\n",
|
||||
" \"logistic\": {\"model\": linear_model.LogisticRegression()},\n",
|
||||
" # \"ridge\": {\"model\": linear_model.RidgeClassifierCV(cv=5, class_weight=\"balanced\")},\n",
|
||||
" \"ridge\": {\"model\": linear_model.LogisticRegression(penalty=\"l2\", class_weight=\"balanced\")},\n",
|
||||
" \"decision_tree\": {\n",
|
||||
" \"model\": tree.DecisionTreeClassifier(max_depth=7, random_state=9)\n",
|
||||
" },\n",
|
||||
" \"knn\": {\"model\": neighbors.KNeighborsClassifier(n_neighbors=7)},\n",
|
||||
" \"naive_bayes\": {\"model\": naive_bayes.GaussianNB()},\n",
|
||||
" \"gradient_boosting\": {\n",
|
||||
" \"model\": ensemble.GradientBoostingClassifier(n_estimators=210)\n",
|
||||
" },\n",
|
||||
" \"random_forest\": {\n",
|
||||
" \"model\": ensemble.RandomForestClassifier(\n",
|
||||
" max_depth=11, class_weight=\"balanced\", random_state=9\n",
|
||||
" )\n",
|
||||
" },\n",
|
||||
" \"mlp\": {\n",
|
||||
" \"model\": neural_network.MLPClassifier(\n",
|
||||
" hidden_layer_sizes=(7,),\n",
|
||||
" max_iter=500,\n",
|
||||
" early_stopping=True,\n",
|
||||
" random_state=9,\n",
|
||||
" )\n",
|
||||
" },\n",
|
||||
"}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Обучение моделей на обучающем наборе данных и оценка на тестовом"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 40,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Model: logistic\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
|
||||
" warnings.warn(\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "ValueError",
|
||||
"evalue": "not enough values to unpack (expected 3, got 2)",
|
||||
"evalue": "Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[1;32mIn[31], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m preprocessing_result \u001b[38;5;241m=\u001b[39m \u001b[43mpipeline_end\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2\u001b[0m preprocessed_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(\n\u001b[0;32m 3\u001b[0m preprocessing_result,\n\u001b[0;32m 4\u001b[0m columns\u001b[38;5;241m=\u001b[39mpipeline_end\u001b[38;5;241m.\u001b[39mget_feature_names_out(),\n\u001b[0;32m 5\u001b[0m )\n\u001b[0;32m 7\u001b[0m preprocessed_df\n",
|
||||
"File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\base.py:1473\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1466\u001b[0m estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m 1468\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m 1469\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m 1470\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m 1471\u001b[0m )\n\u001b[0;32m 1472\u001b[0m ):\n\u001b[1;32m-> 1473\u001b[0m \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[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\pipeline.py:533\u001b[0m, in \u001b[0;36mPipeline.fit_transform\u001b[1;34m(self, X, y, **params)\u001b[0m\n\u001b[0;32m 490\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Fit the model and transform with the final estimator.\u001b[39;00m\n\u001b[0;32m 491\u001b[0m \n\u001b[0;32m 492\u001b[0m \u001b[38;5;124;03mFit all the transformers one after the other and sequentially transform\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 530\u001b[0m \u001b[38;5;124;03m Transformed samples.\u001b[39;00m\n\u001b[0;32m 531\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 532\u001b[0m routed_params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_method_params(method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfit_transform\u001b[39m\u001b[38;5;124m\"\u001b[39m, props\u001b[38;5;241m=\u001b[39mparams)\n\u001b[1;32m--> 533\u001b[0m Xt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrouted_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 535\u001b[0m last_step \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_final_estimator\n\u001b[0;32m 536\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _print_elapsed_time(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPipeline\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_log_message(\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m)):\n",
|
||||
"File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\pipeline.py:406\u001b[0m, in \u001b[0;36mPipeline._fit\u001b[1;34m(self, X, y, routed_params)\u001b[0m\n\u001b[0;32m 404\u001b[0m cloned_transformer \u001b[38;5;241m=\u001b[39m clone(transformer)\n\u001b[0;32m 405\u001b[0m \u001b[38;5;66;03m# Fit or load from cache the current transformer\u001b[39;00m\n\u001b[1;32m--> 406\u001b[0m X, fitted_transformer \u001b[38;5;241m=\u001b[39m \u001b[43mfit_transform_one_cached\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 407\u001b[0m \u001b[43m \u001b[49m\u001b[43mcloned_transformer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 408\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 409\u001b[0m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 410\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 411\u001b[0m \u001b[43m \u001b[49m\u001b[43mmessage_clsname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPipeline\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 412\u001b[0m \u001b[43m \u001b[49m\u001b[43mmessage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_log_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep_idx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 413\u001b[0m \u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrouted_params\u001b[49m\u001b[43m[\u001b[49m\u001b[43mname\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 414\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 415\u001b[0m \u001b[38;5;66;03m# Replace the transformer of the step with the fitted\u001b[39;00m\n\u001b[0;32m 416\u001b[0m \u001b[38;5;66;03m# transformer. This is necessary when loading the transformer\u001b[39;00m\n\u001b[0;32m 417\u001b[0m \u001b[38;5;66;03m# from the cache.\u001b[39;00m\n\u001b[0;32m 418\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps[step_idx] \u001b[38;5;241m=\u001b[39m (name, fitted_transformer)\n",
|
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"File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\joblib\\memory.py:312\u001b[0m, in \u001b[0;36mNotMemorizedFunc.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 311\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m--> 312\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\pipeline.py:1310\u001b[0m, in \u001b[0;36m_fit_transform_one\u001b[1;34m(transformer, X, y, weight, message_clsname, message, params)\u001b[0m\n\u001b[0;32m 1308\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _print_elapsed_time(message_clsname, message):\n\u001b[0;32m 1309\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(transformer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfit_transform\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m-> 1310\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mtransformer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfit_transform\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1311\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1312\u001b[0m res \u001b[38;5;241m=\u001b[39m transformer\u001b[38;5;241m.\u001b[39mfit(X, y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfit\u001b[39m\u001b[38;5;124m\"\u001b[39m, {}))\u001b[38;5;241m.\u001b[39mtransform(\n\u001b[0;32m 1313\u001b[0m X, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtransform\u001b[39m\u001b[38;5;124m\"\u001b[39m, {})\n\u001b[0;32m 1314\u001b[0m )\n",
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"File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\utils\\_set_output.py:316\u001b[0m, in \u001b[0;36m_wrap_method_output.<locals>.wrapped\u001b[1;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[0;32m 314\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[0;32m 315\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m--> 316\u001b[0m data_to_wrap \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 317\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_to_wrap, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m 318\u001b[0m \u001b[38;5;66;03m# only wrap the first output for cross decomposition\u001b[39;00m\n\u001b[0;32m 319\u001b[0m return_tuple \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 320\u001b[0m _wrap_data_with_container(method, data_to_wrap[\u001b[38;5;241m0\u001b[39m], X, \u001b[38;5;28mself\u001b[39m),\n\u001b[0;32m 321\u001b[0m \u001b[38;5;241m*\u001b[39mdata_to_wrap[\u001b[38;5;241m1\u001b[39m:],\n\u001b[0;32m 322\u001b[0m )\n",
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"File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\base.py:1473\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1466\u001b[0m estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m 1468\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m 1469\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m 1470\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m 1471\u001b[0m )\n\u001b[0;32m 1472\u001b[0m ):\n\u001b[1;32m-> 1473\u001b[0m \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[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\compose\\_column_transformer.py:965\u001b[0m, in \u001b[0;36mColumnTransformer.fit_transform\u001b[1;34m(self, X, y, **params)\u001b[0m\n\u001b[0;32m 963\u001b[0m \u001b[38;5;66;03m# set n_features_in_ attribute\u001b[39;00m\n\u001b[0;32m 964\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_n_features(X, reset\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m--> 965\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_transformers\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 966\u001b[0m n_samples \u001b[38;5;241m=\u001b[39m _num_samples(X)\n\u001b[0;32m 968\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_column_callables(X)\n",
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||||
"File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\compose\\_column_transformer.py:501\u001b[0m, in \u001b[0;36mColumnTransformer._validate_transformers\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransformers:\n\u001b[0;32m 499\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m--> 501\u001b[0m names, transformers, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransformers)\n\u001b[0;32m 503\u001b[0m \u001b[38;5;66;03m# validate names\u001b[39;00m\n\u001b[0;32m 504\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_names(names)\n",
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"\u001b[1;31mValueError\u001b[0m: not enough values to unpack (expected 3, got 2)"
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"Cell \u001b[1;32mIn[40], line 19\u001b[0m\n\u001b[0;32m 16\u001b[0m class_models[model_name][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprobs\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m y_test_probs\n\u001b[0;32m 17\u001b[0m class_models[model_name][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpreds\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m y_test_predict\n\u001b[1;32m---> 19\u001b[0m class_models[model_name][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPrecision_train\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mmetrics\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprecision_score\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 20\u001b[0m \u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train_predict\u001b[49m\n\u001b[0;32m 21\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 22\u001b[0m class_models[model_name][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPrecision_test\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m metrics\u001b[38;5;241m.\u001b[39mprecision_score(\n\u001b[0;32m 23\u001b[0m y_test, y_test_predict\n\u001b[0;32m 24\u001b[0m )\n\u001b[0;32m 25\u001b[0m class_models[model_name][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRecall_train\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m metrics\u001b[38;5;241m.\u001b[39mrecall_score(\n\u001b[0;32m 26\u001b[0m y_train, y_train_predict\n\u001b[0;32m 27\u001b[0m )\n",
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||||
"File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:213\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 208\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m 209\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m 210\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m 211\u001b[0m )\n\u001b[0;32m 212\u001b[0m ):\n\u001b[1;32m--> 213\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 215\u001b[0m \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[0;32m 216\u001b[0m \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[0;32m 217\u001b[0m \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[0;32m 218\u001b[0m \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[0;32m 219\u001b[0m msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[0;32m 220\u001b[0m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 221\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 222\u001b[0m \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[0;32m 223\u001b[0m )\n",
|
||||
"File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:2204\u001b[0m, in \u001b[0;36mprecision_score\u001b[1;34m(y_true, y_pred, labels, pos_label, average, sample_weight, zero_division)\u001b[0m\n\u001b[0;32m 2037\u001b[0m \u001b[38;5;129m@validate_params\u001b[39m(\n\u001b[0;32m 2038\u001b[0m {\n\u001b[0;32m 2039\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my_true\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124marray-like\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msparse matrix\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 2064\u001b[0m zero_division\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwarn\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 2065\u001b[0m ):\n\u001b[0;32m 2066\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Compute the precision.\u001b[39;00m\n\u001b[0;32m 2067\u001b[0m \n\u001b[0;32m 2068\u001b[0m \u001b[38;5;124;03m The precision is the ratio ``tp / (tp + fp)`` where ``tp`` is the number of\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 2202\u001b[0m \u001b[38;5;124;03m array([0.5, 1. , 1. ])\u001b[39;00m\n\u001b[0;32m 2203\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 2204\u001b[0m p, _, _, _ \u001b[38;5;241m=\u001b[39m \u001b[43mprecision_recall_fscore_support\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 2205\u001b[0m \u001b[43m \u001b[49m\u001b[43my_true\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2206\u001b[0m \u001b[43m \u001b[49m\u001b[43my_pred\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2207\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2208\u001b[0m \u001b[43m \u001b[49m\u001b[43mpos_label\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpos_label\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2209\u001b[0m \u001b[43m \u001b[49m\u001b[43maverage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maverage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2210\u001b[0m \u001b[43m \u001b[49m\u001b[43mwarn_for\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mprecision\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2211\u001b[0m \u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2212\u001b[0m \u001b[43m \u001b[49m\u001b[43mzero_division\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mzero_division\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2213\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2214\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m p\n",
|
||||
"File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:186\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 184\u001b[0m global_skip_validation \u001b[38;5;241m=\u001b[39m get_config()[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskip_parameter_validation\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 185\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m global_skip_validation:\n\u001b[1;32m--> 186\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 188\u001b[0m func_sig \u001b[38;5;241m=\u001b[39m signature(func)\n\u001b[0;32m 190\u001b[0m \u001b[38;5;66;03m# Map *args/**kwargs to the function signature\u001b[39;00m\n",
|
||||
"File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1789\u001b[0m, in \u001b[0;36mprecision_recall_fscore_support\u001b[1;34m(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight, zero_division)\u001b[0m\n\u001b[0;32m 1626\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Compute precision, recall, F-measure and support for each class.\u001b[39;00m\n\u001b[0;32m 1627\u001b[0m \n\u001b[0;32m 1628\u001b[0m \u001b[38;5;124;03mThe precision is the ratio ``tp / (tp + fp)`` where ``tp`` is the number of\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1786\u001b[0m \u001b[38;5;124;03m array([2, 2, 2]))\u001b[39;00m\n\u001b[0;32m 1787\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 1788\u001b[0m _check_zero_division(zero_division)\n\u001b[1;32m-> 1789\u001b[0m labels \u001b[38;5;241m=\u001b[39m \u001b[43m_check_set_wise_labels\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_true\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_pred\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maverage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpos_label\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1791\u001b[0m \u001b[38;5;66;03m# Calculate tp_sum, pred_sum, true_sum ###\u001b[39;00m\n\u001b[0;32m 1792\u001b[0m samplewise \u001b[38;5;241m=\u001b[39m average \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msamples\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
|
||||
"File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1578\u001b[0m, in \u001b[0;36m_check_set_wise_labels\u001b[1;34m(y_true, y_pred, average, labels, pos_label)\u001b[0m\n\u001b[0;32m 1576\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmulticlass\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 1577\u001b[0m average_options\u001b[38;5;241m.\u001b[39mremove(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msamples\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 1578\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 1579\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTarget is \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m but average=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbinary\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m. Please \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1580\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mchoose another average setting, one of \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (y_type, average_options)\n\u001b[0;32m 1581\u001b[0m )\n\u001b[0;32m 1582\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m pos_label \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m1\u001b[39m):\n\u001b[0;32m 1583\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m 1584\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNote that pos_label (set to \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m) is ignored when \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1585\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maverage != \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbinary\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m (got \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m). You may use \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1588\u001b[0m \u001b[38;5;167;01mUserWarning\u001b[39;00m,\n\u001b[0;32m 1589\u001b[0m )\n",
|
||||
"\u001b[1;31mValueError\u001b[0m: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted']."
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"preprocessing_result = pipeline_end.fit_transform(X_train)\n",
|
||||
"preprocessed_df = pd.DataFrame(\n",
|
||||
" preprocessing_result,\n",
|
||||
" columns=pipeline_end.get_feature_names_out(),\n",
|
||||
")\n",
|
||||
"import numpy as np\n",
|
||||
"from sklearn import metrics\n",
|
||||
"\n",
|
||||
"preprocessed_df"
|
||||
"for model_name in class_models.keys():\n",
|
||||
" print(f\"Model: {model_name}\")\n",
|
||||
" model = class_models[model_name][\"model\"]\n",
|
||||
"\n",
|
||||
" model_pipeline = Pipeline([(\"pipeline\", pipeline_end), (\"model\", model)])\n",
|
||||
" model_pipeline = model_pipeline.fit(X_train, y_train.values.ravel())\n",
|
||||
"\n",
|
||||
" y_train_predict = model_pipeline.predict(X_train)\n",
|
||||
" y_test_probs = model_pipeline.predict_proba(X_test)[:, 1]\n",
|
||||
" y_test_predict = np.where(y_test_probs > 0.5, 1, 0)\n",
|
||||
"\n",
|
||||
" class_models[model_name][\"pipeline\"] = model_pipeline\n",
|
||||
" class_models[model_name][\"probs\"] = y_test_probs\n",
|
||||
" class_models[model_name][\"preds\"] = y_test_predict\n",
|
||||
"\n",
|
||||
" class_models[model_name][\"Precision_train\"] = metrics.precision_score(\n",
|
||||
" y_train, y_train_predict\n",
|
||||
" )\n",
|
||||
" class_models[model_name][\"Precision_test\"] = metrics.precision_score(\n",
|
||||
" y_test, y_test_predict\n",
|
||||
" )\n",
|
||||
" class_models[model_name][\"Recall_train\"] = metrics.recall_score(\n",
|
||||
" y_train, y_train_predict\n",
|
||||
" )\n",
|
||||
" class_models[model_name][\"Recall_test\"] = metrics.recall_score(\n",
|
||||
" y_test, y_test_predict\n",
|
||||
" )\n",
|
||||
" class_models[model_name][\"Accuracy_train\"] = metrics.accuracy_score(\n",
|
||||
" y_train, y_train_predict\n",
|
||||
" )\n",
|
||||
" class_models[model_name][\"Accuracy_test\"] = metrics.accuracy_score(\n",
|
||||
" y_test, y_test_predict\n",
|
||||
" )\n",
|
||||
" class_models[model_name][\"ROC_AUC_test\"] = metrics.roc_auc_score(\n",
|
||||
" y_test, y_test_probs\n",
|
||||
" )\n",
|
||||
" class_models[model_name][\"F1_train\"] = metrics.f1_score(y_train, y_train_predict)\n",
|
||||
" class_models[model_name][\"F1_test\"] = metrics.f1_score(y_test, y_test_predict)\n",
|
||||
" class_models[model_name][\"MCC_test\"] = metrics.matthews_corrcoef(\n",
|
||||
" y_test, y_test_predict\n",
|
||||
" )\n",
|
||||
" class_models[model_name][\"Cohen_kappa_test\"] = metrics.cohen_kappa_score(\n",
|
||||
" y_test, y_test_predict\n",
|
||||
" )\n",
|
||||
" class_models[model_name][\"Confusion_matrix\"] = metrics.confusion_matrix(\n",
|
||||
" y_test, y_test_predict\n",
|
||||
" )"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user