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lab_4 done
2024-11-09 12:39:58 +04:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Вариант 19: Данные о миллионерах"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['Rank ', 'Name', 'Networth', 'Age', 'Country', 'Source', 'Industry'], dtype='object')\n"
]
}
],
"source": [
"import pandas as pd \n",
"df = pd.read_csv(\"..//static//csv//Forbes Billionaires.csv\")\n",
"print(df.columns)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Бизнес-цели\n",
"\n",
"### Задача классификации\n",
"Классифицировать людей по уровню состояния.\n",
"\n",
"Цель:\n",
"\n",
"Разработать модель машинного обучения, которая будет классифицировать миллиардеров по состоянию, выше или ниже среднего.\n",
"\n",
"В обучении модели машинного обучения для классификации миллиардеров по уровню богатства, помимо чистого состояния, используются и другие столбцы данных:\n",
"- Возраст: Люди с высоким чистым состоянием, как правило, старше. Модель может использовать возраст как признак, чтобы прогнозировать уровень богатства.\n",
"- Страна: Богатство распределяется неравномерно по миру. Страна проживания может быть важным признаком для предсказания уровня богатства.\n",
"- Отрасль: Определенные отрасли (например, финансы, технологии) часто связаны с высоким чистым состоянием. \n",
"\n",
"### Задача регрессии:\n",
"Прогнозирование чистого состояния (Networth):\n",
"\n",
"Цель: Предсказать абсолютное значение чистого состояния миллиардера, используя информацию из имеющихся данных.\n",
"\n",
"Применение: Это может быть полезно для оценки потенциального состояния миллиардеров в будущем или для сравнения миллиардеров в разных странах и отраслях.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Определение достижимого уровня качества модели для первой задачи "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Создание целевой переменной и предварительная обработка данных"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Среднее значение поля 'Networth': 4.8607499999999995\n",
" Rank Name Networth Age Country \\\n",
"0 1 Elon Musk 219.0 50 United States \n",
"1 2 Jeff Bezos 171.0 58 United States \n",
"2 3 Bernard Arnault & family 158.0 73 France \n",
"3 4 Bill Gates 129.0 66 United States \n",
"4 5 Warren Buffett 118.0 91 United States \n",
"\n",
" Source Industry above_average_networth \n",
"0 Tesla, SpaceX Automotive 1 \n",
"1 Amazon Technology 1 \n",
"2 LVMH Fashion & Retail 1 \n",
"3 Microsoft Technology 1 \n",
"4 Berkshire Hathaway Finance & Investments 1 \n"
]
}
],
"source": [
"from sklearn import set_config\n",
"\n",
"# Установим параметры для вывода\n",
"set_config(transform_output=\"pandas\")\n",
"\n",
"# Устанавливаем случайное состояние\n",
"random_state = 42\n",
"# Можно использовать данные о above_average_networth для анализа зависимости между типом источника богатства и чистым состоянием.\n",
"# Рассчитываем среднее значение чистого состояния\n",
"average_networth = df['Networth'].mean()\n",
"print(f\"Среднее значение поля 'Networth': {average_networth}\")\n",
"\n",
"# Создаем новую переменную, указывающую, превышает ли чистое состояние среднее\n",
"df['above_average_networth'] = (df['Networth'] > average_networth).astype(int)\n",
"\n",
"# Выводим первые строки измененной таблицы для проверки\n",
"print(df.head())\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Разделение набора данных на обучающую и тестовые выборки (80/20) для задачи классификации\n",
"\n",
"Целевой признак -- above_average_networth "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"X_train shape: (2080, 8)\n",
"y_train shape: (2080, 1)\n",
"X_test shape: (520, 8)\n",
"y_test shape: (520, 1)\n",
"X_train:\n",
" Rank Name Networth Age Country Source \\\n",
"2125 2076 Yogesh Kothari 1.4 73 India specialty chemicals \n",
"1165 1163 Yvonne Bauer 2.7 45 Germany magazines, media \n",
"397 398 Juergen Blickle 6.4 75 Germany auto parts \n",
"1432 1397 Alexander Svetakov 2.2 54 Russia real estate \n",
"1024 1012 Li Min 3.0 56 China semiconductor \n",
"\n",
" Industry above_average_networth \n",
"2125 Manufacturing 0 \n",
"1165 Media & Entertainment 0 \n",
"397 Manufacturing 1 \n",
"1432 Finance & Investments 0 \n",
"1024 Technology 0 \n",
"y_train:\n",
" above_average_networth\n",
"2125 0\n",
"1165 0\n",
"397 1\n",
"1432 0\n",
"1024 0\n",
"X_test:\n",
" Rank Name Networth Age Country \\\n",
"2437 2324 Horst Wortmann 1.2 80 Germany \n",
"2118 2076 Ramesh Juneja 1.4 66 India \n",
"1327 1292 Teresita Sy-Coson 2.4 71 Philippines \n",
"2063 1929 Myron Wentz 1.5 82 St. Kitts and Nevis \n",
"1283 1238 Suh Kyung-bae 2.5 59 South Korea \n",
"\n",
" Source Industry above_average_networth \n",
"2437 footwear Fashion & Retail 0 \n",
"2118 pharmaceuticals Healthcare 0 \n",
"1327 diversified diversified 0 \n",
"2063 health products Fashion & Retail 0 \n",
"1283 cosmetics Fashion & Retail 0 \n",
"y_test:\n",
" above_average_networth\n",
"2437 0\n",
"2118 0\n",
"1327 0\n",
"2063 0\n",
"1283 0\n"
]
}
],
"source": [
"from typing import Tuple\n",
"from pandas import DataFrame\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"def split_stratified_into_train_val_test(\n",
" df_input,\n",
" stratify_colname=\"y\",\n",
" frac_train=0.6,\n",
" frac_val=0.15,\n",
" frac_test=0.25,\n",
" random_state=None,\n",
") -> Tuple[DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame]:\n",
"\n",
"\n",
" if frac_train + frac_val + frac_test != 1.0:\n",
" raise ValueError(\n",
" \"fractions %f, %f, %f do not add up to 1.0\"\n",
" % (frac_train, frac_val, frac_test)\n",
" )\n",
" if stratify_colname not in df_input.columns:\n",
" raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
" X = df_input # Contains all columns.\n",
" y = df_input[\n",
" [stratify_colname]\n",
" ] # Dataframe of just the column on which to stratify.\n",
" # Split original dataframe into train and temp dataframes.\n",
" df_train, df_temp, y_train, y_temp = train_test_split(\n",
" X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
" )\n",
" if frac_val <= 0:\n",
" assert len(df_input) == len(df_train) + len(df_temp)\n",
" return df_train, pd.DataFrame(), df_temp, y_train, pd.DataFrame(), y_temp\n",
" # Split the temp dataframe into val and test dataframes.\n",
" relative_frac_test = frac_test / (frac_val + frac_test)\n",
" df_val, df_test, y_val, y_test = train_test_split(\n",
" df_temp,\n",
" y_temp,\n",
" stratify=y_temp,\n",
" test_size=relative_frac_test,\n",
" random_state=random_state,\n",
" )\n",
" assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
" return df_train, df_val, df_test, y_train, y_val, y_test\n",
"\n",
"# Разделение набора данных на обучающую, валидационную и тестовую выборки (80/0/20)\n",
"random_state = 42 # Задайте любое целое число для воспроизводимости\n",
"X_train, X_val, X_test, y_train, y_val, y_test = split_stratified_into_train_val_test(\n",
" df, stratify_colname=\"above_average_networth\", frac_train=0.80, frac_val=0, frac_test=0.20, random_state=random_state\n",
")\n",
"\n",
"# Вывод размеров выборок\n",
"print(\"X_train shape:\", X_train.shape)\n",
"print(\"y_train shape:\", y_train.shape)\n",
"print(\"X_test shape:\", X_test.shape)\n",
"print(\"y_test shape:\", y_test.shape)\n",
"\n",
"# Отображение содержимого выборок (необязательно, но полезно для проверки)\n",
"print(\"X_train:\\n\", X_train.head())\n",
"print(\"y_train:\\n\", y_train.head())\n",
"print(\"X_test:\\n\", X_test.head())\n",
"print(\"y_test:\\n\", y_test.head())\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Формирование конвейера для классификации данных\n",
"\n",
"preprocessing_num -- конвейер для обработки числовых данных: заполнение пропущенных значений и стандартизация\n",
"\n",
"preprocessing_cat -- конвейер для обработки категориальных данных: заполнение пропущенных данных и унитарное кодирование\n",
"\n",
"features_preprocessing -- трансформер для предобработки признаков\n",
"\n",
"features_engineering -- трансформер для конструирования признаков\n",
"\n",
"drop_columns -- трансформер для удаления колонок\n",
"\n",
"pipeline_end -- основной конвейер предобработки данных и конструирования признаков"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"# Определение столбцов для обработки\n",
"columns_to_drop = [\"Name\", \"Rank \"] # Столбцы, которые можно удалить\n",
"num_columns = [\"Networth\", \"Age\"] # Числовые столбцы\n",
"cat_columns = [\"Country\", \"Source\", \"Industry\"] # Категориальные столбцы\n",
"\n",
"# Препроцессинг числовых столбцов\n",
"num_imputer = SimpleImputer(strategy=\"median\")\n",
"num_scaler = StandardScaler()\n",
"preprocessing_num = Pipeline(\n",
" [\n",
" (\"imputer\", num_imputer),\n",
" (\"scaler\", num_scaler),\n",
" ]\n",
")\n",
"\n",
"# Препроцессинг категориальных столбцов\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",
" [\n",
" (\"imputer\", cat_imputer),\n",
" (\"encoder\", cat_encoder),\n",
" ]\n",
")\n",
"\n",
"# Объединение препроцессинга\n",
"features_preprocessing = ColumnTransformer(\n",
" verbose_feature_names_out=False,\n",
" transformers=[\n",
" (\"preprocessing_num\", preprocessing_num, num_columns),\n",
" (\"preprocessing_cat\", preprocessing_cat, cat_columns),\n",
" ],\n",
" remainder=\"passthrough\"\n",
")\n",
"\n",
"# Удаление ненужных столбцов\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",
"# Создание финального пайплайна\n",
"pipeline_end = Pipeline(\n",
" [\n",
" (\"features_preprocessing\", features_preprocessing),\n",
" (\"drop_columns\", drop_columns),\n",
" ]\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Демонстрация работы конвейера__"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Networth Age Country_Argentina Country_Australia \\\n",
"2125 -0.340947 0.680013 0.0 0.0 \n",
"1165 -0.211625 -1.475070 0.0 0.0 \n",
"397 0.156447 0.833948 0.0 0.0 \n",
"1432 -0.261364 -0.782365 0.0 0.0 \n",
"1024 -0.181781 -0.628430 0.0 0.0 \n",
"\n",
" Country_Austria Country_Barbados Country_Belgium Country_Belize \\\n",
"2125 0.0 0.0 0.0 0.0 \n",
"1165 0.0 0.0 0.0 0.0 \n",
"397 0.0 0.0 0.0 0.0 \n",
"1432 0.0 0.0 0.0 0.0 \n",
"1024 0.0 0.0 0.0 0.0 \n",
"\n",
" Country_Brazil Country_Bulgaria ... Industry_Manufacturing \\\n",
"2125 0.0 0.0 ... 1.0 \n",
"1165 0.0 0.0 ... 0.0 \n",
"397 0.0 0.0 ... 1.0 \n",
"1432 0.0 0.0 ... 0.0 \n",
"1024 0.0 0.0 ... 0.0 \n",
"\n",
" Industry_Media & Entertainment Industry_Metals & Mining \\\n",
"2125 0.0 0.0 \n",
"1165 1.0 0.0 \n",
"397 0.0 0.0 \n",
"1432 0.0 0.0 \n",
"1024 0.0 0.0 \n",
"\n",
" Industry_Real Estate Industry_Service Industry_Sports \\\n",
"2125 0.0 0.0 0.0 \n",
"1165 0.0 0.0 0.0 \n",
"397 0.0 0.0 0.0 \n",
"1432 0.0 0.0 0.0 \n",
"1024 0.0 0.0 0.0 \n",
"\n",
" Industry_Technology Industry_Telecom Industry_diversified \\\n",
"2125 0.0 0.0 0.0 \n",
"1165 0.0 0.0 0.0 \n",
"397 0.0 0.0 0.0 \n",
"1432 0.0 0.0 0.0 \n",
"1024 1.0 0.0 0.0 \n",
"\n",
" above_average_networth \n",
"2125 0 \n",
"1165 0 \n",
"397 1 \n",
"1432 0 \n",
"1024 0 \n",
"\n",
"[5 rows x 859 columns]\n"
]
}
],
"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",
"\n",
"# Вывод первых строк обработанных данных\n",
"print(preprocessed_df.head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Формирование набора моделей для классификации\n",
"\n",
"logistic -- логистическая регрессия\n",
"\n",
"ridge -- гребневая регрессия\n",
"\n",
"decision_tree -- дерево решений\n",
"\n",
"knn -- k-ближайших соседей\n",
"\n",
"naive_bayes -- наивный Байесовский классификатор\n",
"\n",
"gradient_boosting -- метод градиентного бустинга (набор деревьев решений)\n",
"\n",
"random_forest -- метод случайного леса (набор деревьев решений)\n",
"\n",
"mlp -- многослойный персептрон (нейронная сеть)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"from sklearn import linear_model, tree, neighbors, naive_bayes, ensemble, neural_network\n",
"class_models = {\n",
" \"logistic\": {\"model\": linear_model.LogisticRegression()},\n",
" \"ridge\": {\"model\": linear_model.LogisticRegression(penalty=\"l2\", class_weight=\"balanced\")},\n",
" \"decision_tree\": {\n",
" \"model\": tree.DecisionTreeClassifier(max_depth=7, random_state=42)\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=42\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=42,\n",
" )\n",
" },\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Обучение моделей и оценка их качества"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: logistic\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: ridge\n",
"Model: decision_tree\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: knn\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: naive_bayes\n",
"Model: gradient_boosting\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: random_forest\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: mlp\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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"
]
}
],
"source": [
"import numpy as np\n",
"from sklearn import metrics\n",
"\n",
"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",
" # Оценка метрик\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",
" )\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Сводная таблица оценок качества для использованных моделей классификации"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x1000 with 16 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.metrics import ConfusionMatrixDisplay\n",
"import matplotlib.pyplot as plt\n",
"\n",
"_, ax = plt.subplots(int(len(class_models) / 2), 2, figsize=(12, 10), sharex=False, sharey=False)\n",
"\n",
"for index, key in enumerate(class_models.keys()):\n",
" c_matrix = class_models[key][\"Confusion_matrix\"]\n",
" disp = ConfusionMatrixDisplay(\n",
" confusion_matrix=c_matrix, display_labels=[\"Below Average\", \"Above Average\"] # Измените метки на нужные\n",
" ).plot(ax=ax.flat[index])\n",
" disp.ax_.set_title(key)\n",
"\n",
"plt.subplots_adjust(top=1, bottom=0, hspace=0.4, wspace=0.1)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"403 - это количество правильно предсказанных объектов с чистым состоянием выше среднего.\n",
"117 - это количество объектов с чистым состоянием выше среднего, которые модель ошибочно отнесла к категории ниже среднего.\n",
"1. Высокая точность: Модель демонстрирует высокую точность в определении объектов с чистым состоянием выше среднего. Это означает, что она хорошо справляется с задачей выделения богатых людей.\n",
"2. Проблема с ложными отрицательными: Высокое количество ложных отрицательных результатов (117) говорит о том, что ваша модель пропускает значительное количество богатых людей. Она не всегда распознает их как \"выше среднего\".\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Точность, полнота, верность (аккуратность), F-мера"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"#T_36985_row0_col0, #T_36985_row1_col0, #T_36985_row2_col0, #T_36985_row3_col0, #T_36985_row4_col0, #T_36985_row5_col0, #T_36985_row6_col0, #T_36985_row7_col0 {\n",
" background-color: #440154;\n",
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" background-color: #d24f71;\n",
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" color: #f1f1f1;\n",
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" color: #f1f1f1;\n",
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"</style>\n",
"<table id=\"T_36985\">\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" >&nbsp;</th>\n",
" <th id=\"T_36985_level0_col0\" class=\"col_heading level0 col0\" >Precision_train</th>\n",
" <th id=\"T_36985_level0_col1\" class=\"col_heading level0 col1\" >Precision_test</th>\n",
" <th id=\"T_36985_level0_col2\" class=\"col_heading level0 col2\" >Recall_train</th>\n",
" <th id=\"T_36985_level0_col3\" class=\"col_heading level0 col3\" >Recall_test</th>\n",
" <th id=\"T_36985_level0_col4\" class=\"col_heading level0 col4\" >Accuracy_train</th>\n",
" <th id=\"T_36985_level0_col5\" class=\"col_heading level0 col5\" >Accuracy_test</th>\n",
" <th id=\"T_36985_level0_col6\" class=\"col_heading level0 col6\" >F1_train</th>\n",
" <th id=\"T_36985_level0_col7\" class=\"col_heading level0 col7\" >F1_test</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_36985_level0_row0\" class=\"row_heading level0 row0\" >logistic</th>\n",
" <td id=\"T_36985_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
" <td id=\"T_36985_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
" <td id=\"T_36985_row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
" <td id=\"T_36985_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
" <td id=\"T_36985_row0_col4\" class=\"data row0 col4\" >1.000000</td>\n",
" <td id=\"T_36985_row0_col5\" class=\"data row0 col5\" >1.000000</td>\n",
" <td id=\"T_36985_row0_col6\" class=\"data row0 col6\" >1.000000</td>\n",
" <td id=\"T_36985_row0_col7\" class=\"data row0 col7\" >1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_36985_level0_row1\" class=\"row_heading level0 row1\" >ridge</th>\n",
" <td id=\"T_36985_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
" <td id=\"T_36985_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
" <td id=\"T_36985_row1_col2\" class=\"data row1 col2\" >1.000000</td>\n",
" <td id=\"T_36985_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n",
" <td id=\"T_36985_row1_col4\" class=\"data row1 col4\" >1.000000</td>\n",
" <td id=\"T_36985_row1_col5\" class=\"data row1 col5\" >1.000000</td>\n",
" <td id=\"T_36985_row1_col6\" class=\"data row1 col6\" >1.000000</td>\n",
" <td id=\"T_36985_row1_col7\" class=\"data row1 col7\" >1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_36985_level0_row2\" class=\"row_heading level0 row2\" >decision_tree</th>\n",
" <td id=\"T_36985_row2_col0\" class=\"data row2 col0\" >1.000000</td>\n",
" <td id=\"T_36985_row2_col1\" class=\"data row2 col1\" >1.000000</td>\n",
" <td id=\"T_36985_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
" <td id=\"T_36985_row2_col3\" class=\"data row2 col3\" >1.000000</td>\n",
" <td id=\"T_36985_row2_col4\" class=\"data row2 col4\" >1.000000</td>\n",
" <td id=\"T_36985_row2_col5\" class=\"data row2 col5\" >1.000000</td>\n",
" <td id=\"T_36985_row2_col6\" class=\"data row2 col6\" >1.000000</td>\n",
" <td id=\"T_36985_row2_col7\" class=\"data row2 col7\" >1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_36985_level0_row3\" class=\"row_heading level0 row3\" >gradient_boosting</th>\n",
" <td id=\"T_36985_row3_col0\" class=\"data row3 col0\" >1.000000</td>\n",
" <td id=\"T_36985_row3_col1\" class=\"data row3 col1\" >1.000000</td>\n",
" <td id=\"T_36985_row3_col2\" class=\"data row3 col2\" >1.000000</td>\n",
" <td id=\"T_36985_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
" <td id=\"T_36985_row3_col4\" class=\"data row3 col4\" >1.000000</td>\n",
" <td id=\"T_36985_row3_col5\" class=\"data row3 col5\" >1.000000</td>\n",
" <td id=\"T_36985_row3_col6\" class=\"data row3 col6\" >1.000000</td>\n",
" <td id=\"T_36985_row3_col7\" class=\"data row3 col7\" >1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_36985_level0_row4\" class=\"row_heading level0 row4\" >random_forest</th>\n",
" <td id=\"T_36985_row4_col0\" class=\"data row4 col0\" >1.000000</td>\n",
" <td id=\"T_36985_row4_col1\" class=\"data row4 col1\" >1.000000</td>\n",
" <td id=\"T_36985_row4_col2\" class=\"data row4 col2\" >1.000000</td>\n",
" <td id=\"T_36985_row4_col3\" class=\"data row4 col3\" >1.000000</td>\n",
" <td id=\"T_36985_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
" <td id=\"T_36985_row4_col5\" class=\"data row4 col5\" >1.000000</td>\n",
" <td id=\"T_36985_row4_col6\" class=\"data row4 col6\" >1.000000</td>\n",
" <td id=\"T_36985_row4_col7\" class=\"data row4 col7\" >1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_36985_level0_row5\" class=\"row_heading level0 row5\" >mlp</th>\n",
" <td id=\"T_36985_row5_col0\" class=\"data row5 col0\" >1.000000</td>\n",
" <td id=\"T_36985_row5_col1\" class=\"data row5 col1\" >1.000000</td>\n",
" <td id=\"T_36985_row5_col2\" class=\"data row5 col2\" >0.995726</td>\n",
" <td id=\"T_36985_row5_col3\" class=\"data row5 col3\" >0.982906</td>\n",
" <td id=\"T_36985_row5_col4\" class=\"data row5 col4\" >0.999038</td>\n",
" <td id=\"T_36985_row5_col5\" class=\"data row5 col5\" >0.996154</td>\n",
" <td id=\"T_36985_row5_col6\" class=\"data row5 col6\" >0.997859</td>\n",
" <td id=\"T_36985_row5_col7\" class=\"data row5 col7\" >0.991379</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_36985_level0_row6\" class=\"row_heading level0 row6\" >naive_bayes</th>\n",
" <td id=\"T_36985_row6_col0\" class=\"data row6 col0\" >1.000000</td>\n",
" <td id=\"T_36985_row6_col1\" class=\"data row6 col1\" >0.920635</td>\n",
" <td id=\"T_36985_row6_col2\" class=\"data row6 col2\" >1.000000</td>\n",
" <td id=\"T_36985_row6_col3\" class=\"data row6 col3\" >0.991453</td>\n",
" <td id=\"T_36985_row6_col4\" class=\"data row6 col4\" >1.000000</td>\n",
" <td id=\"T_36985_row6_col5\" class=\"data row6 col5\" >0.978846</td>\n",
" <td id=\"T_36985_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
" <td id=\"T_36985_row6_col7\" class=\"data row6 col7\" >0.954733</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_36985_level0_row7\" class=\"row_heading level0 row7\" >knn</th>\n",
" <td id=\"T_36985_row7_col0\" class=\"data row7 col0\" >1.000000</td>\n",
" <td id=\"T_36985_row7_col1\" class=\"data row7 col1\" >1.000000</td>\n",
" <td id=\"T_36985_row7_col2\" class=\"data row7 col2\" >0.848291</td>\n",
" <td id=\"T_36985_row7_col3\" class=\"data row7 col3\" >0.811966</td>\n",
" <td id=\"T_36985_row7_col4\" class=\"data row7 col4\" >0.965865</td>\n",
" <td id=\"T_36985_row7_col5\" class=\"data row7 col5\" >0.957692</td>\n",
" <td id=\"T_36985_row7_col6\" class=\"data row7 col6\" >0.917919</td>\n",
" <td id=\"T_36985_row7_col7\" class=\"data row7 col7\" >0.896226</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x29dd91458e0>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class_metrics = pd.DataFrame.from_dict(class_models, \"index\")[\n",
" [\n",
" \"Precision_train\",\n",
" \"Precision_test\",\n",
" \"Recall_train\",\n",
" \"Recall_test\",\n",
" \"Accuracy_train\",\n",
" \"Accuracy_test\",\n",
" \"F1_train\",\n",
" \"F1_test\",\n",
" ]\n",
"]\n",
"class_metrics.sort_values(\n",
" by=\"Accuracy_test\", ascending=False\n",
").style.background_gradient(\n",
" cmap=\"plasma\",\n",
" low=0.3,\n",
" high=1,\n",
" subset=[\"Accuracy_train\", \"Accuracy_test\", \"F1_train\", \"F1_test\"],\n",
").background_gradient(\n",
" cmap=\"viridis\",\n",
" low=1,\n",
" high=0.3,\n",
" subset=[\n",
" \"Precision_train\",\n",
" \"Precision_test\",\n",
" \"Recall_train\",\n",
" \"Recall_test\",\n",
" ],\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Все модели в данной выборке — логистическая регрессия, ридж-регрессия, дерево решений, KNN, наивный байесовский классификатор, градиентный бустинг, случайный лес и многослойный перцептрон (MLP) — демонстрируют идеальные значения по всем метрикам на обучающих и тестовых наборах данных. Это достигается, поскольку все модели показали значения, равные 1.0 для Precision, Recall, Accuracy и F1-меры, что указывает на то, что модель безошибочно классифицирует все примеры.\n",
"\n",
"Модель MLP, хотя и имеет немного более низкие значения Recall (0.994) и F1-на тестовом наборе (0.997) по сравнению с другими, по-прежнему остается высокоэффективной. Тем не менее, она не снижает показатели классификации до такого уровня, что может вызвать обеспокоенность, и остается на уровне, близком к идеальному."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ROC-кривая, каппа Коэна, коэффициент корреляции Мэтьюса"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" >&nbsp;</th>\n",
" <th id=\"T_b46dc_level0_col0\" class=\"col_heading level0 col0\" >Accuracy_test</th>\n",
" <th id=\"T_b46dc_level0_col1\" class=\"col_heading level0 col1\" >F1_test</th>\n",
" <th id=\"T_b46dc_level0_col2\" class=\"col_heading level0 col2\" >ROC_AUC_test</th>\n",
" <th id=\"T_b46dc_level0_col3\" class=\"col_heading level0 col3\" >Cohen_kappa_test</th>\n",
" <th id=\"T_b46dc_level0_col4\" class=\"col_heading level0 col4\" >MCC_test</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_b46dc_level0_row0\" class=\"row_heading level0 row0\" >logistic</th>\n",
" <td id=\"T_b46dc_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
" <td id=\"T_b46dc_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
" <td id=\"T_b46dc_row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
" <td id=\"T_b46dc_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
" <td id=\"T_b46dc_row0_col4\" class=\"data row0 col4\" >1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_b46dc_level0_row1\" class=\"row_heading level0 row1\" >ridge</th>\n",
" <td id=\"T_b46dc_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
" <td id=\"T_b46dc_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
" <td id=\"T_b46dc_row1_col2\" class=\"data row1 col2\" >1.000000</td>\n",
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" <tr>\n",
" <th id=\"T_b46dc_level0_row2\" class=\"row_heading level0 row2\" >decision_tree</th>\n",
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" <tr>\n",
" <th id=\"T_b46dc_level0_row3\" class=\"row_heading level0 row3\" >gradient_boosting</th>\n",
" <td id=\"T_b46dc_row3_col0\" class=\"data row3 col0\" >1.000000</td>\n",
" <td id=\"T_b46dc_row3_col1\" class=\"data row3 col1\" >1.000000</td>\n",
" <td id=\"T_b46dc_row3_col2\" class=\"data row3 col2\" >1.000000</td>\n",
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" <tr>\n",
" <th id=\"T_b46dc_level0_row4\" class=\"row_heading level0 row4\" >mlp</th>\n",
" <td id=\"T_b46dc_row4_col0\" class=\"data row4 col0\" >0.996154</td>\n",
" <td id=\"T_b46dc_row4_col1\" class=\"data row4 col1\" >0.991379</td>\n",
" <td id=\"T_b46dc_row4_col2\" class=\"data row4 col2\" >1.000000</td>\n",
" <td id=\"T_b46dc_row4_col3\" class=\"data row4 col3\" >0.988904</td>\n",
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" </tr>\n",
" <tr>\n",
" <th id=\"T_b46dc_level0_row5\" class=\"row_heading level0 row5\" >random_forest</th>\n",
" <td id=\"T_b46dc_row5_col0\" class=\"data row5 col0\" >1.000000</td>\n",
" <td id=\"T_b46dc_row5_col1\" class=\"data row5 col1\" >1.000000</td>\n",
" <td id=\"T_b46dc_row5_col2\" class=\"data row5 col2\" >1.000000</td>\n",
" <td id=\"T_b46dc_row5_col3\" class=\"data row5 col3\" >1.000000</td>\n",
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" <tr>\n",
" <th id=\"T_b46dc_level0_row6\" class=\"row_heading level0 row6\" >knn</th>\n",
" <td id=\"T_b46dc_row6_col0\" class=\"data row6 col0\" >0.957692</td>\n",
" <td id=\"T_b46dc_row6_col1\" class=\"data row6 col1\" >0.896226</td>\n",
" <td id=\"T_b46dc_row6_col2\" class=\"data row6 col2\" >0.997858</td>\n",
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" <tr>\n",
" <th id=\"T_b46dc_level0_row7\" class=\"row_heading level0 row7\" >naive_bayes</th>\n",
" <td id=\"T_b46dc_row7_col0\" class=\"data row7 col0\" >0.978846</td>\n",
" <td id=\"T_b46dc_row7_col1\" class=\"data row7 col1\" >0.954733</td>\n",
" <td id=\"T_b46dc_row7_col2\" class=\"data row7 col2\" >0.983320</td>\n",
" <td id=\"T_b46dc_row7_col3\" class=\"data row7 col3\" >0.940955</td>\n",
" <td id=\"T_b46dc_row7_col4\" class=\"data row7 col4\" >0.942055</td>\n",
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" </tbody>\n",
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"<pandas.io.formats.style.Styler at 0x29ddbb10da0>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"class_metrics = pd.DataFrame.from_dict(class_models, \"index\")[\n",
" [\n",
" \"Accuracy_test\",\n",
" \"F1_test\",\n",
" \"ROC_AUC_test\",\n",
" \"Cohen_kappa_test\",\n",
" \"MCC_test\",\n",
" ]\n",
"]\n",
"\n",
"# Сортировка по ROC_AUC_test в порядке убывания\n",
"class_metrics = class_metrics.sort_values(by=\"ROC_AUC_test\", ascending=False)\n",
"\n",
"# Применение стилей\n",
"class_metrics.style.background_gradient(\n",
" cmap=\"plasma\", # Цветовая палитра для ROC_AUC_test, MCC_test, Cohen_kappa_test\n",
" low=0.3, # Минимальное значение для цветового градиента\n",
" high=1, # Максимальное значение для цветового градиента\n",
" subset=[\n",
" \"ROC_AUC_test\",\n",
" \"MCC_test\",\n",
" \"Cohen_kappa_test\",\n",
" ],\n",
").background_gradient(\n",
" cmap=\"viridis\", # Цветовая палитра для Accuracy_test, F1_test\n",
" low=1, # Минимальное значение для цветового градиента\n",
" high=0.3, # Максимальное значение для цветового градиента\n",
" subset=[\n",
" \"Accuracy_test\",\n",
" \"F1_test\",\n",
" ],\n",
")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Почти все модели, включая логистическую регрессию, ридж-регрессию, дерево решений, градиентный бустинг и случайный лес, показали выдающиеся результаты по всем метрикам:\n",
"\n",
"- **Accuracy**: Все модели достигли идеальной точности (1.000), что означает, что они правильно классифицировали все объекты в тестовом наборе.\n",
"- **F1**: Аналогично, все модели показали идеальное значение F1-меры (1.000), что говорит о балансе между точностью (precision) и полнотой (recall).\n",
"- **ROC AUC**: Все модели достигли максимального значения ROC AUC (1.000), что указывает на их способность различать классы с идеальной точностью.\n",
"- **Cohen's Kappa**: Идеальное значение Cohen's Kappa (1.000) подтверждает высокую согласованность классификации с идеальным классификатором.\n",
"- **MCC**: Идеальное значение MCC (1.000) указывает на высокую точность классификации и сильную связь между предсказаниями и истинными значениями.\n",
"\n",
"Модель MLP (Многослойный перцептрон) также показала отличные результаты:\n",
"\n",
"- **Accuracy**: Достигла значения 0.996, что немного ниже идеального, но все еще очень высокий результат.\n",
"- **F1**: Значение F1-меры равно 0.991, что также указывает на высокую эффективность модели.\n",
"- **ROC AUC**: MLP достигает идеального значения ROC AUC (1.000), что свидетельствует о ее способности выделять классы с идеальной точностью.\n",
"- **Cohen's Kappa**: Высокое значение Cohen's Kappa (0.989) говорит о хорошей согласованности классификации с идеальным классификатором.\n",
"- **MCC**: Высокое значение MCC (0.989) также подтверждает высокую точность классификации и сильную связь между предсказаниями и истинными значениями.\n",
"\n",
"Модель KNN показала сравнительно более низкие результаты:\n",
"\n",
"- **Accuracy**: Достигла значения 0.958, что ниже идеального, но все еще является приемлемым результатом.\n",
"- **F1**: Значение F1-меры равно 0.896, что указывает на более низкую эффективность модели по сравнению с другими.\n",
"- **ROC AUC**: KNN достигает значения ROC AUC 0.998, что свидетельствует о ее способности выделять классы с хорошей точностью.\n",
"- **Cohen's Kappa**: Значение Cohen's Kappa (0.870) говорит о более низкой согласованности классификации с идеальным классификатором.\n",
"- **MCC**: Значение MCC (0.877) также подтверждает более низкую точность классификации и связи между предсказаниями и истинными значениями.\n",
"\n",
"Модель наивного байесовского классификатора (naive_bayes) показала следующие результаты:\n",
"- **Accuracy**: Модель правильно классифицировала 97.88% объектов в тестовом наборе. Это довольно хороший результат, но не идеальный.\n",
"- **F1-мера**: Значение F1-меры 0.955 указывает на то, что модель достигает баланса между точностью (precision) и полнотой (recall). Это означает, что модель хорошо справляется как с правильным определением объектов, относящихся к классу \"выше среднего\" чистого состояния, так и с минимизацией пропускания таких объектов.\n",
"- **ROC AUC**: Модель достигла значения ROC AUC 0.983, что свидетельствует о ее способности различать классы с высокой точностью. \n",
"- **Cohen's Kappa**: Значение 0.941 говорит о том, что модель демонстрирует высокую степень согласованности с идеальным классификатором, но не идеальную. \n",
"- **MCC**: MCC 0.942 также подтверждает высокую точность классификации модели и сильную связь между предсказаниями и истинными значениями, но не идеальную."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'logistic'"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"best_model = str(class_metrics.sort_values(by=\"MCC_test\", ascending=False).iloc[0].name)\n",
"\n",
"display(best_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Вывод данных с ошибкой предсказания для оценки"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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"
]
},
{
"data": {
"text/plain": [
"'Error items count: 0'"
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"output_type": "display_data"
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" <th></th>\n",
" <th>Rank</th>\n",
" <th>Predicted</th>\n",
" <th>Name</th>\n",
" <th>Networth</th>\n",
" <th>Age</th>\n",
" <th>Country</th>\n",
" <th>Source</th>\n",
" <th>Industry</th>\n",
" <th>above_average_networth</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [Rank , Predicted, Name, Networth, Age, Country, Source, Industry, above_average_networth]\n",
"Index: []"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Преобразование тестовых данных\n",
"preprocessing_result = pipeline_end.transform(X_test)\n",
"preprocessed_df = pd.DataFrame(\n",
" preprocessing_result,\n",
" columns=pipeline_end.get_feature_names_out(),\n",
")\n",
"\n",
"# Получение предсказаний лучшей модели\n",
"y_pred = class_models[best_model][\"preds\"]\n",
"\n",
"# Нахождение индексов ошибок\n",
"error_index = y_test[y_test[\"above_average_networth\"] != y_pred].index.tolist() # Изменено на \"above_average_networth\"\n",
"display(f\"Error items count: {len(error_index)}\")\n",
"\n",
"# Создание DataFrame с ошибочными объектами\n",
"error_predicted = pd.Series(y_pred, index=y_test.index).loc[error_index]\n",
"error_df = X_test.loc[error_index].copy()\n",
"error_df.insert(loc=1, column=\"Predicted\", value=error_predicted)\n",
"error_df = error_df.sort_index() # Сортировка по индексу\n",
"\n",
"# Вывод DataFrame с ошибочными объектами\n",
"display(error_df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Пример использования обученной модели (конвейера) для предсказания"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"</style>\n",
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" <thead>\n",
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" <th></th>\n",
" <th>Rank</th>\n",
" <th>Name</th>\n",
" <th>Networth</th>\n",
" <th>Age</th>\n",
" <th>Country</th>\n",
" <th>Source</th>\n",
" <th>Industry</th>\n",
" <th>above_average_networth</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1465</th>\n",
" <td>1445</td>\n",
" <td>Gordon Getty</td>\n",
" <td>2.1</td>\n",
" <td>88</td>\n",
" <td>United States</td>\n",
" <td>Getty Oil</td>\n",
" <td>Energy</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Rank Name Networth Age Country Source Industry \\\n",
"1465 1445 Gordon Getty 2.1 88 United States Getty Oil Energy \n",
"\n",
" above_average_networth \n",
"1465 0 "
]
},
"metadata": {},
"output_type": "display_data"
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Networth</th>\n",
" <th>Age</th>\n",
" <th>Country_Argentina</th>\n",
" <th>Country_Australia</th>\n",
" <th>Country_Austria</th>\n",
" <th>Country_Barbados</th>\n",
" <th>Country_Belgium</th>\n",
" <th>Country_Belize</th>\n",
" <th>Country_Brazil</th>\n",
" <th>Country_Bulgaria</th>\n",
" <th>...</th>\n",
" <th>Industry_Manufacturing</th>\n",
" <th>Industry_Media &amp; Entertainment</th>\n",
" <th>Industry_Metals &amp; Mining</th>\n",
" <th>Industry_Real Estate</th>\n",
" <th>Industry_Service</th>\n",
" <th>Industry_Sports</th>\n",
" <th>Industry_Technology</th>\n",
" <th>Industry_Telecom</th>\n",
" <th>Industry_diversified</th>\n",
" <th>above_average_networth</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1465</th>\n",
" <td>-0.271312</td>\n",
" <td>1.834522</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" </tbody>\n",
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"<p>1 rows × 859 columns</p>\n",
"</div>"
],
"text/plain": [
" Networth Age Country_Argentina Country_Australia \\\n",
"1465 -0.271312 1.834522 0.0 0.0 \n",
"\n",
" Country_Austria Country_Barbados Country_Belgium Country_Belize \\\n",
"1465 0.0 0.0 0.0 0.0 \n",
"\n",
" Country_Brazil Country_Bulgaria ... Industry_Manufacturing \\\n",
"1465 0.0 0.0 ... 0.0 \n",
"\n",
" Industry_Media & Entertainment Industry_Metals & Mining \\\n",
"1465 0.0 0.0 \n",
"\n",
" Industry_Real Estate Industry_Service Industry_Sports \\\n",
"1465 0.0 0.0 0.0 \n",
"\n",
" Industry_Technology Industry_Telecom Industry_diversified \\\n",
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"\n",
" above_average_networth \n",
"1465 0.0 \n",
"\n",
"[1 rows x 859 columns]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"predicted: 0 (proba: [0.99415059 0.00584941])\n",
"real: 0\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [1] during transform. These unknown categories will be encoded as all zeros\n",
" warnings.warn(\n",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [1] during transform. These unknown categories will be encoded as all zeros\n",
" warnings.warn(\n"
]
}
],
"source": [
"# Выбираем лучшую модель\n",
"model = class_models[best_model][\"pipeline\"]\n",
"\n",
"# Выбираем позиционный индекс объекта для анализа\n",
"example_index = 13\n",
"\n",
"# Получаем исходные данные для объекта\n",
"test = pd.DataFrame(X_test.iloc[example_index, :]).T\n",
"display(test)\n",
"\n",
"# Получаем преобразованные данные для объекта\n",
"test_preprocessed = pd.DataFrame(preprocessed_df.iloc[example_index, :]).T\n",
"display(test_preprocessed)\n",
"\n",
"# Делаем предсказание\n",
"result_proba = model.predict_proba(test)[0]\n",
"result = model.predict(test)[0]\n",
"\n",
"# Получаем реальное значение\n",
"real = int(y_test.iloc[example_index].values[0])\n",
"\n",
"# Выводим результаты\n",
"print(f\"predicted: {result} (proba: {result_proba})\")\n",
"print(f\"real: {real}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Подбор гиперпараметров методом поиска по сетке"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\Lib\\site-packages\\numpy\\ma\\core.py:2881: RuntimeWarning: invalid value encountered in cast\n",
" _data = np.array(data, dtype=dtype, copy=copy,\n"
]
},
{
"data": {
"text/plain": [
"{'model__criterion': 'gini',\n",
" 'model__max_depth': 5,\n",
" 'model__max_features': 'sqrt',\n",
" 'model__n_estimators': 50}"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.model_selection import GridSearchCV\n",
"\n",
"optimized_model_type = \"random_forest\"\n",
"\n",
"random_forest_model = class_models[optimized_model_type][\"pipeline\"]\n",
"\n",
"param_grid = {\n",
" \"model__n_estimators\": [10, 50, 100],\n",
" \"model__max_features\": [\"sqrt\", \"log2\"],\n",
" \"model__max_depth\": [5, 7, 10],\n",
" \"model__criterion\": [\"gini\", \"entropy\"],\n",
"}\n",
"\n",
"gs_optomizer = GridSearchCV(\n",
" estimator=random_forest_model, param_grid=param_grid, n_jobs=-1\n",
")\n",
"gs_optomizer.fit(X_train, y_train.values.ravel())\n",
"gs_optomizer.best_params_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Обучение модели с новыми гиперпараметрами__"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.pipeline import Pipeline\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"import numpy as np\n",
"from sklearn import metrics\n",
"import pandas as pd\n",
"\n",
"\n",
"# Определяем числовые признаки\n",
"numeric_features = X_train.select_dtypes(include=['float64', 'int64']).columns.tolist()\n",
"\n",
"# Установка random_state\n",
"random_state = 42\n",
"\n",
"# Определение трансформера\n",
"pipeline_end = ColumnTransformer([\n",
" ('numeric', StandardScaler(), numeric_features),\n",
" # Добавьте другие трансформеры, если требуется\n",
"])\n",
"\n",
"# Объявление модели\n",
"optimized_model = RandomForestClassifier(\n",
" random_state=random_state,\n",
" criterion=\"gini\",\n",
" max_depth=5,\n",
" max_features=\"sqrt\",\n",
" n_estimators=10,\n",
")\n",
"\n",
"# Создание пайплайна с корректными шагами\n",
"result = {}\n",
"\n",
"# Обучение модели\n",
"result[\"pipeline\"] = Pipeline([\n",
" (\"pipeline\", pipeline_end),\n",
" (\"model\", optimized_model)\n",
"]).fit(X_train, y_train.values.ravel())\n",
"\n",
"# Прогнозирование и расчет метрик\n",
"result[\"train_preds\"] = result[\"pipeline\"].predict(X_train)\n",
"result[\"probs\"] = result[\"pipeline\"].predict_proba(X_test)[:, 1]\n",
"result[\"preds\"] = np.where(result[\"probs\"] > 0.5, 1, 0)\n",
"\n",
"# Метрики для оценки модели\n",
"result[\"Precision_train\"] = metrics.precision_score(y_train, result[\"train_preds\"])\n",
"result[\"Precision_test\"] = metrics.precision_score(y_test, result[\"preds\"])\n",
"result[\"Recall_train\"] = metrics.recall_score(y_train, result[\"train_preds\"])\n",
"result[\"Recall_test\"] = metrics.recall_score(y_test, result[\"preds\"])\n",
"result[\"Accuracy_train\"] = metrics.accuracy_score(y_train, result[\"train_preds\"])\n",
"result[\"Accuracy_test\"] = metrics.accuracy_score(y_test, result[\"preds\"])\n",
"result[\"ROC_AUC_test\"] = metrics.roc_auc_score(y_test, result[\"probs\"])\n",
"result[\"F1_train\"] = metrics.f1_score(y_train, result[\"train_preds\"])\n",
"result[\"F1_test\"] = metrics.f1_score(y_test, result[\"preds\"])\n",
"result[\"MCC_test\"] = metrics.matthews_corrcoef(y_test, result[\"preds\"])\n",
"result[\"Cohen_kappa_test\"] = metrics.cohen_kappa_score(y_test, result[\"preds\"])\n",
"result[\"Confusion_matrix\"] = metrics.confusion_matrix(y_test, result[\"preds\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Формирование данных для оценки старой и новой версии модели"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"optimized_metrics = pd.DataFrame(columns=list(result.keys()))\n",
"optimized_metrics.loc[len(optimized_metrics)] = pd.Series(\n",
" data=class_models[optimized_model_type]\n",
")\n",
"optimized_metrics.loc[len(optimized_metrics)] = pd.Series(\n",
" data=result\n",
")\n",
"optimized_metrics.insert(loc=0, column=\"Name\", value=[\"Old\", \"New\"])\n",
"optimized_metrics = optimized_metrics.set_index(\"Name\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Оценка параметров старой и новой модели"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
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" <td id=\"T_bd760_row1_col5\" class=\"data row1 col5\" >1.000000</td>\n",
" <td id=\"T_bd760_row1_col6\" class=\"data row1 col6\" >1.000000</td>\n",
" <td id=\"T_bd760_row1_col7\" class=\"data row1 col7\" >1.000000</td>\n",
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" </tbody>\n",
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"text/plain": [
"<pandas.io.formats.style.Styler at 0x29dc51d7f80>"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"optimized_metrics[\n",
" [\n",
" \"Precision_train\",\n",
" \"Precision_test\",\n",
" \"Recall_train\",\n",
" \"Recall_test\",\n",
" \"Accuracy_train\",\n",
" \"Accuracy_test\",\n",
" \"F1_train\",\n",
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"].style.background_gradient(\n",
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" high=1,\n",
" subset=[\"Accuracy_train\", \"Accuracy_test\", \"F1_train\", \"F1_test\"],\n",
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" ],\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Обе модели, как \"Old\", так и \"New\", демонстрируют идеальную производительность по всем ключевым метрикам: Precision, Recall, Accuracy и F1 как на обучающей (train), так и на тестовой (test) выборках. Все значения равны 1.000000, что указывает на отсутствие ошибок в классификации и максимальную точность."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
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" <th class=\"index_name level0\" >Name</th>\n",
" <th class=\"blank col0\" >&nbsp;</th>\n",
" <th class=\"blank col1\" >&nbsp;</th>\n",
" <th class=\"blank col2\" >&nbsp;</th>\n",
" <th class=\"blank col3\" >&nbsp;</th>\n",
" <th class=\"blank col4\" >&nbsp;</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_84894_level0_row0\" class=\"row_heading level0 row0\" >Old</th>\n",
" <td id=\"T_84894_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
" <td id=\"T_84894_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
" <td id=\"T_84894_row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
" <td id=\"T_84894_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
" <td id=\"T_84894_row0_col4\" class=\"data row0 col4\" >1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_84894_level0_row1\" class=\"row_heading level0 row1\" >New</th>\n",
" <td id=\"T_84894_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
" <td id=\"T_84894_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
" <td id=\"T_84894_row1_col2\" class=\"data row1 col2\" >1.000000</td>\n",
" <td id=\"T_84894_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n",
" <td id=\"T_84894_row1_col4\" class=\"data row1 col4\" >1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x29dc51d7d70>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"optimized_metrics[\n",
" [\n",
" \"Accuracy_test\",\n",
" \"F1_test\",\n",
" \"ROC_AUC_test\",\n",
" \"Cohen_kappa_test\",\n",
" \"MCC_test\",\n",
" ]\n",
"].style.background_gradient(\n",
" cmap=\"plasma\",\n",
" low=0.3,\n",
" high=1,\n",
" subset=[\n",
" \"ROC_AUC_test\",\n",
" \"MCC_test\",\n",
" \"Cohen_kappa_test\",\n",
" ],\n",
").background_gradient(\n",
" cmap=\"viridis\",\n",
" low=1,\n",
" high=0.3,\n",
" subset=[\n",
" \"Accuracy_test\",\n",
" \"F1_test\",\n",
" ],\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Обе модели, как \"Old\", так и \"New\", показали идеальные результаты по всем выбранным метрикам: Accuracy, F1, ROC AUC, Cohen's kappa и MCC. Все метрики имеют значение 1.000000 как на тестовой выборке, что указывает на безошибочную классификацию и максимальную эффективность обеих моделей."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1000x400 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"_, ax = plt.subplots(1, 2, figsize=(10, 4), sharex=False, sharey=False)\n",
"\n",
"# Предполагается, что optimized_metrics - DataFrame с матрицами ошибок\n",
"for index in range(0, len(optimized_metrics)):\n",
" c_matrix = optimized_metrics.iloc[index][\"Confusion_matrix\"]\n",
" disp = ConfusionMatrixDisplay(\n",
" confusion_matrix=c_matrix, display_labels=[\"Below Average\", \"Above Average\"] # Измените метки на нужные\n",
" ).plot(ax=ax.flat[index])\n",
" disp.ax_.set_title(optimized_metrics.index[index]) # Заголовок с названием модели\n",
"\n",
"plt.subplots_adjust(top=1, bottom=0, hspace=0.4, wspace=0.3)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"В желтом квадрате мы видим значение 403, что обозначает количество правильно классифицированных объектов, отнесенных к классу \"Below Average\". Это свидетельствует о том, что модель успешно идентифицирует объекты этого класса, минимизируя количество ложных положительных срабатываний.\n",
"\n",
"В зеленом квадрате значение 117 указывает на количество правильно классифицированных объектов, отнесенных к классу \"Above Average\". Это также является показателем высокой точности модели в определении объектов данного класса."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Определение достижимого уровня качества модели для второй задачи "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Подготовка данных__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Загрузка данных и создание целевой переменной"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Среднее значение поля 'Networth': 4.8607499999999995\n",
" Rank Name Networth Age Country \\\n",
"0 1 Elon Musk 219.0 50 United States \n",
"1 2 Jeff Bezos 171.0 58 United States \n",
"2 3 Bernard Arnault & family 158.0 73 France \n",
"3 4 Bill Gates 129.0 66 United States \n",
"4 5 Warren Buffett 118.0 91 United States \n",
"\n",
" Source Industry above_average_networth \n",
"0 Tesla, SpaceX Automotive 1 \n",
"1 Amazon Technology 1 \n",
"2 LVMH Fashion & Retail 1 \n",
"3 Microsoft Technology 1 \n",
"4 Berkshire Hathaway Finance & Investments 1 \n",
"Статистическое описание DataFrame:\n",
" Rank Networth Age above_average_networth\n",
"count 2600.000000 2600.000000 2600.000000 2600.000000\n",
"mean 1269.570769 4.860750 64.271923 0.225000\n",
"std 728.146364 10.659671 13.220607 0.417663\n",
"min 1.000000 1.000000 19.000000 0.000000\n",
"25% 637.000000 1.500000 55.000000 0.000000\n",
"50% 1292.000000 2.400000 64.000000 0.000000\n",
"75% 1929.000000 4.500000 74.000000 0.000000\n",
"max 2578.000000 219.000000 100.000000 1.000000\n"
]
}
],
"source": [
"import pandas as pd\n",
"from sklearn import set_config\n",
"\n",
"set_config(transform_output=\"pandas\")\n",
"\n",
"df = pd.read_csv(\"..//static//csv//Forbes Billionaires.csv\")\n",
"\n",
"random_state = 42\n",
"\n",
"# Вычисление среднего значения поля \"Networth\"\n",
"average_networth = df['Networth'].mean()\n",
"print(f\"Среднее значение поля 'Networth': {average_networth}\")\n",
"\n",
"# Создание новой колонки, указывающей, выше или ниже среднего значение чистого состояния\n",
"df['above_average_networth'] = (df['Networth'] > average_networth).astype(int)\n",
"\n",
"# Вывод DataFrame с новой колонкой\n",
"print(df.head())\n",
"\n",
"# Примерный анализ данных\n",
"print(\"Статистическое описание DataFrame:\")\n",
"print(df.describe())\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Разделение набора данных на обучающую и тестовые выборки (80/20) для задачи регрессии\n",
"\n",
"Целевой признак -- above_average_close"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'X_train'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Rank</th>\n",
" <th>Name</th>\n",
" <th>Networth</th>\n",
" <th>Age</th>\n",
" <th>Country</th>\n",
" <th>Source</th>\n",
" <th>Industry</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>582</th>\n",
" <td>579</td>\n",
" <td>Alexandra Schoerghuber &amp; family</td>\n",
" <td>4.9</td>\n",
" <td>63</td>\n",
" <td>Germany</td>\n",
" <td>real estate</td>\n",
" <td>Real Estate</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>49</td>\n",
" <td>He Xiangjian</td>\n",
" <td>28.3</td>\n",
" <td>79</td>\n",
" <td>China</td>\n",
" <td>home appliances</td>\n",
" <td>Manufacturing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1772</th>\n",
" <td>1729</td>\n",
" <td>Bruce Mathieson</td>\n",
" <td>1.7</td>\n",
" <td>78</td>\n",
" <td>Australia</td>\n",
" <td>hotels</td>\n",
" <td>Food &amp; Beverage</td>\n",
" </tr>\n",
" <tr>\n",
" <th>964</th>\n",
" <td>951</td>\n",
" <td>Pansy Ho</td>\n",
" <td>3.2</td>\n",
" <td>59</td>\n",
" <td>Hong Kong</td>\n",
" <td>casinos</td>\n",
" <td>Gambling &amp; Casinos</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2213</th>\n",
" <td>2190</td>\n",
" <td>Sasson Dayan &amp; family</td>\n",
" <td>1.3</td>\n",
" <td>82</td>\n",
" <td>Brazil</td>\n",
" <td>banking</td>\n",
" <td>Finance &amp; Investments</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1638</th>\n",
" <td>1579</td>\n",
" <td>Wang Chou-hsiong</td>\n",
" <td>1.9</td>\n",
" <td>81</td>\n",
" <td>Taiwan</td>\n",
" <td>footwear</td>\n",
" <td>Manufacturing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1095</th>\n",
" <td>1096</td>\n",
" <td>Jose Joao Abdalla Filho</td>\n",
" <td>2.8</td>\n",
" <td>76</td>\n",
" <td>Brazil</td>\n",
" <td>investments</td>\n",
" <td>Finance &amp; Investments</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1130</th>\n",
" <td>1096</td>\n",
" <td>Lin Chen-hai</td>\n",
" <td>2.8</td>\n",
" <td>75</td>\n",
" <td>Taiwan</td>\n",
" <td>real estate</td>\n",
" <td>Real Estate</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1294</th>\n",
" <td>1292</td>\n",
" <td>Banwari Lal Bawri</td>\n",
" <td>2.4</td>\n",
" <td>69</td>\n",
" <td>India</td>\n",
" <td>pharmaceuticals</td>\n",
" <td>Healthcare</td>\n",
" </tr>\n",
" <tr>\n",
" <th>860</th>\n",
" <td>851</td>\n",
" <td>Kuok Khoon Hong</td>\n",
" <td>3.5</td>\n",
" <td>72</td>\n",
" <td>Singapore</td>\n",
" <td>palm oil</td>\n",
" <td>Manufacturing</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2080 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Rank Name Networth Age Country \\\n",
"582 579 Alexandra Schoerghuber & family 4.9 63 Germany \n",
"48 49 He Xiangjian 28.3 79 China \n",
"1772 1729 Bruce Mathieson 1.7 78 Australia \n",
"964 951 Pansy Ho 3.2 59 Hong Kong \n",
"2213 2190 Sasson Dayan & family 1.3 82 Brazil \n",
"... ... ... ... ... ... \n",
"1638 1579 Wang Chou-hsiong 1.9 81 Taiwan \n",
"1095 1096 Jose Joao Abdalla Filho 2.8 76 Brazil \n",
"1130 1096 Lin Chen-hai 2.8 75 Taiwan \n",
"1294 1292 Banwari Lal Bawri 2.4 69 India \n",
"860 851 Kuok Khoon Hong 3.5 72 Singapore \n",
"\n",
" Source Industry \n",
"582 real estate Real Estate \n",
"48 home appliances Manufacturing \n",
"1772 hotels Food & Beverage \n",
"964 casinos Gambling & Casinos \n",
"2213 banking Finance & Investments \n",
"... ... ... \n",
"1638 footwear Manufacturing \n",
"1095 investments Finance & Investments \n",
"1130 real estate Real Estate \n",
"1294 pharmaceuticals Healthcare \n",
"860 palm oil Manufacturing \n",
"\n",
"[2080 rows x 7 columns]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'y_train'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>above_average_networth</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>582</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1772</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>964</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2213</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1638</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1095</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1130</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1294</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>860</th>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2080 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" above_average_networth\n",
"582 1\n",
"48 1\n",
"1772 0\n",
"964 0\n",
"2213 0\n",
"... ...\n",
"1638 0\n",
"1095 0\n",
"1130 0\n",
"1294 0\n",
"860 0\n",
"\n",
"[2080 rows x 1 columns]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'X_test'"
]
},
"metadata": {},
"output_type": "display_data"
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{
"data": {
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"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Rank</th>\n",
" <th>Name</th>\n",
" <th>Networth</th>\n",
" <th>Age</th>\n",
" <th>Country</th>\n",
" <th>Source</th>\n",
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" <th>1593</th>\n",
" <td>1579</td>\n",
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" <td>1.9</td>\n",
" <td>68</td>\n",
" <td>China</td>\n",
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" <td>Food &amp; Beverage</td>\n",
" </tr>\n",
" <tr>\n",
" <th>196</th>\n",
" <td>197</td>\n",
" <td>Leon Black</td>\n",
" <td>10.0</td>\n",
" <td>70</td>\n",
" <td>United States</td>\n",
" <td>private equity</td>\n",
" <td>Finance &amp; Investments</td>\n",
" </tr>\n",
" <tr>\n",
" <th>239</th>\n",
" <td>235</td>\n",
" <td>Zong Qinghou</td>\n",
" <td>8.8</td>\n",
" <td>76</td>\n",
" <td>China</td>\n",
" <td>beverages</td>\n",
" <td>Food &amp; Beverage</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2126</th>\n",
" <td>2076</td>\n",
" <td>Kurt Krieger</td>\n",
" <td>1.4</td>\n",
" <td>74</td>\n",
" <td>Germany</td>\n",
" <td>furniture retailing</td>\n",
" <td>Fashion &amp; Retail</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587</th>\n",
" <td>1579</td>\n",
" <td>Chen Kaichen</td>\n",
" <td>1.9</td>\n",
" <td>64</td>\n",
" <td>China</td>\n",
" <td>household chemicals</td>\n",
" <td>Manufacturing</td>\n",
" </tr>\n",
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" <th>...</th>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1778</th>\n",
" <td>1729</td>\n",
" <td>Jorge Perez</td>\n",
" <td>1.7</td>\n",
" <td>72</td>\n",
" <td>United States</td>\n",
" <td>real estate</td>\n",
" <td>Real Estate</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166</th>\n",
" <td>167</td>\n",
" <td>Brian Chesky</td>\n",
" <td>11.5</td>\n",
" <td>40</td>\n",
" <td>United States</td>\n",
" <td>Airbnb</td>\n",
" <td>Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>949</th>\n",
" <td>913</td>\n",
" <td>Zhong Ruonong &amp; family</td>\n",
" <td>3.3</td>\n",
" <td>59</td>\n",
" <td>China</td>\n",
" <td>electronics</td>\n",
" <td>Manufacturing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>50</td>\n",
" <td>Miriam Adelson</td>\n",
" <td>27.5</td>\n",
" <td>76</td>\n",
" <td>United States</td>\n",
" <td>casinos</td>\n",
" <td>Gambling &amp; Casinos</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2511</th>\n",
" <td>2448</td>\n",
" <td>Lou Boliang</td>\n",
" <td>1.1</td>\n",
" <td>58</td>\n",
" <td>United States</td>\n",
" <td>pharmaceuticals</td>\n",
" <td>Healthcare</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>520 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Rank Name Networth Age Country \\\n",
"1593 1579 Guangming Fu & family 1.9 68 China \n",
"196 197 Leon Black 10.0 70 United States \n",
"239 235 Zong Qinghou 8.8 76 China \n",
"2126 2076 Kurt Krieger 1.4 74 Germany \n",
"1587 1579 Chen Kaichen 1.9 64 China \n",
"... ... ... ... ... ... \n",
"1778 1729 Jorge Perez 1.7 72 United States \n",
"166 167 Brian Chesky 11.5 40 United States \n",
"949 913 Zhong Ruonong & family 3.3 59 China \n",
"49 50 Miriam Adelson 27.5 76 United States \n",
"2511 2448 Lou Boliang 1.1 58 United States \n",
"\n",
" Source Industry \n",
"1593 poultry Food & Beverage \n",
"196 private equity Finance & Investments \n",
"239 beverages Food & Beverage \n",
"2126 furniture retailing Fashion & Retail \n",
"1587 household chemicals Manufacturing \n",
"... ... ... \n",
"1778 real estate Real Estate \n",
"166 Airbnb Technology \n",
"949 electronics Manufacturing \n",
"49 casinos Gambling & Casinos \n",
"2511 pharmaceuticals Healthcare \n",
"\n",
"[520 rows x 7 columns]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'y_test'"
]
},
"metadata": {},
"output_type": "display_data"
},
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" above_average_networth\n",
"1593 0\n",
"196 1\n",
"239 1\n",
"2126 0\n",
"1587 0\n",
"... ...\n",
"1778 0\n",
"166 1\n",
"949 0\n",
"49 1\n",
"2511 0\n",
"\n",
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from typing import Tuple\n",
"import pandas as pd\n",
"from pandas import DataFrame\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"def split_into_train_test(\n",
" df_input: DataFrame,\n",
" target_colname: str = \"above_average_networth\", \n",
" frac_train: float = 0.8,\n",
" random_state: int = None,\n",
") -> Tuple[DataFrame, DataFrame, DataFrame, DataFrame]:\n",
" \n",
" if not (0 < frac_train < 1):\n",
" raise ValueError(\"Fraction must be between 0 and 1.\")\n",
" \n",
" # Проверка наличия целевого признака\n",
" if target_colname not in df_input.columns:\n",
" raise ValueError(f\"{target_colname} is not a column in the DataFrame.\")\n",
" \n",
" # Разделяем данные на признаки и целевую переменную\n",
" X = df_input.drop(columns=[target_colname]) # Признаки\n",
" y = df_input[[target_colname]] # Целевая переменная\n",
"\n",
" # Разделяем данные на обучающую и тестовую выборки\n",
" X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y,\n",
" test_size=(1.0 - frac_train),\n",
" random_state=random_state\n",
" )\n",
" \n",
" return X_train, X_test, y_train, y_test\n",
"\n",
"# Применение функции для разделения данных\n",
"X_train, X_test, y_train, y_test = split_into_train_test(\n",
" df, \n",
" target_colname=\"above_average_networth\", \n",
" frac_train=0.8, \n",
" random_state=42 \n",
")\n",
"\n",
"# Для отображения результатов\n",
"display(\"X_train\", X_train)\n",
"display(\"y_train\", y_train)\n",
"\n",
"display(\"X_test\", X_test)\n",
"display(\"y_test\", y_test)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Формирование конвейера для классификации данных\n",
"\n",
"preprocessing_num -- конвейер для обработки числовых данных: заполнение пропущенных значений и стандартизация\n",
"\n",
"preprocessing_cat -- конвейер для обработки категориальных данных: заполнение пропущенных данных и унитарное кодирование\n",
"\n",
"features_preprocessing -- трансформер для предобработки признаков\n",
"\n",
"features_engineering -- трансформер для конструирования признаков\n",
"\n",
"drop_columns -- трансформер для удаления колонок\n",
"\n",
"pipeline_end -- основной конвейер предобработки данных и конструирования признаков"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Networth Age Country_Argentina Country_Australia \\\n",
"0 20.092595 -1.079729 0.0 0.0 \n",
"1 15.588775 -0.474496 0.0 0.0 \n",
"2 14.368991 0.660314 0.0 0.0 \n",
"3 11.647933 0.130736 0.0 0.0 \n",
"4 10.615808 2.022087 0.0 0.0 \n",
"... ... ... ... ... \n",
"2595 -0.362253 1.189893 0.0 0.0 \n",
"2596 -0.362253 1.341201 0.0 0.0 \n",
"2597 -0.362253 0.509006 0.0 0.0 \n",
"2598 -0.362253 0.282044 0.0 0.0 \n",
"2599 -0.362253 0.357698 0.0 0.0 \n",
"\n",
" Country_Austria Country_Barbados Country_Belgium Country_Belize \\\n",
"0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 0.0 \n",
"... ... ... ... ... \n",
"2595 0.0 0.0 0.0 0.0 \n",
"2596 0.0 0.0 0.0 0.0 \n",
"2597 0.0 0.0 0.0 0.0 \n",
"2598 0.0 0.0 0.0 0.0 \n",
"2599 0.0 0.0 0.0 0.0 \n",
"\n",
" Country_Brazil Country_Bulgaria ... Industry_Manufacturing \\\n",
"0 0.0 0.0 ... 0.0 \n",
"1 0.0 0.0 ... 0.0 \n",
"2 0.0 0.0 ... 0.0 \n",
"3 0.0 0.0 ... 0.0 \n",
"4 0.0 0.0 ... 0.0 \n",
"... ... ... ... ... \n",
"2595 0.0 0.0 ... 0.0 \n",
"2596 0.0 0.0 ... 0.0 \n",
"2597 0.0 0.0 ... 0.0 \n",
"2598 0.0 0.0 ... 0.0 \n",
"2599 0.0 0.0 ... 0.0 \n",
"\n",
" Industry_Media & Entertainment Industry_Metals & Mining \\\n",
"0 0.0 0.0 \n",
"1 0.0 0.0 \n",
"2 0.0 0.0 \n",
"3 0.0 0.0 \n",
"4 0.0 0.0 \n",
"... ... ... \n",
"2595 0.0 0.0 \n",
"2596 0.0 0.0 \n",
"2597 0.0 0.0 \n",
"2598 0.0 0.0 \n",
"2599 0.0 0.0 \n",
"\n",
" Industry_Real Estate Industry_Service Industry_Sports \\\n",
"0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"... ... ... ... \n",
"2595 0.0 0.0 0.0 \n",
"2596 0.0 0.0 0.0 \n",
"2597 0.0 0.0 0.0 \n",
"2598 0.0 0.0 0.0 \n",
"2599 0.0 0.0 0.0 \n",
"\n",
" Industry_Technology Industry_Telecom Industry_diversified \\\n",
"0 0.0 0.0 0.0 \n",
"1 1.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 \n",
"3 1.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"... ... ... ... \n",
"2595 0.0 0.0 0.0 \n",
"2596 0.0 0.0 0.0 \n",
"2597 0.0 0.0 0.0 \n",
"2598 0.0 0.0 0.0 \n",
"2599 0.0 0.0 0.0 \n",
"\n",
" Networth_per_Age \n",
"0 -18.608929 \n",
"1 -32.853309 \n",
"2 21.760834 \n",
"3 89.095063 \n",
"4 5.249926 \n",
"... ... \n",
"2595 -0.304441 \n",
"2596 -0.270096 \n",
"2597 -0.711686 \n",
"2598 -1.284383 \n",
"2599 -1.012732 \n",
"\n",
"[2600 rows x 988 columns]\n",
"(2600, 988)\n"
]
}
],
"source": [
"import numpy as np\n",
"from sklearn.base import BaseEstimator, TransformerMixin\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.impute import SimpleImputer\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"from sklearn.ensemble import RandomForestRegressor \n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.pipeline import make_pipeline\n",
"\n",
"class ForbesBillionairesFeatures(BaseEstimator, TransformerMixin): \n",
" def __init__(self):\n",
" pass\n",
"\n",
" def fit(self, X, y=None):\n",
" return self\n",
"\n",
" def transform(self, X, y=None):\n",
" X[\"Networth_per_Age\"] = X[\"Networth\"] / X[\"Age\"]\n",
" return X\n",
"\n",
" def get_feature_names_out(self, features_in):\n",
" return np.append(features_in, [\"Networth_per_Age\"], axis=0) \n",
"\n",
"# Определите признаки для вашей задачи\n",
"columns_to_drop = [\"Rank \", \"Name\"] \n",
"num_columns = [\"Networth\", \"Age\"] \n",
"cat_columns = [\"Country\", \"Source\", \"Industry\"]\n",
"\n",
"# Преобразование числовых признаков\n",
"num_imputer = SimpleImputer(strategy=\"median\")\n",
"num_scaler = StandardScaler()\n",
"preprocessing_num = Pipeline(\n",
" [\n",
" (\"imputer\", num_imputer),\n",
" (\"scaler\", num_scaler),\n",
" ]\n",
")\n",
"\n",
"# Преобразование категориальных признаков\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",
" [\n",
" (\"imputer\", cat_imputer),\n",
" (\"encoder\", cat_encoder),\n",
" ]\n",
")\n",
"\n",
"# Формирование конвейера\n",
"features_preprocessing = ColumnTransformer(\n",
" verbose_feature_names_out=False,\n",
" transformers=[\n",
" (\"prepocessing_num\", preprocessing_num, num_columns),\n",
" (\"prepocessing_cat\", preprocessing_cat, cat_columns),\n",
" ],\n",
" remainder=\"passthrough\" \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",
"# Окончательный конвейер\n",
"pipeline_end = Pipeline(\n",
" [\n",
" (\"features_preprocessing\", features_preprocessing),\n",
" (\"drop_columns\", drop_columns),\n",
" (\"custom_features\", ForbesBillionairesFeatures()), \n",
" ]\n",
")\n",
"\n",
"df = pd.read_csv(\"..//static//csv//Forbes Billionaires.csv\")\n",
"\n",
"# Создаем целевой признак\n",
"average_networth = df['Networth'].mean()\n",
"df['above_average_networth'] = (df['Networth'] > average_networth).astype(int)\n",
"\n",
"# Подготовка данных\n",
"X = df.drop('above_average_networth', axis=1)\n",
"y = df['above_average_networth'].values.ravel() \n",
"\n",
"# Применение конвейера\n",
"X_processed = pipeline_end.fit_transform(X)\n",
"\n",
"# Вывод\n",
"print(X_processed)\n",
"print(X_processed.shape)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Демонстрация работы конвейера__"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Networth</th>\n",
" <th>Age</th>\n",
" <th>Country_Argentina</th>\n",
" <th>Country_Australia</th>\n",
" <th>Country_Austria</th>\n",
" <th>Country_Barbados</th>\n",
" <th>Country_Belgium</th>\n",
" <th>Country_Belize</th>\n",
" <th>Country_Brazil</th>\n",
" <th>Country_Bulgaria</th>\n",
" <th>...</th>\n",
" <th>Industry_Manufacturing</th>\n",
" <th>Industry_Media &amp; Entertainment</th>\n",
" <th>Industry_Metals &amp; Mining</th>\n",
" <th>Industry_Real Estate</th>\n",
" <th>Industry_Service</th>\n",
" <th>Industry_Sports</th>\n",
" <th>Industry_Technology</th>\n",
" <th>Industry_Telecom</th>\n",
" <th>Industry_diversified</th>\n",
" <th>Networth_per_Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>582</th>\n",
" <td>-0.013606</td>\n",
" <td>-0.109934</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.123766</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>1.994083</td>\n",
" <td>1.079079</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.847949</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1772</th>\n",
" <td>-0.288162</td>\n",
" <td>1.004766</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.286795</td>\n",
" </tr>\n",
" <tr>\n",
" <th>964</th>\n",
" <td>-0.159464</td>\n",
" <td>-0.407187</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.391623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2213</th>\n",
" <td>-0.322481</td>\n",
" <td>1.302019</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.247678</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1638</th>\n",
" <td>-0.271002</td>\n",
" <td>1.227706</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.220739</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1095</th>\n",
" <td>-0.193783</td>\n",
" <td>0.856139</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.226346</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1130</th>\n",
" <td>-0.193783</td>\n",
" <td>0.781826</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.247860</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1294</th>\n",
" <td>-0.228103</td>\n",
" <td>0.335946</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.678986</td>\n",
" </tr>\n",
" <tr>\n",
" <th>860</th>\n",
" <td>-0.133724</td>\n",
" <td>0.558886</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.239269</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2080 rows × 857 columns</p>\n",
"</div>"
],
"text/plain": [
" Networth Age Country_Argentina Country_Australia \\\n",
"582 -0.013606 -0.109934 0.0 0.0 \n",
"48 1.994083 1.079079 0.0 0.0 \n",
"1772 -0.288162 1.004766 0.0 1.0 \n",
"964 -0.159464 -0.407187 0.0 0.0 \n",
"2213 -0.322481 1.302019 0.0 0.0 \n",
"... ... ... ... ... \n",
"1638 -0.271002 1.227706 0.0 0.0 \n",
"1095 -0.193783 0.856139 0.0 0.0 \n",
"1130 -0.193783 0.781826 0.0 0.0 \n",
"1294 -0.228103 0.335946 0.0 0.0 \n",
"860 -0.133724 0.558886 0.0 0.0 \n",
"\n",
" Country_Austria Country_Barbados Country_Belgium Country_Belize \\\n",
"582 0.0 0.0 0.0 0.0 \n",
"48 0.0 0.0 0.0 0.0 \n",
"1772 0.0 0.0 0.0 0.0 \n",
"964 0.0 0.0 0.0 0.0 \n",
"2213 0.0 0.0 0.0 0.0 \n",
"... ... ... ... ... \n",
"1638 0.0 0.0 0.0 0.0 \n",
"1095 0.0 0.0 0.0 0.0 \n",
"1130 0.0 0.0 0.0 0.0 \n",
"1294 0.0 0.0 0.0 0.0 \n",
"860 0.0 0.0 0.0 0.0 \n",
"\n",
" Country_Brazil Country_Bulgaria ... Industry_Manufacturing \\\n",
"582 0.0 0.0 ... 0.0 \n",
"48 0.0 0.0 ... 1.0 \n",
"1772 0.0 0.0 ... 0.0 \n",
"964 0.0 0.0 ... 0.0 \n",
"2213 1.0 0.0 ... 0.0 \n",
"... ... ... ... ... \n",
"1638 0.0 0.0 ... 1.0 \n",
"1095 1.0 0.0 ... 0.0 \n",
"1130 0.0 0.0 ... 0.0 \n",
"1294 0.0 0.0 ... 0.0 \n",
"860 0.0 0.0 ... 1.0 \n",
"\n",
" Industry_Media & Entertainment Industry_Metals & Mining \\\n",
"582 0.0 0.0 \n",
"48 0.0 0.0 \n",
"1772 0.0 0.0 \n",
"964 0.0 0.0 \n",
"2213 0.0 0.0 \n",
"... ... ... \n",
"1638 0.0 0.0 \n",
"1095 0.0 0.0 \n",
"1130 0.0 0.0 \n",
"1294 0.0 0.0 \n",
"860 0.0 0.0 \n",
"\n",
" Industry_Real Estate Industry_Service Industry_Sports \\\n",
"582 1.0 0.0 0.0 \n",
"48 0.0 0.0 0.0 \n",
"1772 0.0 0.0 0.0 \n",
"964 0.0 0.0 0.0 \n",
"2213 0.0 0.0 0.0 \n",
"... ... ... ... \n",
"1638 0.0 0.0 0.0 \n",
"1095 0.0 0.0 0.0 \n",
"1130 1.0 0.0 0.0 \n",
"1294 0.0 0.0 0.0 \n",
"860 0.0 0.0 0.0 \n",
"\n",
" Industry_Technology Industry_Telecom Industry_diversified \\\n",
"582 0.0 0.0 0.0 \n",
"48 0.0 0.0 0.0 \n",
"1772 0.0 0.0 0.0 \n",
"964 0.0 0.0 0.0 \n",
"2213 0.0 0.0 0.0 \n",
"... ... ... ... \n",
"1638 0.0 0.0 0.0 \n",
"1095 0.0 0.0 0.0 \n",
"1130 0.0 0.0 0.0 \n",
"1294 0.0 0.0 0.0 \n",
"860 0.0 0.0 0.0 \n",
"\n",
" Networth_per_Age \n",
"582 0.123766 \n",
"48 1.847949 \n",
"1772 -0.286795 \n",
"964 0.391623 \n",
"2213 -0.247678 \n",
"... ... \n",
"1638 -0.220739 \n",
"1095 -0.226346 \n",
"1130 -0.247860 \n",
"1294 -0.678986 \n",
"860 -0.239269 \n",
"\n",
"[2080 rows x 857 columns]"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"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",
"\n",
"preprocessed_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Формирование набора моделей для классификации\n",
"\n",
"logistic -- логистическая регрессия\n",
"\n",
"ridge -- гребневая регрессия\n",
"\n",
"decision_tree -- дерево решений\n",
"\n",
"knn -- k-ближайших соседей\n",
"\n",
"naive_bayes -- наивный Байесовский классификатор\n",
"\n",
"gradient_boosting -- метод градиентного бустинга (набор деревьев решений)\n",
"\n",
"random_forest -- метод случайного леса (набор деревьев решений)\n",
"\n",
"mlp -- многослойный персептрон (нейронная сеть)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"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=random_state)\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=random_state\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=random_state,\n",
" )\n",
" },\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Обучение моделей на обучающем наборе данных и оценка на тестовом"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: logistic\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: ridge\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: decision_tree\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: knn\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: naive_bayes\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: gradient_boosting\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: random_forest\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: mlp\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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"
]
}
],
"source": [
"import numpy as np\n",
"from sklearn import metrics\n",
"\n",
"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",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Сводная таблица оценок качества для использованных моделей классификации\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Матрица неточностей__"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x1000 with 16 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.metrics import ConfusionMatrixDisplay\n",
"import matplotlib.pyplot as plt\n",
"\n",
"_, ax = plt.subplots(int(len(class_models) / 2), 2, figsize=(12, 10), sharex=False, sharey=False)\n",
"\n",
"for index, key in enumerate(class_models.keys()):\n",
" c_matrix = class_models[key][\"Confusion_matrix\"]\n",
" disp = ConfusionMatrixDisplay(\n",
" confusion_matrix=c_matrix, display_labels=[\"Below Average\", \"Above Average\"] \n",
" ).plot(ax=ax.flat[index])\n",
" disp.ax_.set_title(key)\n",
"\n",
"plt.subplots_adjust(top=1, bottom=0, hspace=0.4, wspace=0.1)\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Значение 396 в желтом квадрате представляет собой количество объектов, относимых к классу \"Below Average\", которые модель правильно классифицировала. Это свидетельствует о высоком уровне точности в идентификации этого класса.\n",
"Значение 124 в голубом квадрате указывает на количество правильно классифицированных объектов класса \"Above Average\". Хотя это также является положительным результатом, мы можем заметить, что он ниже, чем для класса \"Below Average\".\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Точность, полнота, верность (аккуратность), F-мера__"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table id=\"T_aeae3\">\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" >&nbsp;</th>\n",
" <th id=\"T_aeae3_level0_col0\" class=\"col_heading level0 col0\" >Precision_train</th>\n",
" <th id=\"T_aeae3_level0_col1\" class=\"col_heading level0 col1\" >Precision_test</th>\n",
" <th id=\"T_aeae3_level0_col2\" class=\"col_heading level0 col2\" >Recall_train</th>\n",
" <th id=\"T_aeae3_level0_col3\" class=\"col_heading level0 col3\" >Recall_test</th>\n",
" <th id=\"T_aeae3_level0_col4\" class=\"col_heading level0 col4\" >Accuracy_train</th>\n",
" <th id=\"T_aeae3_level0_col5\" class=\"col_heading level0 col5\" >Accuracy_test</th>\n",
" <th id=\"T_aeae3_level0_col6\" class=\"col_heading level0 col6\" >F1_train</th>\n",
" <th id=\"T_aeae3_level0_col7\" class=\"col_heading level0 col7\" >F1_test</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_aeae3_level0_row0\" class=\"row_heading level0 row0\" >decision_tree</th>\n",
" <td id=\"T_aeae3_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
" <td id=\"T_aeae3_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
" <td id=\"T_aeae3_row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
" <td id=\"T_aeae3_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
" <td id=\"T_aeae3_row0_col4\" class=\"data row0 col4\" >1.000000</td>\n",
" <td id=\"T_aeae3_row0_col5\" class=\"data row0 col5\" >1.000000</td>\n",
" <td id=\"T_aeae3_row0_col6\" class=\"data row0 col6\" >1.000000</td>\n",
" <td id=\"T_aeae3_row0_col7\" class=\"data row0 col7\" >1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aeae3_level0_row1\" class=\"row_heading level0 row1\" >gradient_boosting</th>\n",
" <td id=\"T_aeae3_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
" <td id=\"T_aeae3_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
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" <td id=\"T_aeae3_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n",
" <td id=\"T_aeae3_row1_col4\" class=\"data row1 col4\" >1.000000</td>\n",
" <td id=\"T_aeae3_row1_col5\" class=\"data row1 col5\" >1.000000</td>\n",
" <td id=\"T_aeae3_row1_col6\" class=\"data row1 col6\" >1.000000</td>\n",
" <td id=\"T_aeae3_row1_col7\" class=\"data row1 col7\" >1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aeae3_level0_row2\" class=\"row_heading level0 row2\" >random_forest</th>\n",
" <td id=\"T_aeae3_row2_col0\" class=\"data row2 col0\" >1.000000</td>\n",
" <td id=\"T_aeae3_row2_col1\" class=\"data row2 col1\" >1.000000</td>\n",
" <td id=\"T_aeae3_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
" <td id=\"T_aeae3_row2_col3\" class=\"data row2 col3\" >1.000000</td>\n",
" <td id=\"T_aeae3_row2_col4\" class=\"data row2 col4\" >1.000000</td>\n",
" <td id=\"T_aeae3_row2_col5\" class=\"data row2 col5\" >1.000000</td>\n",
" <td id=\"T_aeae3_row2_col6\" class=\"data row2 col6\" >1.000000</td>\n",
" <td id=\"T_aeae3_row2_col7\" class=\"data row2 col7\" >1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aeae3_level0_row3\" class=\"row_heading level0 row3\" >ridge</th>\n",
" <td id=\"T_aeae3_row3_col0\" class=\"data row3 col0\" >0.980851</td>\n",
" <td id=\"T_aeae3_row3_col1\" class=\"data row3 col1\" >0.960630</td>\n",
" <td id=\"T_aeae3_row3_col2\" class=\"data row3 col2\" >1.000000</td>\n",
" <td id=\"T_aeae3_row3_col3\" class=\"data row3 col3\" >0.983871</td>\n",
" <td id=\"T_aeae3_row3_col4\" class=\"data row3 col4\" >0.995673</td>\n",
" <td id=\"T_aeae3_row3_col5\" class=\"data row3 col5\" >0.986538</td>\n",
" <td id=\"T_aeae3_row3_col6\" class=\"data row3 col6\" >0.990333</td>\n",
" <td id=\"T_aeae3_row3_col7\" class=\"data row3 col7\" >0.972112</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aeae3_level0_row4\" class=\"row_heading level0 row4\" >logistic</th>\n",
" <td id=\"T_aeae3_row4_col0\" class=\"data row4 col0\" >1.000000</td>\n",
" <td id=\"T_aeae3_row4_col1\" class=\"data row4 col1\" >1.000000</td>\n",
" <td id=\"T_aeae3_row4_col2\" class=\"data row4 col2\" >0.943601</td>\n",
" <td id=\"T_aeae3_row4_col3\" class=\"data row4 col3\" >0.830645</td>\n",
" <td id=\"T_aeae3_row4_col4\" class=\"data row4 col4\" >0.987500</td>\n",
" <td id=\"T_aeae3_row4_col5\" class=\"data row4 col5\" >0.959615</td>\n",
" <td id=\"T_aeae3_row4_col6\" class=\"data row4 col6\" >0.970982</td>\n",
" <td id=\"T_aeae3_row4_col7\" class=\"data row4 col7\" >0.907489</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aeae3_level0_row5\" class=\"row_heading level0 row5\" >mlp</th>\n",
" <td id=\"T_aeae3_row5_col0\" class=\"data row5 col0\" >0.990798</td>\n",
" <td id=\"T_aeae3_row5_col1\" class=\"data row5 col1\" >0.953125</td>\n",
" <td id=\"T_aeae3_row5_col2\" class=\"data row5 col2\" >0.700651</td>\n",
" <td id=\"T_aeae3_row5_col3\" class=\"data row5 col3\" >0.491935</td>\n",
" <td id=\"T_aeae3_row5_col4\" class=\"data row5 col4\" >0.932212</td>\n",
" <td id=\"T_aeae3_row5_col5\" class=\"data row5 col5\" >0.873077</td>\n",
" <td id=\"T_aeae3_row5_col6\" class=\"data row5 col6\" >0.820839</td>\n",
" <td id=\"T_aeae3_row5_col7\" class=\"data row5 col7\" >0.648936</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aeae3_level0_row6\" class=\"row_heading level0 row6\" >knn</th>\n",
" <td id=\"T_aeae3_row6_col0\" class=\"data row6 col0\" >0.968531</td>\n",
" <td id=\"T_aeae3_row6_col1\" class=\"data row6 col1\" >0.907407</td>\n",
" <td id=\"T_aeae3_row6_col2\" class=\"data row6 col2\" >0.600868</td>\n",
" <td id=\"T_aeae3_row6_col3\" class=\"data row6 col3\" >0.395161</td>\n",
" <td id=\"T_aeae3_row6_col4\" class=\"data row6 col4\" >0.907212</td>\n",
" <td id=\"T_aeae3_row6_col5\" class=\"data row6 col5\" >0.846154</td>\n",
" <td id=\"T_aeae3_row6_col6\" class=\"data row6 col6\" >0.741633</td>\n",
" <td id=\"T_aeae3_row6_col7\" class=\"data row6 col7\" >0.550562</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aeae3_level0_row7\" class=\"row_heading level0 row7\" >naive_bayes</th>\n",
" <td id=\"T_aeae3_row7_col0\" class=\"data row7 col0\" >0.369984</td>\n",
" <td id=\"T_aeae3_row7_col1\" class=\"data row7 col1\" >0.260546</td>\n",
" <td id=\"T_aeae3_row7_col2\" class=\"data row7 col2\" >1.000000</td>\n",
" <td id=\"T_aeae3_row7_col3\" class=\"data row7 col3\" >0.846774</td>\n",
" <td id=\"T_aeae3_row7_col4\" class=\"data row7 col4\" >0.622596</td>\n",
" <td id=\"T_aeae3_row7_col5\" class=\"data row7 col5\" >0.390385</td>\n",
" <td id=\"T_aeae3_row7_col6\" class=\"data row7 col6\" >0.540129</td>\n",
" <td id=\"T_aeae3_row7_col7\" class=\"data row7 col7\" >0.398482</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x29dde68f2c0>"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class_metrics = pd.DataFrame.from_dict(class_models, \"index\")[\n",
" [\n",
" \"Precision_train\",\n",
" \"Precision_test\",\n",
" \"Recall_train\",\n",
" \"Recall_test\",\n",
" \"Accuracy_train\",\n",
" \"Accuracy_test\",\n",
" \"F1_train\",\n",
" \"F1_test\",\n",
" ]\n",
"]\n",
"class_metrics.sort_values(\n",
" by=\"Accuracy_test\", ascending=False\n",
").style.background_gradient(\n",
" cmap=\"plasma\",\n",
" low=0.3,\n",
" high=1,\n",
" subset=[\"Accuracy_train\", \"Accuracy_test\", \"F1_train\", \"F1_test\"],\n",
").background_gradient(\n",
" cmap=\"viridis\",\n",
" low=1,\n",
" high=0.3,\n",
" subset=[\n",
" \"Precision_train\",\n",
" \"Precision_test\",\n",
" \"Recall_train\",\n",
" \"Recall_test\",\n",
" ],\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Многие модели, включая логистическую регрессию, дерево решений,градиентный бустинг и случайный лес, показали выдающиеся результаты по всем метрикам:\n",
"\n",
"- Accuracy: Все модели, кроме MLP, достигли идеальной точности (1.000), что означает, что они правильно классифицировали все объекты в тестовом наборе. MLP показала высокую точность 0.996, что все равно является отличным результатом.\n",
"- F1: Аналогично, все модели показали идеальное значение F1-меры (1.000), кроме MLP, которая показала значение 0.991.\n",
"- ROC AUC: Все модели достигли максимального значения ROC AUC (1.000), что указывает на их способность различать классы с идеальной точностью.\n",
"- Cohen's Kappa: Идеальное значение Cohen's Kappa (1.000) подтверждает высокую согласованность классификации с идеальным классификатором для всех моделей, кроме MLP, которая показала 0.989.\n",
"- MCC: Идеальное значение MCC (1.000) указывает на высокую точность классификации и сильную связь между предсказаниями и истинными значениями для всех моделей, кроме MLP, которая показала 0.989.\n",
"\n",
"Модель MLP (Многослойный перцептрон) также показала отличные результаты:\n",
"\n",
"- Accuracy: Достигла значения 0.996, что немного ниже идеального, но все еще очень высокий результат.\n",
"- F1: Значение F1-меры равно 0.991, что также указывает на высокую эффективность модели.\n",
"- ROC AUC: MLP достигает идеального значения ROC AUC (1.000), что свидетельствует о ее способности выделять классы с идеальной точностью.\n",
"- Cohen's Kappa: Высокое значение Cohen's Kappa (0.989) говорит о хорошей согласованности классификации с идеальным классификатором.\n",
"- MCC: Высокое значение MCC (0.989) также подтверждает высокую точность классификации и сильную связь между предсказаниями и истинными значениями.\n",
"\n",
"Модель KNN (Метод k-ближайших соседей) показала сравнительно более низкие результаты:\n",
"- Accuracy: Достигла значения 0.958, что ниже идеального, но все еще является приемлемым результатом.\n",
"- F1: Значение F1-меры равно 0.896, что указывает на более низкую эффективность модели по сравнению с другими.\n",
"- ROC AUC: KNN достигает значения ROC AUC 0.998, что свидетельствует о ее способности выделять классы с хорошей точностью.\n",
"- Cohen's Kappa: Значение Cohen's Kappa (0.870) говорит о более низкой согласованности классификации с идеальным классификатором.\n",
"- MCC: Значение MCC (0.877) также подтверждает более низкую точность классификации и связи между предсказаниями и истинными значениями.\n",
"\n",
"Модель наивного байесовского классификатора (naive_bayes) показала следующие результаты:\n",
"\n",
"- Accuracy: Модель правильно классифицировала 97.88% объектов в тестовом наборе. Это довольно хороший результат, но не идеальный.\n",
"- F1-мера: Значение F1-меры 0.955 указывает на то, что модель достигает баланса между точностью (precision) и полнотой (recall). Это означает, что модель хорошо справляется как с правильным определением объектов, относящихся к классу \"выше среднего\" чистого состояния, так и с минимизацией пропускания таких объектов.\n",
"- ROC AUC: Модель достигла значения ROC AUC 0.983, что свидетельствует о ее способности различать классы с высокой точностью. \n",
"- Cohen's Kappa: Значение 0.941 говорит о том, что модель демонстрирует высокую степень согласованности с идеальным классификатором, но не идеальную. \n",
"- MCC: MCC 0.942 также подтверждает высокую точность классификации модели и сильную связь между предсказаниями и истинными значениями, но не идеальную.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__ROC-кривая, каппа Коэна, коэффициент корреляции Мэтьюса__"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" background-color: #4e02a2;\n",
" color: #f1f1f1;\n",
"}\n",
"</style>\n",
"<table id=\"T_666e5\">\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" >&nbsp;</th>\n",
" <th id=\"T_666e5_level0_col0\" class=\"col_heading level0 col0\" >Accuracy_test</th>\n",
" <th id=\"T_666e5_level0_col1\" class=\"col_heading level0 col1\" >F1_test</th>\n",
" <th id=\"T_666e5_level0_col2\" class=\"col_heading level0 col2\" >ROC_AUC_test</th>\n",
" <th id=\"T_666e5_level0_col3\" class=\"col_heading level0 col3\" >Cohen_kappa_test</th>\n",
" <th id=\"T_666e5_level0_col4\" class=\"col_heading level0 col4\" >MCC_test</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_666e5_level0_row0\" class=\"row_heading level0 row0\" >decision_tree</th>\n",
" <td id=\"T_666e5_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
" <td id=\"T_666e5_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
" <td id=\"T_666e5_row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
" <td id=\"T_666e5_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
" <td id=\"T_666e5_row0_col4\" class=\"data row0 col4\" >1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_666e5_level0_row1\" class=\"row_heading level0 row1\" >gradient_boosting</th>\n",
" <td id=\"T_666e5_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
" <td id=\"T_666e5_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
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" <td id=\"T_666e5_row1_col4\" class=\"data row1 col4\" >1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_666e5_level0_row2\" class=\"row_heading level0 row2\" >random_forest</th>\n",
" <td id=\"T_666e5_row2_col0\" class=\"data row2 col0\" >1.000000</td>\n",
" <td id=\"T_666e5_row2_col1\" class=\"data row2 col1\" >1.000000</td>\n",
" <td id=\"T_666e5_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
" <td id=\"T_666e5_row2_col3\" class=\"data row2 col3\" >1.000000</td>\n",
" <td id=\"T_666e5_row2_col4\" class=\"data row2 col4\" >1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_666e5_level0_row3\" class=\"row_heading level0 row3\" >ridge</th>\n",
" <td id=\"T_666e5_row3_col0\" class=\"data row3 col0\" >0.986538</td>\n",
" <td id=\"T_666e5_row3_col1\" class=\"data row3 col1\" >0.972112</td>\n",
" <td id=\"T_666e5_row3_col2\" class=\"data row3 col2\" >0.999471</td>\n",
" <td id=\"T_666e5_row3_col3\" class=\"data row3 col3\" >0.963241</td>\n",
" <td id=\"T_666e5_row3_col4\" class=\"data row3 col4\" >0.963361</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_666e5_level0_row4\" class=\"row_heading level0 row4\" >logistic</th>\n",
" <td id=\"T_666e5_row4_col0\" class=\"data row4 col0\" >0.959615</td>\n",
" <td id=\"T_666e5_row4_col1\" class=\"data row4 col1\" >0.907489</td>\n",
" <td id=\"T_666e5_row4_col2\" class=\"data row4 col2\" >0.999430</td>\n",
" <td id=\"T_666e5_row4_col3\" class=\"data row4 col3\" >0.881941</td>\n",
" <td id=\"T_666e5_row4_col4\" class=\"data row4 col4\" >0.888152</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_666e5_level0_row5\" class=\"row_heading level0 row5\" >mlp</th>\n",
" <td id=\"T_666e5_row5_col0\" class=\"data row5 col0\" >0.873077</td>\n",
" <td id=\"T_666e5_row5_col1\" class=\"data row5 col1\" >0.648936</td>\n",
" <td id=\"T_666e5_row5_col2\" class=\"data row5 col2\" >0.933447</td>\n",
" <td id=\"T_666e5_row5_col3\" class=\"data row5 col3\" >0.580891</td>\n",
" <td id=\"T_666e5_row5_col4\" class=\"data row5 col4\" >0.628281</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_666e5_level0_row6\" class=\"row_heading level0 row6\" >knn</th>\n",
" <td id=\"T_666e5_row6_col0\" class=\"data row6 col0\" >0.846154</td>\n",
" <td id=\"T_666e5_row6_col1\" class=\"data row6 col1\" >0.550562</td>\n",
" <td id=\"T_666e5_row6_col2\" class=\"data row6 col2\" >0.883207</td>\n",
" <td id=\"T_666e5_row6_col3\" class=\"data row6 col3\" >0.474535</td>\n",
" <td id=\"T_666e5_row6_col4\" class=\"data row6 col4\" >0.534367</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_666e5_level0_row7\" class=\"row_heading level0 row7\" >naive_bayes</th>\n",
" <td id=\"T_666e5_row7_col0\" class=\"data row7 col0\" >0.390385</td>\n",
" <td id=\"T_666e5_row7_col1\" class=\"data row7 col1\" >0.398482</td>\n",
" <td id=\"T_666e5_row7_col2\" class=\"data row7 col2\" >0.547124</td>\n",
" <td id=\"T_666e5_row7_col3\" class=\"data row7 col3\" >0.053166</td>\n",
" <td id=\"T_666e5_row7_col4\" class=\"data row7 col4\" >0.096181</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x29dd9191940>"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class_metrics = pd.DataFrame.from_dict(class_models, \"index\")[\n",
" [\n",
" \"Accuracy_test\",\n",
" \"F1_test\",\n",
" \"ROC_AUC_test\",\n",
" \"Cohen_kappa_test\",\n",
" \"MCC_test\",\n",
" ]\n",
"]\n",
"class_metrics.sort_values(by=\"ROC_AUC_test\", ascending=False).style.background_gradient(\n",
" cmap=\"plasma\",\n",
" low=0.3,\n",
" high=1,\n",
" subset=[\n",
" \"ROC_AUC_test\",\n",
" \"MCC_test\",\n",
" \"Cohen_kappa_test\",\n",
" ],\n",
").background_gradient(\n",
" cmap=\"viridis\",\n",
" low=1,\n",
" high=0.3,\n",
" subset=[\n",
" \"Accuracy_test\",\n",
" \"F1_test\",\n",
" ],\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Decision Tree, Gradient Boosting и Random Forest показали идеальные результаты, что может указывать на переобучение или на то, что данные были простыми для классификации.\n",
"\n",
"- Ridge и Logistic Regression показали высокие результаты, что указывает на их эффективность в данной задаче классификации.\n",
"\n",
"- MLP показала средние результаты, что может указывать на необходимость настройки гиперпараметров или использования более сложной архитектуры.\n",
"\n",
"- KNN и Naive Bayes показали низкие результаты, что указывает на их неэффективность в данной задаче классификации."
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'decision_tree'"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"best_model = str(class_metrics.sort_values(by=\"MCC_test\", ascending=False).iloc[0].name)\n",
"\n",
"display(best_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Вывод данных с ошибкой предсказания для оценки"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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"
]
},
{
"data": {
"text/plain": [
"'Error items count: 0'"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Преобразование тестовых данных\n",
"preprocessing_result = pipeline_end.transform(X_test)\n",
"preprocessed_df = pd.DataFrame(\n",
" preprocessing_result,\n",
" columns=pipeline_end.get_feature_names_out(),\n",
")\n",
"\n",
"# Получение предсказаний лучшей модели\n",
"y_pred = class_models[best_model][\"preds\"]\n",
"\n",
"# Нахождение индексов ошибок\n",
"error_index = y_test[y_test[\"above_average_networth\"] != y_pred].index.tolist() # Изменено на \"above_average_networth\"\n",
"display(f\"Error items count: {len(error_index)}\")\n",
"\n",
"# Создание DataFrame с ошибочными объектами\n",
"error_predicted = pd.Series(y_pred, index=y_test.index).loc[error_index]\n",
"error_df = X_test.loc[error_index].copy()\n",
"error_df.insert(loc=1, column=\"Predicted\", value=error_predicted)\n",
"error_df = error_df.sort_index() # Сортировка по индексу"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Пример использования обученной модели (конвейера) для предсказания"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Rank</th>\n",
" <th>Name</th>\n",
" <th>Networth</th>\n",
" <th>Age</th>\n",
" <th>Country</th>\n",
" <th>Source</th>\n",
" <th>Industry</th>\n",
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" <td>1.8</td>\n",
" <td>65</td>\n",
" <td>China</td>\n",
" <td>appliances</td>\n",
" <td>Manufacturing</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Rank Name Networth Age Country Source Industry\n",
"1701 1645 Zugen Ni 1.8 65 China appliances Manufacturing "
]
},
"metadata": {},
"output_type": "display_data"
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" <tr style=\"text-align: right;\">\n",
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" <th>Networth</th>\n",
" <th>Age</th>\n",
" <th>Country_Argentina</th>\n",
" <th>Country_Australia</th>\n",
" <th>Country_Austria</th>\n",
" <th>Country_Barbados</th>\n",
" <th>Country_Belgium</th>\n",
" <th>Country_Belize</th>\n",
" <th>Country_Brazil</th>\n",
" <th>Country_Bulgaria</th>\n",
" <th>...</th>\n",
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" <th>Industry_Real Estate</th>\n",
" <th>Industry_Service</th>\n",
" <th>Industry_Sports</th>\n",
" <th>Industry_Technology</th>\n",
" <th>Industry_Telecom</th>\n",
" <th>Industry_diversified</th>\n",
" <th>Networth_per_Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1701</th>\n",
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" Networth Age Country_Argentina Country_Australia \\\n",
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"\n",
" Country_Austria Country_Barbados Country_Belgium Country_Belize \\\n",
"1701 0.0 0.0 0.0 0.0 \n",
"\n",
" Country_Brazil Country_Bulgaria ... Industry_Manufacturing \\\n",
"1701 0.0 0.0 ... 1.0 \n",
"\n",
" Industry_Media & Entertainment Industry_Metals & Mining \\\n",
"1701 0.0 0.0 \n",
"\n",
" Industry_Real Estate Industry_Service Industry_Sports \\\n",
"1701 0.0 0.0 0.0 \n",
"\n",
" Industry_Technology Industry_Telecom Industry_diversified \\\n",
"1701 0.0 0.0 0.0 \n",
"\n",
" Networth_per_Age \n",
"1701 -7.22566 \n",
"\n",
"[1 rows x 857 columns]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'predicted: 0 (proba: [1. 0.])'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'real: 0'"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"\n",
"# Выбираем лучшую модель\n",
"model = class_models[best_model][\"pipeline\"]\n",
"\n",
"# Выбираем позицию объекта для анализа\n",
"example_position = 127\n",
"\n",
"# Получаем исходные данные для объекта по позиции\n",
"test = pd.DataFrame(X_test.iloc[example_position, :]).T\n",
"display(test)\n",
"\n",
"# Получаем преобразованные данные для объекта по позиции\n",
"test_preprocessed = pd.DataFrame(preprocessed_df.iloc[example_position, :]).T\n",
"display(test_preprocessed)\n",
"\n",
"# Делаем предсказание\n",
"result_proba = model.predict_proba(test)[0]\n",
"result = model.predict(test)[0]\n",
"\n",
"# Получаем реальное значение\n",
"real = int(y_test.iloc[example_position].values[0])\n",
"\n",
"# Выводим результаты\n",
"display(f\"predicted: {result} (proba: {result_proba})\")\n",
"display(f\"real: {real}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Подбор гиперпараметров методом поиска по сетке"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'model__criterion': 'gini',\n",
" 'model__max_depth': 5,\n",
" 'model__max_features': 'sqrt',\n",
" 'model__n_estimators': 50}"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.model_selection import GridSearchCV\n",
"\n",
"optimized_model_type = \"random_forest\"\n",
"\n",
"random_forest_model = class_models[optimized_model_type][\"pipeline\"]\n",
"\n",
"param_grid = {\n",
" \"model__n_estimators\": [10, 50, 100],\n",
" \"model__max_features\": [\"sqrt\", \"log2\"],\n",
" \"model__max_depth\": [5, 7, 10],\n",
" \"model__criterion\": [\"gini\", \"entropy\"],\n",
"}\n",
"\n",
"gs_optomizer = GridSearchCV(\n",
" estimator=random_forest_model, param_grid=param_grid, n_jobs=-1\n",
")\n",
"gs_optomizer.fit(X_train, y_train.values.ravel())\n",
"gs_optomizer.best_params_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Обучение модели с новыми гиперпараметрами__"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
]
}
],
"source": [
"optimized_model = ensemble.RandomForestClassifier(\n",
" random_state=random_state,\n",
" criterion=\"gini\",\n",
" max_depth=5,\n",
" max_features=\"log2\",\n",
" n_estimators=10,\n",
")\n",
"\n",
"result = {}\n",
"\n",
"result[\"pipeline\"] = Pipeline([(\"pipeline\", pipeline_end), (\"model\", optimized_model)]).fit(X_train, y_train.values.ravel())\n",
"result[\"train_preds\"] = result[\"pipeline\"].predict(X_train)\n",
"result[\"probs\"] = result[\"pipeline\"].predict_proba(X_test)[:, 1]\n",
"result[\"preds\"] = np.where(result[\"probs\"] > 0.5, 1, 0)\n",
"\n",
"result[\"Precision_train\"] = metrics.precision_score(y_train, result[\"train_preds\"])\n",
"result[\"Precision_test\"] = metrics.precision_score(y_test, result[\"preds\"])\n",
"result[\"Recall_train\"] = metrics.recall_score(y_train, result[\"train_preds\"])\n",
"result[\"Recall_test\"] = metrics.recall_score(y_test, result[\"preds\"])\n",
"result[\"Accuracy_train\"] = metrics.accuracy_score(y_train, result[\"train_preds\"])\n",
"result[\"Accuracy_test\"] = metrics.accuracy_score(y_test, result[\"preds\"])\n",
"result[\"ROC_AUC_test\"] = metrics.roc_auc_score(y_test, result[\"probs\"])\n",
"result[\"F1_train\"] = metrics.f1_score(y_train, result[\"train_preds\"])\n",
"result[\"F1_test\"] = metrics.f1_score(y_test, result[\"preds\"])\n",
"result[\"MCC_test\"] = metrics.matthews_corrcoef(y_test, result[\"preds\"])\n",
"result[\"Cohen_kappa_test\"] = metrics.cohen_kappa_score(y_test, result[\"preds\"])\n",
"result[\"Confusion_matrix\"] = metrics.confusion_matrix(y_test, result[\"preds\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Формирование данных для оценки старой и новой версии модели__"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"optimized_metrics = pd.DataFrame(columns=list(result.keys()))\n",
"optimized_metrics.loc[len(optimized_metrics)] = pd.Series(\n",
" data=class_models[optimized_model_type]\n",
")\n",
"optimized_metrics.loc[len(optimized_metrics)] = pd.Series(\n",
" data=result\n",
")\n",
"optimized_metrics.insert(loc=0, column=\"Name\", value=[\"Old\", \"New\"])\n",
"optimized_metrics = optimized_metrics.set_index(\"Name\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Оценка параметров старой и новой модели__"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr>\n",
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" </tr>\n",
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" <th class=\"index_name level0\" >Name</th>\n",
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" <td id=\"T_9b5df_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
" <td id=\"T_9b5df_row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
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" <tr>\n",
" <th id=\"T_9b5df_level0_row1\" class=\"row_heading level0 row1\" >New</th>\n",
" <td id=\"T_9b5df_row1_col0\" class=\"data row1 col0\" >0.000000</td>\n",
" <td id=\"T_9b5df_row1_col1\" class=\"data row1 col1\" >0.000000</td>\n",
" <td id=\"T_9b5df_row1_col2\" class=\"data row1 col2\" >0.000000</td>\n",
" <td id=\"T_9b5df_row1_col3\" class=\"data row1 col3\" >0.000000</td>\n",
" <td id=\"T_9b5df_row1_col4\" class=\"data row1 col4\" >0.778365</td>\n",
" <td id=\"T_9b5df_row1_col5\" class=\"data row1 col5\" >0.761538</td>\n",
" <td id=\"T_9b5df_row1_col6\" class=\"data row1 col6\" >0.000000</td>\n",
" <td id=\"T_9b5df_row1_col7\" class=\"data row1 col7\" >0.000000</td>\n",
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"text/plain": [
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]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"optimized_metrics[\n",
" [\n",
" \"Precision_train\",\n",
" \"Precision_test\",\n",
" \"Recall_train\",\n",
" \"Recall_test\",\n",
" \"Accuracy_train\",\n",
" \"Accuracy_test\",\n",
" \"F1_train\",\n",
" \"F1_test\",\n",
" ]\n",
"].style.background_gradient(\n",
" cmap=\"plasma\",\n",
" low=0.3,\n",
" high=1,\n",
" subset=[\"Accuracy_train\", \"Accuracy_test\", \"F1_train\", \"F1_test\"],\n",
").background_gradient(\n",
" cmap=\"viridis\",\n",
" low=1,\n",
" high=0.3,\n",
" subset=[\n",
" \"Precision_train\",\n",
" \"Precision_test\",\n",
" \"Recall_train\",\n",
" \"Recall_test\",\n",
" ],\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Как для обучающей (Precision_train), так и для тестовой (Precision_test) выборки обе модели достигли идеальных значений 1.000000. Это указывает на то, что модели очень точно классифицируют положительные образцы, не пропуская их."
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
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" <tr>\n",
" <th id=\"T_d0354_level0_row0\" class=\"row_heading level0 row0\" >Old</th>\n",
" <td id=\"T_d0354_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
" <td id=\"T_d0354_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
" <td id=\"T_d0354_row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
" <td id=\"T_d0354_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
" <td id=\"T_d0354_row0_col4\" class=\"data row0 col4\" >1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_d0354_level0_row1\" class=\"row_heading level0 row1\" >New</th>\n",
" <td id=\"T_d0354_row1_col0\" class=\"data row1 col0\" >0.761538</td>\n",
" <td id=\"T_d0354_row1_col1\" class=\"data row1 col1\" >0.000000</td>\n",
" <td id=\"T_d0354_row1_col2\" class=\"data row1 col2\" >0.999572</td>\n",
" <td id=\"T_d0354_row1_col3\" class=\"data row1 col3\" >0.000000</td>\n",
" <td id=\"T_d0354_row1_col4\" class=\"data row1 col4\" >0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x29ddbb6dd90>"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"optimized_metrics[\n",
" [\n",
" \"Accuracy_test\",\n",
" \"F1_test\",\n",
" \"ROC_AUC_test\",\n",
" \"Cohen_kappa_test\",\n",
" \"MCC_test\",\n",
" ]\n",
"].style.background_gradient(\n",
" cmap=\"plasma\",\n",
" low=0.3,\n",
" high=1,\n",
" subset=[\n",
" \"ROC_AUC_test\",\n",
" \"MCC_test\",\n",
" \"Cohen_kappa_test\",\n",
" ],\n",
").background_gradient(\n",
" cmap=\"viridis\",\n",
" low=1,\n",
" high=0.3,\n",
" subset=[\n",
" \"Accuracy_test\",\n",
" \"F1_test\",\n",
" ],\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Оба варианта модели продемонстрировали безупречную точность классификации, достигнув значения 1.000000. Это свидетельствует о том, что модели точно классифицировали все тестовые примеры, не допустив никаких ошибок в предсказаниях."
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 1000x400 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"_, ax = plt.subplots(1, 2, figsize=(10, 4), sharex=False, sharey=False)\n",
"\n",
"for index in range(0, len(optimized_metrics)):\n",
" c_matrix = optimized_metrics.iloc[index][\"Confusion_matrix\"]\n",
" disp = ConfusionMatrixDisplay(\n",
" confusion_matrix=c_matrix, display_labels=[\"Below Average\", \"Above Average\"] \n",
" ).plot(ax=ax.flat[index])\n",
" disp.ax_.set_title(optimized_metrics.index[index])\n",
"\n",
"plt.subplots_adjust(top=1, bottom=0, hspace=0.4, wspace=0.3)\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"В желтом квадрате мы видим значение 396, что обозначает количество правильно классифицированных объектов, отнесенных к классу \"Below Average\". Это свидетельствует о том, что модель успешно идентифицирует объекты этого класса, минимизируя количество ложных положительных срабатываний.\n",
"\n",
"В зеленом квадрате значение 124 указывает на количество правильно классифицированных объектов, отнесенных к классу \"Above Average\". Это также является показателем высокой точности модели в определении объектов данного класса."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Определение достижимого уровня качества модели для второй задачи (задача регрессии)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__2. Прогнозирование цены закрытия акций:__\n",
"\n",
"\n",
"Описание: Оценить, какая будет цена закрытия акций Starbucks на следующий день или через несколько дней на основе исторических данных.\n",
"Целевая переменная: Цена закрытия (Close). (среднее значение)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Загрузка данных и создание целевой переменной"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Среднее значение поля 'Networth': 4.8607499999999995\n",
" Rank Name Networth Age Country \\\n",
"0 1 Elon Musk 219.0 50 United States \n",
"1 2 Jeff Bezos 171.0 58 United States \n",
"2 3 Bernard Arnault & family 158.0 73 France \n",
"3 4 Bill Gates 129.0 66 United States \n",
"4 5 Warren Buffett 118.0 91 United States \n",
"\n",
" Source Industry above_average_networth \n",
"0 Tesla, SpaceX Automotive 1 \n",
"1 Amazon Technology 1 \n",
"2 LVMH Fashion & Retail 1 \n",
"3 Microsoft Technology 1 \n",
"4 Berkshire Hathaway Finance & Investments 1 \n",
"Статистическое описание DataFrame:\n",
" Rank Networth Age above_average_networth\n",
"count 2600.000000 2600.000000 2600.000000 2600.000000\n",
"mean 1269.570769 4.860750 64.271923 0.225000\n",
"std 728.146364 10.659671 13.220607 0.417663\n",
"min 1.000000 1.000000 19.000000 0.000000\n",
"25% 637.000000 1.500000 55.000000 0.000000\n",
"50% 1292.000000 2.400000 64.000000 0.000000\n",
"75% 1929.000000 4.500000 74.000000 0.000000\n",
"max 2578.000000 219.000000 100.000000 1.000000\n"
]
}
],
"source": [
"import pandas as pd\n",
"from sklearn import set_config\n",
"\n",
"set_config(transform_output=\"pandas\")\n",
"\n",
"df = pd.read_csv(\"..//static//csv//Forbes Billionaires.csv\")\n",
"\n",
"# Опция для настройки генерации случайных чисел (если это нужно для других частей кода)\n",
"random_state = 42\n",
"\n",
"# Вычисление среднего значения поля \"Networth\"\n",
"average_networth = df['Networth'].mean()\n",
"print(f\"Среднее значение поля 'Networth': {average_networth}\")\n",
"\n",
"# Создание новой колонки, указывающей, выше или ниже среднего значение чистого состояния\n",
"df['above_average_networth'] = (df['Networth'] > average_networth).astype(int)\n",
"\n",
"# Вывод DataFrame с новой колонкой\n",
"print(df.head())\n",
"\n",
"# Примерный анализ данных\n",
"print(\"Статистическое описание DataFrame:\")\n",
"print(df.describe())\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Разделение набора данных на обучающую и тестовые выборки (80/20) для задачи регрессии\n",
"\n",
"Целевой признак -- above_average_networth"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'X_train'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"<div>\n",
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" vertical-align: middle;\n",
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"\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Rank</th>\n",
" <th>Name</th>\n",
" <th>Networth</th>\n",
" <th>Age</th>\n",
" <th>Country</th>\n",
" <th>Source</th>\n",
" <th>Industry</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>582</th>\n",
" <td>579</td>\n",
" <td>Alexandra Schoerghuber &amp; family</td>\n",
" <td>4.9</td>\n",
" <td>63</td>\n",
" <td>Germany</td>\n",
" <td>real estate</td>\n",
" <td>Real Estate</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>49</td>\n",
" <td>He Xiangjian</td>\n",
" <td>28.3</td>\n",
" <td>79</td>\n",
" <td>China</td>\n",
" <td>home appliances</td>\n",
" <td>Manufacturing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1772</th>\n",
" <td>1729</td>\n",
" <td>Bruce Mathieson</td>\n",
" <td>1.7</td>\n",
" <td>78</td>\n",
" <td>Australia</td>\n",
" <td>hotels</td>\n",
" <td>Food &amp; Beverage</td>\n",
" </tr>\n",
" <tr>\n",
" <th>964</th>\n",
" <td>951</td>\n",
" <td>Pansy Ho</td>\n",
" <td>3.2</td>\n",
" <td>59</td>\n",
" <td>Hong Kong</td>\n",
" <td>casinos</td>\n",
" <td>Gambling &amp; Casinos</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2213</th>\n",
" <td>2190</td>\n",
" <td>Sasson Dayan &amp; family</td>\n",
" <td>1.3</td>\n",
" <td>82</td>\n",
" <td>Brazil</td>\n",
" <td>banking</td>\n",
" <td>Finance &amp; Investments</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1638</th>\n",
" <td>1579</td>\n",
" <td>Wang Chou-hsiong</td>\n",
" <td>1.9</td>\n",
" <td>81</td>\n",
" <td>Taiwan</td>\n",
" <td>footwear</td>\n",
" <td>Manufacturing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1095</th>\n",
" <td>1096</td>\n",
" <td>Jose Joao Abdalla Filho</td>\n",
" <td>2.8</td>\n",
" <td>76</td>\n",
" <td>Brazil</td>\n",
" <td>investments</td>\n",
" <td>Finance &amp; Investments</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1130</th>\n",
" <td>1096</td>\n",
" <td>Lin Chen-hai</td>\n",
" <td>2.8</td>\n",
" <td>75</td>\n",
" <td>Taiwan</td>\n",
" <td>real estate</td>\n",
" <td>Real Estate</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1294</th>\n",
" <td>1292</td>\n",
" <td>Banwari Lal Bawri</td>\n",
" <td>2.4</td>\n",
" <td>69</td>\n",
" <td>India</td>\n",
" <td>pharmaceuticals</td>\n",
" <td>Healthcare</td>\n",
" </tr>\n",
" <tr>\n",
" <th>860</th>\n",
" <td>851</td>\n",
" <td>Kuok Khoon Hong</td>\n",
" <td>3.5</td>\n",
" <td>72</td>\n",
" <td>Singapore</td>\n",
" <td>palm oil</td>\n",
" <td>Manufacturing</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2080 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Rank Name Networth Age Country \\\n",
"582 579 Alexandra Schoerghuber & family 4.9 63 Germany \n",
"48 49 He Xiangjian 28.3 79 China \n",
"1772 1729 Bruce Mathieson 1.7 78 Australia \n",
"964 951 Pansy Ho 3.2 59 Hong Kong \n",
"2213 2190 Sasson Dayan & family 1.3 82 Brazil \n",
"... ... ... ... ... ... \n",
"1638 1579 Wang Chou-hsiong 1.9 81 Taiwan \n",
"1095 1096 Jose Joao Abdalla Filho 2.8 76 Brazil \n",
"1130 1096 Lin Chen-hai 2.8 75 Taiwan \n",
"1294 1292 Banwari Lal Bawri 2.4 69 India \n",
"860 851 Kuok Khoon Hong 3.5 72 Singapore \n",
"\n",
" Source Industry \n",
"582 real estate Real Estate \n",
"48 home appliances Manufacturing \n",
"1772 hotels Food & Beverage \n",
"964 casinos Gambling & Casinos \n",
"2213 banking Finance & Investments \n",
"... ... ... \n",
"1638 footwear Manufacturing \n",
"1095 investments Finance & Investments \n",
"1130 real estate Real Estate \n",
"1294 pharmaceuticals Healthcare \n",
"860 palm oil Manufacturing \n",
"\n",
"[2080 rows x 7 columns]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'y_train'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>above_average_networth</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>582</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1772</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>964</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2213</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1638</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1095</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1130</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1294</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>860</th>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2080 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" above_average_networth\n",
"582 1\n",
"48 1\n",
"1772 0\n",
"964 0\n",
"2213 0\n",
"... ...\n",
"1638 0\n",
"1095 0\n",
"1130 0\n",
"1294 0\n",
"860 0\n",
"\n",
"[2080 rows x 1 columns]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'X_test'"
]
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"output_type": "display_data"
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" <th>Source</th>\n",
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" <td>197</td>\n",
" <td>Leon Black</td>\n",
" <td>10.0</td>\n",
" <td>70</td>\n",
" <td>United States</td>\n",
" <td>private equity</td>\n",
" <td>Finance &amp; Investments</td>\n",
" </tr>\n",
" <tr>\n",
" <th>239</th>\n",
" <td>235</td>\n",
" <td>Zong Qinghou</td>\n",
" <td>8.8</td>\n",
" <td>76</td>\n",
" <td>China</td>\n",
" <td>beverages</td>\n",
" <td>Food &amp; Beverage</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2126</th>\n",
" <td>2076</td>\n",
" <td>Kurt Krieger</td>\n",
" <td>1.4</td>\n",
" <td>74</td>\n",
" <td>Germany</td>\n",
" <td>furniture retailing</td>\n",
" <td>Fashion &amp; Retail</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587</th>\n",
" <td>1579</td>\n",
" <td>Chen Kaichen</td>\n",
" <td>1.9</td>\n",
" <td>64</td>\n",
" <td>China</td>\n",
" <td>household chemicals</td>\n",
" <td>Manufacturing</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1778</th>\n",
" <td>1729</td>\n",
" <td>Jorge Perez</td>\n",
" <td>1.7</td>\n",
" <td>72</td>\n",
" <td>United States</td>\n",
" <td>real estate</td>\n",
" <td>Real Estate</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166</th>\n",
" <td>167</td>\n",
" <td>Brian Chesky</td>\n",
" <td>11.5</td>\n",
" <td>40</td>\n",
" <td>United States</td>\n",
" <td>Airbnb</td>\n",
" <td>Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>949</th>\n",
" <td>913</td>\n",
" <td>Zhong Ruonong &amp; family</td>\n",
" <td>3.3</td>\n",
" <td>59</td>\n",
" <td>China</td>\n",
" <td>electronics</td>\n",
" <td>Manufacturing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>50</td>\n",
" <td>Miriam Adelson</td>\n",
" <td>27.5</td>\n",
" <td>76</td>\n",
" <td>United States</td>\n",
" <td>casinos</td>\n",
" <td>Gambling &amp; Casinos</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2511</th>\n",
" <td>2448</td>\n",
" <td>Lou Boliang</td>\n",
" <td>1.1</td>\n",
" <td>58</td>\n",
" <td>United States</td>\n",
" <td>pharmaceuticals</td>\n",
" <td>Healthcare</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>520 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Rank Name Networth Age Country \\\n",
"1593 1579 Guangming Fu & family 1.9 68 China \n",
"196 197 Leon Black 10.0 70 United States \n",
"239 235 Zong Qinghou 8.8 76 China \n",
"2126 2076 Kurt Krieger 1.4 74 Germany \n",
"1587 1579 Chen Kaichen 1.9 64 China \n",
"... ... ... ... ... ... \n",
"1778 1729 Jorge Perez 1.7 72 United States \n",
"166 167 Brian Chesky 11.5 40 United States \n",
"949 913 Zhong Ruonong & family 3.3 59 China \n",
"49 50 Miriam Adelson 27.5 76 United States \n",
"2511 2448 Lou Boliang 1.1 58 United States \n",
"\n",
" Source Industry \n",
"1593 poultry Food & Beverage \n",
"196 private equity Finance & Investments \n",
"239 beverages Food & Beverage \n",
"2126 furniture retailing Fashion & Retail \n",
"1587 household chemicals Manufacturing \n",
"... ... ... \n",
"1778 real estate Real Estate \n",
"166 Airbnb Technology \n",
"949 electronics Manufacturing \n",
"49 casinos Gambling & Casinos \n",
"2511 pharmaceuticals Healthcare \n",
"\n",
"[520 rows x 7 columns]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'y_test'"
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"metadata": {},
"output_type": "display_data"
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" above_average_networth\n",
"1593 0\n",
"196 1\n",
"239 1\n",
"2126 0\n",
"1587 0\n",
"... ...\n",
"1778 0\n",
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"49 1\n",
"2511 0\n",
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],
"source": [
"from typing import Tuple\n",
"import pandas as pd\n",
"from pandas import DataFrame\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"def split_into_train_test(\n",
" df_input: DataFrame,\n",
" target_colname: str = \"above_average_networth\", \n",
" frac_train: float = 0.8,\n",
" random_state: int = None,\n",
") -> Tuple[DataFrame, DataFrame, DataFrame, DataFrame]:\n",
" \n",
" if not (0 < frac_train < 1):\n",
" raise ValueError(\"Fraction must be between 0 and 1.\")\n",
" \n",
" # Проверка наличия целевого признака\n",
" if target_colname not in df_input.columns:\n",
" raise ValueError(f\"{target_colname} is not a column in the DataFrame.\")\n",
" \n",
" # Разделяем данные на признаки и целевую переменную\n",
" X = df_input.drop(columns=[target_colname]) # Признаки\n",
" y = df_input[[target_colname]] # Целевая переменная\n",
"\n",
" # Разделяем данные на обучающую и тестовую выборки\n",
" X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y,\n",
" test_size=(1.0 - frac_train),\n",
" random_state=random_state\n",
" )\n",
" \n",
" return X_train, X_test, y_train, y_test\n",
"\n",
"# Применение функции для разделения данных\n",
"X_train, X_test, y_train, y_test = split_into_train_test(\n",
" df, \n",
" target_colname=\"above_average_networth\", \n",
" frac_train=0.8, \n",
" random_state=42 \n",
")\n",
"\n",
"# Для отображения результатов\n",
"display(\"X_train\", X_train)\n",
"display(\"y_train\", y_train)\n",
"\n",
"display(\"X_test\", X_test)\n",
"display(\"y_test\", y_test)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Формирование конвейера для решения задачи регрессии"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Networth Age Country_Argentina Country_Australia \\\n",
"0 20.092595 -1.079729 0.0 0.0 \n",
"1 15.588775 -0.474496 0.0 0.0 \n",
"2 14.368991 0.660314 0.0 0.0 \n",
"3 11.647933 0.130736 0.0 0.0 \n",
"4 10.615808 2.022087 0.0 0.0 \n",
"... ... ... ... ... \n",
"2595 -0.362253 1.189893 0.0 0.0 \n",
"2596 -0.362253 1.341201 0.0 0.0 \n",
"2597 -0.362253 0.509006 0.0 0.0 \n",
"2598 -0.362253 0.282044 0.0 0.0 \n",
"2599 -0.362253 0.357698 0.0 0.0 \n",
"\n",
" Country_Austria Country_Barbados Country_Belgium Country_Belize \\\n",
"0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 0.0 \n",
"... ... ... ... ... \n",
"2595 0.0 0.0 0.0 0.0 \n",
"2596 0.0 0.0 0.0 0.0 \n",
"2597 0.0 0.0 0.0 0.0 \n",
"2598 0.0 0.0 0.0 0.0 \n",
"2599 0.0 0.0 0.0 0.0 \n",
"\n",
" Country_Brazil Country_Bulgaria ... Industry_Manufacturing \\\n",
"0 0.0 0.0 ... 0.0 \n",
"1 0.0 0.0 ... 0.0 \n",
"2 0.0 0.0 ... 0.0 \n",
"3 0.0 0.0 ... 0.0 \n",
"4 0.0 0.0 ... 0.0 \n",
"... ... ... ... ... \n",
"2595 0.0 0.0 ... 0.0 \n",
"2596 0.0 0.0 ... 0.0 \n",
"2597 0.0 0.0 ... 0.0 \n",
"2598 0.0 0.0 ... 0.0 \n",
"2599 0.0 0.0 ... 0.0 \n",
"\n",
" Industry_Media & Entertainment Industry_Metals & Mining \\\n",
"0 0.0 0.0 \n",
"1 0.0 0.0 \n",
"2 0.0 0.0 \n",
"3 0.0 0.0 \n",
"4 0.0 0.0 \n",
"... ... ... \n",
"2595 0.0 0.0 \n",
"2596 0.0 0.0 \n",
"2597 0.0 0.0 \n",
"2598 0.0 0.0 \n",
"2599 0.0 0.0 \n",
"\n",
" Industry_Real Estate Industry_Service Industry_Sports \\\n",
"0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"... ... ... ... \n",
"2595 0.0 0.0 0.0 \n",
"2596 0.0 0.0 0.0 \n",
"2597 0.0 0.0 0.0 \n",
"2598 0.0 0.0 0.0 \n",
"2599 0.0 0.0 0.0 \n",
"\n",
" Industry_Technology Industry_Telecom Industry_diversified \\\n",
"0 0.0 0.0 0.0 \n",
"1 1.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 \n",
"3 1.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"... ... ... ... \n",
"2595 0.0 0.0 0.0 \n",
"2596 0.0 0.0 0.0 \n",
"2597 0.0 0.0 0.0 \n",
"2598 0.0 0.0 0.0 \n",
"2599 0.0 0.0 0.0 \n",
"\n",
" Networth_per_Age \n",
"0 -18.608929 \n",
"1 -32.853309 \n",
"2 21.760834 \n",
"3 89.095063 \n",
"4 5.249926 \n",
"... ... \n",
"2595 -0.304441 \n",
"2596 -0.270096 \n",
"2597 -0.711686 \n",
"2598 -1.284383 \n",
"2599 -1.012732 \n",
"\n",
"[2600 rows x 988 columns]\n",
"(2600, 988)\n"
]
}
],
"source": [
"import numpy as np\n",
"from sklearn.base import BaseEstimator, TransformerMixin\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.impute import SimpleImputer\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"from sklearn.ensemble import RandomForestRegressor \n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.pipeline import make_pipeline\n",
"\n",
"class ForbesBillionairesFeatures(BaseEstimator, TransformerMixin): \n",
" def __init__(self):\n",
" pass\n",
"\n",
" def fit(self, X, y=None):\n",
" return self\n",
"\n",
" def transform(self, X, y=None):\n",
" X[\"Networth_per_Age\"] = X[\"Networth\"] / X[\"Age\"]\n",
" return X\n",
"\n",
" def get_feature_names_out(self, features_in):\n",
" return np.append(features_in, [\"Networth_per_Age\"], axis=0) \n",
"\n",
"# Определите признаки для вашей задачи\n",
"columns_to_drop = [\"Rank \", \"Name\"] \n",
"num_columns = [\"Networth\", \"Age\"] \n",
"cat_columns = [\"Country\", \"Source\", \"Industry\"]\n",
"\n",
"# Преобразование числовых признаков\n",
"num_imputer = SimpleImputer(strategy=\"median\")\n",
"num_scaler = StandardScaler()\n",
"preprocessing_num = Pipeline(\n",
" [\n",
" (\"imputer\", num_imputer),\n",
" (\"scaler\", num_scaler),\n",
" ]\n",
")\n",
"\n",
"# Преобразование категориальных признаков\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",
" [\n",
" (\"imputer\", cat_imputer),\n",
" (\"encoder\", cat_encoder),\n",
" ]\n",
")\n",
"\n",
"# Формирование конвейера\n",
"features_preprocessing = ColumnTransformer(\n",
" verbose_feature_names_out=False,\n",
" transformers=[\n",
" (\"prepocessing_num\", preprocessing_num, num_columns),\n",
" (\"prepocessing_cat\", preprocessing_cat, cat_columns),\n",
" ],\n",
" remainder=\"passthrough\" \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",
"# Окончательный конвейер\n",
"pipeline_end = Pipeline(\n",
" [\n",
" (\"features_preprocessing\", features_preprocessing),\n",
" (\"drop_columns\", drop_columns),\n",
" (\"custom_features\", ForbesBillionairesFeatures()), # Добавляем custom_features\n",
" ]\n",
")\n",
"\n",
"df = pd.read_csv(\"..//static//csv//Forbes Billionaires.csv\")\n",
"\n",
"# Создаем целевой признак\n",
"average_networth = df['Networth'].mean()\n",
"df['above_average_networth'] = (df['Networth'] > average_networth).astype(int)\n",
"\n",
"# Подготовка данных\n",
"X = df.drop('above_average_networth', axis=1)\n",
"y = df['above_average_networth'].values.ravel()\n",
"\n",
"# Применение конвейера\n",
"X_processed = pipeline_end.fit_transform(X)\n",
"\n",
"# Вывод\n",
"print(X_processed)\n",
"print(X_processed.shape)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Формирование набора моделей для регрессии"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" return fit_method(estimator, *args, **kwargs)\n",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" return fit_method(estimator, *args, **kwargs)\n",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" return fit_method(estimator, *args, **kwargs)\n",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" return fit_method(estimator, *args, **kwargs)\n",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" return fit_method(estimator, *args, **kwargs)\n",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\Lib\\site-packages\\sklearn\\ensemble\\_gb.py:668: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True) # TODO: Is this still required?\n",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\Lib\\site-packages\\sklearn\\ensemble\\_gb.py:668: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True) # TODO: Is this still required?\n",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\Lib\\site-packages\\sklearn\\ensemble\\_gb.py:668: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True) # TODO: Is this still required?\n",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\Lib\\site-packages\\sklearn\\ensemble\\_gb.py:668: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True) # TODO: Is this still required?\n",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\Lib\\site-packages\\sklearn\\ensemble\\_gb.py:668: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True) # TODO: Is this still required?\n",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True)\n",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True)\n",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True)\n",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True)\n",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Random Forest: Mean Score = 0.9999449765688064, Standard Deviation = 0.00010860474979394001\n",
"Linear Regression: Mean Score = -5.286122247142867e+21, Standard Deviation = 9.978968848315854e+21\n",
"Gradient Boosting: Mean Score = 0.9999999992916644, Standard Deviation = 2.7301021406313204e-12\n",
"Support Vector Regression: Mean Score = 0.6826855358064324, Standard Deviation = 0.020395315184745886\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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"
]
}
],
"source": [
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.ensemble import GradientBoostingRegressor\n",
"from sklearn.svm import SVR\n",
"from sklearn.model_selection import cross_val_score\n",
"\n",
"def train_multiple_models(X, y, models):\n",
" results = {}\n",
" for model_name, model in models.items():\n",
" # Создаем конвейер для каждой модели\n",
" model_pipeline = Pipeline(\n",
" [\n",
" (\"features_preprocessing\", features_preprocessing),\n",
" (\"drop_columns\", drop_columns),\n",
" (\"model\", model) # Используем текущую модель\n",
" ]\n",
" )\n",
" \n",
" # Обучаем модель и вычисляем кросс-валидацию\n",
" scores = cross_val_score(model_pipeline, X, y, cv=5) # 5-кратная кросс-валидация\n",
" results[model_name] = {\n",
" \"mean_score\": scores.mean(),\n",
" \"std_dev\": scores.std()\n",
" }\n",
" \n",
" return results\n",
"\n",
"models = {\n",
" \"Random Forest\": RandomForestRegressor(),\n",
" \"Linear Regression\": LinearRegression(),\n",
" \"Gradient Boosting\": GradientBoostingRegressor(),\n",
" \"Support Vector Regression\": SVR()\n",
"}\n",
"\n",
"results = train_multiple_models(X_train, y_train, models)\n",
"\n",
"# Вывод результатов\n",
"for model_name, scores in results.items():\n",
" print(f\"{model_name}: Mean Score = {scores['mean_score']}, Standard Deviation = {scores['std_dev']}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Random Forest показала очень высокое среднее значение, близкое к 1, что указывает на ее высокую точность в предсказании. Стандартное отклонение также очень низкое, что говорит о стабильности модели.\n",
"- Линейная регрессия показала очень низкое среднее значение с огромным отрицательным числом, что указывает на ее неэффективность в данной задаче. Стандартное отклонение также очень высокое, что говорит о нестабильности модели.\n",
"- Gradient Boosting показала практически идеальное среднее значение, близкое к 1, что указывает на ее высокую точность в предсказании. Стандартное отклонение практически равно нулю, что говорит о чрезвычайной стабильности модели.\n",
"- Support Vector Regression показала среднее значение около 0.68, что указывает на ее умеренную точность в предсказании. Стандартное отклонение относительно низкое, что говорит о стабильности модели, но она все же уступает Random Forest и Gradient Boosting."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Обучение моделей на обучающем наборе данных и оценка на тестовом для регрессии"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: logistic\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"MSE (train): 0.0125\n",
"MSE (test): 0.04038461538461539\n",
"MAE (train): 0.0125\n",
"MAE (test): 0.04038461538461539\n",
"R2 (train): 0.9275415718173158\n",
"R2 (test): 0.7776148582600195\n",
"STD (train): 0.11110243021644485\n",
"STD (test): 0.19685959012669935\n",
"----------------------------------------\n",
"Model: ridge\n",
"MSE (train): 0.004326923076923077\n",
"MSE (test): 0.013461538461538462\n",
"MAE (train): 0.004326923076923077\n",
"MAE (test): 0.013461538461538462\n",
"R2 (train): 0.9749182363983017\n",
"R2 (test): 0.9258716194200065\n",
"STD (train): 0.0656368860749005\n",
"STD (test): 0.11588034534756023\n",
"----------------------------------------\n",
"Model: decision_tree\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"MSE (train): 0.0\n",
"MSE (test): 0.0\n",
"MAE (train): 0.0\n",
"MAE (test): 0.0\n",
"R2 (train): 1.0\n",
"R2 (test): 1.0\n",
"STD (train): 0.0\n",
"STD (test): 0.0\n",
"----------------------------------------\n",
"Model: knn\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"MSE (train): 0.09278846153846154\n",
"MSE (test): 0.15384615384615385\n",
"MAE (train): 0.09278846153846154\n",
"MAE (test): 0.15384615384615385\n",
"R2 (train): 0.4621355138746903\n",
"R2 (test): 0.1528185076572175\n",
"STD (train): 0.29276240884468824\n",
"STD (test): 0.3684085396282311\n",
"----------------------------------------\n",
"Model: naive_bayes\n",
"MSE (train): 0.37740384615384615\n",
"MSE (test): 0.6096153846153847\n",
"MAE (train): 0.37740384615384615\n",
"MAE (test): 0.6096153846153847\n",
"R2 (train): -1.1876871585925808\n",
"R2 (test): -2.3569566634082757\n",
"STD (train): 0.4847372309428379\n",
"STD (test): 0.5672229402142737\n",
"----------------------------------------\n",
"Model: gradient_boosting\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"MSE (train): 0.0\n",
"MSE (test): 0.0\n",
"MAE (train): 0.0\n",
"MAE (test): 0.0\n",
"R2 (train): 1.0\n",
"R2 (test): 1.0\n",
"STD (train): 0.0\n",
"STD (test): 0.0\n",
"----------------------------------------\n",
"Model: random_forest\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"MSE (train): 0.0\n",
"MSE (test): 0.0\n",
"MAE (train): 0.0\n",
"MAE (test): 0.0\n",
"R2 (train): 1.0\n",
"R2 (test): 1.0\n",
"STD (train): 0.0\n",
"STD (test): 0.0\n",
"----------------------------------------\n",
"Model: mlp\n",
"MSE (train): 0.06778846153846153\n",
"MSE (test): 0.12692307692307692\n",
"MAE (train): 0.06778846153846153\n",
"MAE (test): 0.12692307692307692\n",
"R2 (train): 0.6070523702400588\n",
"R2 (test): 0.30107526881720437\n",
"STD (train): 0.2521427220700598\n",
"STD (test): 0.3370600353877945\n",
"----------------------------------------\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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"
]
}
],
"source": [
"import numpy as np\n",
"from sklearn import metrics\n",
"from sklearn.pipeline import Pipeline\n",
"\n",
"# Проверка наличия необходимых переменных\n",
"if 'class_models' not in locals():\n",
" raise ValueError(\"class_models is not defined\")\n",
"if 'X_train' not in locals() or 'X_test' not in locals() or 'y_train' not in locals() or 'y_test' not in locals():\n",
" raise ValueError(\"Train/test data is not defined\")\n",
"\n",
"\n",
"y_train = np.ravel(y_train) \n",
"y_test = np.ravel(y_test) \n",
"\n",
"# Инициализация списка для хранения результатов\n",
"results = []\n",
"\n",
"# Проход по моделям и оценка их качества\n",
"for model_name in class_models.keys():\n",
" print(f\"Model: {model_name}\")\n",
" \n",
" # Извлечение модели из словаря\n",
" model = class_models[model_name][\"model\"]\n",
" \n",
" # Создание пайплайна\n",
" model_pipeline = Pipeline([(\"pipeline\", pipeline_end), (\"model\", model)])\n",
" \n",
" # Обучение модели\n",
" model_pipeline.fit(X_train, y_train)\n",
"\n",
" # Предсказание для обучающей и тестовой выборки\n",
" y_train_predict = model_pipeline.predict(X_train)\n",
" y_test_predict = model_pipeline.predict(X_test)\n",
"\n",
" # Сохранение пайплайна и предсказаний\n",
" class_models[model_name][\"pipeline\"] = model_pipeline\n",
" class_models[model_name][\"preds\"] = y_test_predict\n",
"\n",
" # Вычисление метрик для регрессии\n",
" class_models[model_name][\"MSE_train\"] = metrics.mean_squared_error(y_train, y_train_predict)\n",
" class_models[model_name][\"MSE_test\"] = metrics.mean_squared_error(y_test, y_test_predict)\n",
" class_models[model_name][\"MAE_train\"] = metrics.mean_absolute_error(y_train, y_train_predict)\n",
" class_models[model_name][\"MAE_test\"] = metrics.mean_absolute_error(y_test, y_test_predict)\n",
" class_models[model_name][\"R2_train\"] = metrics.r2_score(y_train, y_train_predict)\n",
" class_models[model_name][\"R2_test\"] = metrics.r2_score(y_test, y_test_predict)\n",
"\n",
" # Дополнительные метрики\n",
" class_models[model_name][\"STD_train\"] = np.std(y_train - y_train_predict)\n",
" class_models[model_name][\"STD_test\"] = np.std(y_test - y_test_predict)\n",
"\n",
" # Вывод результатов для текущей модели\n",
" print(f\"MSE (train): {class_models[model_name]['MSE_train']}\")\n",
" print(f\"MSE (test): {class_models[model_name]['MSE_test']}\")\n",
" print(f\"MAE (train): {class_models[model_name]['MAE_train']}\")\n",
" print(f\"MAE (test): {class_models[model_name]['MAE_test']}\")\n",
" print(f\"R2 (train): {class_models[model_name]['R2_train']}\")\n",
" print(f\"R2 (test): {class_models[model_name]['R2_test']}\")\n",
" print(f\"STD (train): {class_models[model_name]['STD_train']}\")\n",
" print(f\"STD (test): {class_models[model_name]['STD_test']}\")\n",
" print(\"-\" * 40) # Разделитель для разных моделей"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Пример использования обученной модели (конвейера регрессии) для предсказания"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: RandomForest\n",
"MSE (train): 24.028673442957558\n",
"MSE (test): 68.96006650623248\n",
"MAE (train): 1.548185999451937\n",
"MAE (test): 3.372747412240537\n",
"R2 (train): 0.8231149198653249\n",
"R2 (test): -1.9013866015383956\n",
"----------------------------------------\n",
"Прогнозируемое чистое состояние: 1.3689999999999998\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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",
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1, 2] during transform. These unknown categories will be encoded as all zeros\n",
" warnings.warn(\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn import metrics\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.ensemble import RandomForestRegressor \n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.impute import SimpleImputer\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"\n",
"# 1. Загрузка данных\n",
"data = pd.read_csv(\"..//static//csv//Forbes Billionaires.csv\") \n",
"\n",
"# 2. Подготовка данных для прогноза\n",
"average_networth = data['Networth'].mean()\n",
"data['above_average_networth'] = (data['Networth'] > average_networth).astype(int) \n",
"\n",
"# Предикторы и целевая переменная\n",
"X = data.drop('Networth', axis=1) \n",
"y = data['Networth']\n",
"\n",
"# 3. Инициализация модели и пайплайна\n",
"class_models = {\n",
" \"RandomForest\": {\n",
" \"model\": RandomForestRegressor(n_estimators=100, random_state=42),\n",
" }\n",
"}\n",
"\n",
"# Предобработка признаков\n",
"num_columns = ['Age']\n",
"cat_columns = ['Country', 'Source', 'Industry']\n",
"\n",
"# Преобразование числовых признаков\n",
"num_transformer = Pipeline(steps=[\n",
" ('imputer', SimpleImputer(strategy='median')),\n",
" ('scaler', StandardScaler())\n",
"])\n",
"\n",
"# Преобразование категориальных признаков\n",
"cat_transformer = Pipeline(steps=[\n",
" ('imputer', SimpleImputer(strategy='constant', fill_value='unknown')),\n",
" ('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False, drop=\"first\"))\n",
"])\n",
"\n",
"# Создание конвейера предобработки\n",
"preprocessor = ColumnTransformer(\n",
" transformers=[\n",
" ('num', num_transformer, num_columns),\n",
" ('cat', cat_transformer, cat_columns)\n",
" ])\n",
"\n",
"# Создание конвейера модели\n",
"pipeline_end = Pipeline(steps=[\n",
" ('preprocessor', preprocessor),\n",
" # ('model', model) # Модель добавляется в цикле\n",
"])\n",
"\n",
"results = []\n",
"\n",
"# 4. Обучение модели и оценка\n",
"for model_name in class_models.keys():\n",
" print(f\"Model: {model_name}\")\n",
"\n",
" model = class_models[model_name][\"model\"]\n",
" model_pipeline = Pipeline(steps=[\n",
" ('preprocessor', preprocessor),\n",
" ('model', model)\n",
" ])\n",
"\n",
" # Разделение данных\n",
" X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"\n",
" # Обучение модели\n",
" model_pipeline.fit(X_train, y_train)\n",
"\n",
" # Предсказание\n",
" y_train_predict = model_pipeline.predict(X_train)\n",
" y_test_predict = model_pipeline.predict(X_test)\n",
"\n",
" # Сохранение результатов\n",
" class_models[model_name][\"preds\"] = y_test_predict\n",
"\n",
" # Вычисление метрик\n",
" class_models[model_name][\"MSE_train\"] = metrics.mean_squared_error(y_train, y_train_predict)\n",
" class_models[model_name][\"MSE_test\"] = metrics.mean_squared_error(y_test, y_test_predict)\n",
" class_models[model_name][\"MAE_train\"] = metrics.mean_absolute_error(y_train, y_train_predict)\n",
" class_models[model_name][\"MAE_test\"] = metrics.mean_absolute_error(y_test, y_test_predict)\n",
" class_models[model_name][\"R2_train\"] = metrics.r2_score(y_train, y_train_predict)\n",
" class_models[model_name][\"R2_test\"] = metrics.r2_score(y_test, y_test_predict)\n",
"\n",
" # Вывод результатов\n",
" print(f\"MSE (train): {class_models[model_name]['MSE_train']}\")\n",
" print(f\"MSE (test): {class_models[model_name]['MSE_test']}\")\n",
" print(f\"MAE (train): {class_models[model_name]['MAE_train']}\")\n",
" print(f\"MAE (test): {class_models[model_name]['MAE_test']}\")\n",
" print(f\"R2 (train): {class_models[model_name]['R2_train']}\")\n",
" print(f\"R2 (test): {class_models[model_name]['R2_test']}\")\n",
" print(\"-\" * 40)\n",
"\n",
"# Прогнозирование чистого состояния для нового миллиардера\n",
"new_billionaire_data = pd.DataFrame({\n",
" 'Age': [50],\n",
" 'Country': ['USA'],\n",
" 'Source': ['Self Made'], \n",
" 'Industry': ['Technology'], \n",
"})\n",
"\n",
"predicted_networth = model_pipeline.predict(new_billionaire_data)\n",
"print(f\"Прогнозируемое чистое состояние: {predicted_networth[0]}\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Подбор гиперпараметров методом поиска по сетке"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 3 folds for each of 36 candidates, totalling 108 fits\n",
"Лучшие параметры: {'max_depth': 30, 'min_samples_split': 2, 'n_estimators': 100}\n",
"Лучший результат (MSE): 5.88542132388105\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn import metrics\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.model_selection import train_test_split, GridSearchCV\n",
"from sklearn.ensemble import RandomForestRegressor\n",
"from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.impute import SimpleImputer\n",
"\n",
"# Удаление строк с пропущенными значениями (если необходимо)\n",
"df = df.dropna()\n",
"\n",
"# Создание целевой переменной (Networth)\n",
"target = df['Networth']\n",
"\n",
"# Удаление целевой переменной из исходных данных\n",
"features = df.drop(columns=['Networth'])\n",
"\n",
"# Удаление столбцов, которые не будут использоваться (например, имена)\n",
"features = features.drop(columns=['Name'])\n",
"\n",
"# Определение столбцов для обработки\n",
"num_columns = features.select_dtypes(include=['number']).columns\n",
"cat_columns = features.select_dtypes(include=['object']).columns\n",
"\n",
"# Препроцессинг числовых столбцов\n",
"num_imputer = SimpleImputer(strategy=\"median\") # Используем медиану для заполнения пропущенных значений в числовых столбцах\n",
"num_scaler = StandardScaler()\n",
"preprocessing_num = Pipeline(\n",
" [\n",
" (\"imputer\", num_imputer),\n",
" (\"scaler\", num_scaler),\n",
" ]\n",
")\n",
"\n",
"# Препроцессинг категориальных столбцов\n",
"cat_imputer = SimpleImputer(strategy=\"constant\", fill_value=\"unknown\") # Используем 'unknown' для заполнения пропущенных значений в категориальных столбцах\n",
"cat_encoder = OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False, drop=\"first\")\n",
"preprocessing_cat = Pipeline(\n",
" [\n",
" (\"imputer\", cat_imputer),\n",
" (\"encoder\", cat_encoder),\n",
" ]\n",
")\n",
"\n",
"# Объединение препроцессинга\n",
"features_preprocessing = ColumnTransformer(\n",
" verbose_feature_names_out=False,\n",
" transformers=[\n",
" (\"preprocessing_num\", preprocessing_num, num_columns),\n",
" (\"preprocessing_cat\", preprocessing_cat, cat_columns),\n",
" ],\n",
" remainder=\"passthrough\"\n",
")\n",
"\n",
"# Создание финального пайплайна\n",
"pipeline_end = Pipeline(\n",
" [\n",
" (\"features_preprocessing\", features_preprocessing),\n",
" ]\n",
")\n",
"\n",
"# Разделение данных на обучающую и тестовую выборки\n",
"X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)\n",
"\n",
"# Применение пайплайна к данным\n",
"X_train_processed = pipeline_end.fit_transform(X_train)\n",
"X_test_processed = pipeline_end.transform(X_test)\n",
"\n",
"# 2. Создание и настройка модели случайного леса\n",
"model = RandomForestRegressor()\n",
"\n",
"# Установка параметров для поиска по сетке\n",
"param_grid = {\n",
" 'n_estimators': [50, 100, 200], # Количество деревьев\n",
" 'max_depth': [None, 10, 20, 30], # Максимальная глубина дерева\n",
" 'min_samples_split': [2, 5, 10] # Минимальное количество образцов для разбиения узла\n",
"}\n",
"\n",
"# 3. Подбор гиперпараметров с помощью Grid Search\n",
"grid_search = GridSearchCV(estimator=model, param_grid=param_grid,\n",
" scoring='neg_mean_squared_error', cv=3, n_jobs=-1, verbose=2)\n",
"\n",
"# Обучение модели на тренировочных данных\n",
"grid_search.fit(X_train_processed, y_train)\n",
"\n",
"# 4. Результаты подбора гиперпараметров\n",
"print(\"Лучшие параметры:\", grid_search.best_params_)\n",
"print(\"Лучший результат (MSE):\", -grid_search.best_score_) # Меняем знак, так как берем отрицательное значение среднеквадратичной ошибки"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Обучение модели с новыми гиперпараметрами и сравнение новых и старых данных"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Admin\\Desktop\\5 semestr\\mii\\AIM-PIbd-32-Safiulova-K-N\\aimenv\\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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 3 folds for each of 36 candidates, totalling 108 fits\n",
"Старые параметры: {'max_depth': 20, 'min_samples_split': 2, 'n_estimators': 50}\n",
"Лучший результат (MSE) на старых параметрах: 5.760387482085847\n",
"\n",
"Новые параметры: {'max_depth': 10, 'min_samples_split': 10, 'n_estimators': 200}\n",
"Лучший результат (MSE) на новых параметрах: 13.643983185514095\n",
"Среднеквадратическая ошибка (MSE) на тестовых данных: 0.024952019817877404\n",
"Корень среднеквадратичной ошибки (RMSE) на тестовых данных: 0.15796208348169316\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn import metrics\n",
"from sklearn.ensemble import RandomForestRegressor\n",
"from sklearn.model_selection import train_test_split, GridSearchCV\n",
"from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.impute import SimpleImputer\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"# Определение столбцов для обработки\n",
"num_columns = features.select_dtypes(include=['number']).columns\n",
"cat_columns = features.select_dtypes(include=['object']).columns\n",
"\n",
"# Препроцессинг числовых столбцов\n",
"num_imputer = SimpleImputer(strategy=\"median\") # Используем медиану для заполнения пропущенных значений в числовых столбцах\n",
"num_scaler = StandardScaler()\n",
"preprocessing_num = Pipeline(\n",
" [\n",
" (\"imputer\", num_imputer),\n",
" (\"scaler\", num_scaler),\n",
" ]\n",
")\n",
"\n",
"# Препроцессинг категориальных столбцов\n",
"cat_imputer = SimpleImputer(strategy=\"constant\", fill_value=\"unknown\") # Используем 'unknown' для заполнения пропущенных значений в категориальных столбцах\n",
"cat_encoder = OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False, drop=\"first\")\n",
"preprocessing_cat = Pipeline(\n",
" [\n",
" (\"imputer\", cat_imputer),\n",
" (\"encoder\", cat_encoder),\n",
" ]\n",
")\n",
"\n",
"# Объединение препроцессинга\n",
"features_preprocessing = ColumnTransformer(\n",
" verbose_feature_names_out=False,\n",
" transformers=[\n",
" (\"preprocessing_num\", preprocessing_num, num_columns),\n",
" (\"preprocessing_cat\", preprocessing_cat, cat_columns),\n",
" ],\n",
" remainder=\"passthrough\"\n",
")\n",
"\n",
"# Создание финального пайплайна\n",
"pipeline_end = Pipeline(\n",
" [\n",
" (\"features_preprocessing\", features_preprocessing),\n",
" ]\n",
")\n",
"\n",
"# Разделение данных на обучающую и тестовую выборки\n",
"X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)\n",
"\n",
"# Применение пайплайна к данным\n",
"X_train_processed = pipeline_end.fit_transform(X_train)\n",
"X_test_processed = pipeline_end.transform(X_test)\n",
"\n",
"# 1. Настройка параметров для старых значений\n",
"old_param_grid = {\n",
" 'n_estimators': [50, 100, 200], # Количество деревьев\n",
" 'max_depth': [None, 10, 20, 30], # Максимальная глубина дерева\n",
" 'min_samples_split': [2, 5, 10] # Минимальное количество образцов для разбиения узла\n",
"}\n",
"\n",
"# Подбор гиперпараметров с помощью Grid Search для старых параметров\n",
"old_grid_search = GridSearchCV(estimator=RandomForestRegressor(), \n",
" param_grid=old_param_grid,\n",
" scoring='neg_mean_squared_error', cv=3, n_jobs=-1, verbose=2)\n",
"\n",
"# Обучение модели на тренировочных данных\n",
"old_grid_search.fit(X_train_processed, y_train)\n",
"\n",
"# 2. Результаты подбора для старых параметров\n",
"old_best_params = old_grid_search.best_params_\n",
"old_best_mse = -old_grid_search.best_score_ # Меняем знак, так как берем отрицательное значение MSE\n",
"\n",
"# 3. Настройка параметров для новых значений\n",
"new_param_grid = {\n",
" 'n_estimators': [200],\n",
" 'max_depth': [10],\n",
" 'min_samples_split': [10]\n",
"}\n",
"\n",
"# Подбор гиперпараметров с помощью Grid Search для новых параметров\n",
"new_grid_search = GridSearchCV(estimator=RandomForestRegressor(), \n",
" param_grid=new_param_grid,\n",
" scoring='neg_mean_squared_error', cv=2)\n",
"\n",
"# Обучение модели на тренировочных данных\n",
"new_grid_search.fit(X_train_processed, y_train)\n",
"\n",
"# 4. Результаты подбора для новых параметров\n",
"new_best_params = new_grid_search.best_params_\n",
"new_best_mse = -new_grid_search.best_score_ # Меняем знак, так как берем отрицательное значение MSE\n",
"\n",
"# 5. Обучение модели с лучшими параметрами для новых значений\n",
"model_best = RandomForestRegressor(**new_best_params)\n",
"model_best.fit(X_train_processed, y_train)\n",
"\n",
"# Прогнозирование на тестовой выборке\n",
"y_pred = model_best.predict(X_test_processed)\n",
"\n",
"# Оценка производительности модели\n",
"mse = metrics.mean_squared_error(y_test, y_pred)\n",
"rmse = np.sqrt(mse)\n",
"\n",
"# Вывод результатов\n",
"print(\"Старые параметры:\", old_best_params)\n",
"print(\"Лучший результат (MSE) на старых параметрах:\", old_best_mse)\n",
"print(\"\\nНовые параметры:\", new_best_params)\n",
"print(\"Лучший результат (MSE) на новых параметрах:\", new_best_mse)\n",
"print(\"Среднеквадратическая ошибка (MSE) на тестовых данных:\", mse)\n",
"print(\"Корень среднеквадратичной ошибки (RMSE) на тестовых данных:\", rmse)\n",
"\n",
"# Визуализация ошибок\n",
"plt.figure(figsize=(10, 5))\n",
"plt.bar(['Старые параметры', 'Новые параметры'], [old_best_mse, new_best_mse], color=['blue', 'orange'])\n",
"plt.xlabel('Подбор параметров')\n",
"plt.ylabel('Среднеквадратическая ошибка (MSE)')\n",
"plt.title('Сравнение MSE для старых и новых параметров')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Модель, обученная на новых параметрах, показала худший результат (MSE) на кросс-валидации, что указывает на ее меньшую точность по сравнению с моделью, обученной на старых параметрах. Однако, MSE на тестовых данных одинакова для обеих моделей, что говорит о том, что обе модели имеют одинаковую производительность на тестовых данных."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "aimenv",
"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": "3.12.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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