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lab3
2024-11-29 18:17:46 +04:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Вариант 4. Данные по инсультам"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['id', 'gender', 'age', 'hypertension', 'heart_disease', 'ever_married',\n",
" 'work_type', 'Residence_type', 'avg_glucose_level', 'bmi',\n",
" 'smoking_status', 'stroke'],\n",
" dtype='object')\n"
]
},
{
"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>id</th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" <th>hypertension</th>\n",
" <th>heart_disease</th>\n",
" <th>ever_married</th>\n",
" <th>work_type</th>\n",
" <th>Residence_type</th>\n",
" <th>avg_glucose_level</th>\n",
" <th>bmi</th>\n",
" <th>smoking_status</th>\n",
" <th>stroke</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9046</td>\n",
" <td>Male</td>\n",
" <td>67.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Yes</td>\n",
" <td>Private</td>\n",
" <td>Urban</td>\n",
" <td>228.69</td>\n",
" <td>36.6</td>\n",
" <td>formerly smoked</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>51676</td>\n",
" <td>Female</td>\n",
" <td>61.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Yes</td>\n",
" <td>Self-employed</td>\n",
" <td>Rural</td>\n",
" <td>202.21</td>\n",
" <td>NaN</td>\n",
" <td>never smoked</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>31112</td>\n",
" <td>Male</td>\n",
" <td>80.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Yes</td>\n",
" <td>Private</td>\n",
" <td>Rural</td>\n",
" <td>105.92</td>\n",
" <td>32.5</td>\n",
" <td>never smoked</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>60182</td>\n",
" <td>Female</td>\n",
" <td>49.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Yes</td>\n",
" <td>Private</td>\n",
" <td>Urban</td>\n",
" <td>171.23</td>\n",
" <td>34.4</td>\n",
" <td>smokes</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1665</td>\n",
" <td>Female</td>\n",
" <td>79.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>Yes</td>\n",
" <td>Self-employed</td>\n",
" <td>Rural</td>\n",
" <td>174.12</td>\n",
" <td>24.0</td>\n",
" <td>never smoked</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id gender age hypertension heart_disease ever_married \\\n",
"0 9046 Male 67.0 0 1 Yes \n",
"1 51676 Female 61.0 0 0 Yes \n",
"2 31112 Male 80.0 0 1 Yes \n",
"3 60182 Female 49.0 0 0 Yes \n",
"4 1665 Female 79.0 1 0 Yes \n",
"\n",
" work_type Residence_type avg_glucose_level bmi smoking_status \\\n",
"0 Private Urban 228.69 36.6 formerly smoked \n",
"1 Self-employed Rural 202.21 NaN never smoked \n",
"2 Private Rural 105.92 32.5 never smoked \n",
"3 Private Urban 171.23 34.4 smokes \n",
"4 Self-employed Rural 174.12 24.0 never smoked \n",
"\n",
" stroke \n",
"0 1 \n",
"1 1 \n",
"2 1 \n",
"3 1 \n",
"4 1 "
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from sklearn.model_selection import train_test_split\n",
"from imblearn.over_sampling import RandomOverSampler\n",
"from sklearn.preprocessing import StandardScaler\n",
"import featuretools as ft\n",
"import time\n",
"from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.model_selection import cross_val_score\n",
"\n",
"df = pd.read_csv(\"../data/healthcare-dataset-stroke-data.csv\")\n",
"\n",
"print(df.columns)\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Бизнес цели и цели технического проекта.\n",
"## Бизнес цели:\n",
"### 1. Предсказание инсульта: Разработать систему, которая сможет предсказать вероятность инсульта у пациентов на основе их медицинских и социальных данных. Это может помочь медицинским учреждениям и специалистам в более раннем выявлении пациентов с высоким риском.\n",
"### 2. Снижение затрат на лечение: Предупреждение инсультов у пациентов позволит снизить затраты на лечение и реабилитацию. Это также поможет улучшить качество медицинских услуг и повысить удовлетворенность пациентов.\n",
"### 3. Повышение эффективности профилактики: Выявление факторов риска инсульта на ранней стадии может способствовать более эффективному проведению профилактических мероприятий.\n",
"## Цели технического проекта:\n",
"### 1. Создание и обучение модели машинного обучения: Разработка модели, способной предсказать вероятность инсульта на основе данных о пациентах (например, возраст, уровень глюкозы, наличие сердечно-сосудистых заболеваний, тип работы, индекс массы тела и т.д.).\n",
"### 2. Анализ и обработка данных: Провести предобработку данных (очистка, заполнение пропущенных значений, кодирование категориальных признаков), чтобы улучшить качество и надежность модели.\n",
"### 3. Оценка модели: Использовать метрики, такие как точность, полнота и F1-мера, чтобы оценить эффективность модели и минимизировать риск ложных положительных и ложных отрицательных предсказаний."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"id 0\n",
"gender 0\n",
"age 0\n",
"hypertension 0\n",
"heart_disease 0\n",
"ever_married 0\n",
"work_type 0\n",
"Residence_type 0\n",
"avg_glucose_level 0\n",
"bmi 201\n",
"smoking_status 0\n",
"stroke 0\n",
"dtype: int64\n",
"\n",
"id False\n",
"gender False\n",
"age False\n",
"hypertension False\n",
"heart_disease False\n",
"ever_married False\n",
"work_type False\n",
"Residence_type False\n",
"avg_glucose_level False\n",
"bmi True\n",
"smoking_status False\n",
"stroke False\n",
"dtype: bool\n",
"\n",
"bmi процент пустых значений: %3.93\n"
]
}
],
"source": [
"print(df.isnull().sum())\n",
"print()\n",
"\n",
"print(df.isnull().any())\n",
"print()\n",
"\n",
"for i in df.columns:\n",
" null_rate = df[i].isnull().sum() / len(df) * 100\n",
" if null_rate > 0:\n",
" print(f\"{i} процент пустых значений: %{null_rate:.2f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Видим пустые значения в bmi, заменяем их"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Количество пустых значений в каждом столбце после замены:\n",
"id 0\n",
"gender 0\n",
"age 0\n",
"hypertension 0\n",
"heart_disease 0\n",
"ever_married 0\n",
"work_type 0\n",
"Residence_type 0\n",
"avg_glucose_level 0\n",
"bmi 0\n",
"smoking_status 0\n",
"stroke 0\n",
"dtype: int64\n"
]
}
],
"source": [
"df[\"bmi\"] = df[\"bmi\"].fillna(df[\"bmi\"].median())\n",
"\n",
"missing_values = df.isnull().sum()\n",
"\n",
"print(\"Количество пустых значений в каждом столбце после замены:\")\n",
"print(missing_values)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['gender', 'age', 'hypertension', 'heart_disease', 'ever_married',\n",
" 'work_type', 'Residence_type', 'avg_glucose_level', 'bmi',\n",
" 'smoking_status', 'stroke'],\n",
" dtype='object')\n"
]
}
],
"source": [
"df = df.drop('id', axis = 1)\n",
"print(df.columns)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Создаем выборки"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Размер обучающей выборки: (2503, 10)\n",
"Размер контрольной выборки: (1074, 10)\n",
"Размер тестовой выборки: (1533, 10)\n"
]
}
],
"source": [
"# Разделим данные на признак (X) и переменую (Y)\n",
"# Начнем со stroke\n",
"X = df.drop(columns=['stroke'])\n",
"y = df['stroke']\n",
"\n",
"# Разбиваем на обучающую и тестовую выборки\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
"\n",
"# Разбиваем на обучающую и контрольную выборки\n",
"X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.3)\n",
"\n",
"print(\"Размер обучающей выборки: \", X_train.shape)\n",
"print(\"Размер контрольной выборки: \", X_val.shape)\n",
"print(\"Размер тестовой выборки: \", X_test.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Оценим сбалансированность сборок"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Распределение классов в обучающей выборке:\n",
"stroke\n",
"0 0.94966\n",
"1 0.05034\n",
"Name: proportion, dtype: float64\n",
"\n",
"Распределение классов в контрольной выборке:\n",
"stroke\n",
"0 0.946927\n",
"1 0.053073\n",
"Name: proportion, dtype: float64\n",
"\n",
"Распределение классов в тестовой выборке:\n",
"stroke\n",
"0 0.956947\n",
"1 0.043053\n",
"Name: proportion, dtype: float64\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1800x500 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from locale import normalize\n",
"\n",
"\n",
"def analyze_balance(y_train, y_val, y_test, y_name):\n",
" print(\"Распределение классов в обучающей выборке:\")\n",
" print(y_train.value_counts(normalize=True))\n",
"\n",
" print(\"\\nРаспределение классов в контрольной выборке:\")\n",
" print(y_val.value_counts(normalize=True))\n",
"\n",
" print(\"\\nРаспределение классов в тестовой выборке:\")\n",
" print(y_test.value_counts(normalize=True))\n",
"\n",
" fig, axes = plt.subplots(1, 3, figsize=(18,5), sharey=True)\n",
" fig.suptitle('Распределение в различных выборках')\n",
"\n",
" sns.barplot(x=y_train.value_counts().index, y=y_train.value_counts(normalize=True), ax=axes[0])\n",
" axes[0].set_title('Обучающая выборка')\n",
" axes[0].set_xlabel(y_name)\n",
" axes[0].set_ylabel('Доля')\n",
"\n",
" sns.barplot(x=y_val.value_counts().index, y=y_val.value_counts(normalize=True), ax=axes[1])\n",
" axes[1].set_title('Контрольная выборка')\n",
" axes[1].set_xlabel(y_name)\n",
"\n",
" sns.barplot(x=y_test.value_counts().index, y=y_test.value_counts(normalize=True), ax=axes[2])\n",
" axes[2].set_title('Тестовая выборка')\n",
" axes[2].set_xlabel(y_name)\n",
"\n",
" plt.show()\n",
"\n",
"analyze_balance(y_train, y_val, y_test, 'stroke')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Заметим, что выборки не сбалансированы. Для балансировки будем использовать RandomOverSampler"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Распределение классов в обучающей выборке:\n",
"stroke\n",
"0 0.5\n",
"1 0.5\n",
"Name: proportion, dtype: float64\n",
"\n",
"Распределение классов в контрольной выборке:\n",
"stroke\n",
"0 0.5\n",
"1 0.5\n",
"Name: proportion, dtype: float64\n",
"\n",
"Распределение классов в тестовой выборке:\n",
"stroke\n",
"0 0.956947\n",
"1 0.043053\n",
"Name: proportion, dtype: float64\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1800x500 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"randoversamp = RandomOverSampler(random_state=42)\n",
"\n",
"# Применение RandomOverSampler для балансировки выборок\n",
"X_train_resampled, y_train_resampled = randoversamp.fit_resample(X_train, y_train)\n",
"X_val_resampled, y_val_resampled = randoversamp.fit_resample(X_val, y_val)\n",
"\n",
"# Проверка сбалансированности после RandomOverSampler\n",
"analyze_balance(y_train_resampled, y_val_resampled, y_test, \"stroke\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Выборки сбалансированы"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Применим унитарное кодирование категориальных признаков (one-hot encoding), переведя их в бинарные вектора."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" age hypertension heart_disease avg_glucose_level bmi gender_Male \\\n",
"0 67.0 0 0 190.70 36.0 True \n",
"1 72.0 0 0 99.73 36.7 True \n",
"2 74.0 1 1 70.09 27.4 True \n",
"3 42.0 0 0 59.43 25.4 False \n",
"4 60.0 0 0 69.53 26.2 True \n",
"\n",
" ever_married_Yes work_type_Never_worked work_type_Private \\\n",
"0 True False True \n",
"1 True False False \n",
"2 True False True \n",
"3 True False False \n",
"4 True False False \n",
"\n",
" work_type_Self-employed work_type_children Residence_type_Urban \\\n",
"0 False False True \n",
"1 True False False \n",
"2 False False False \n",
"3 False False True \n",
"4 True False True \n",
"\n",
" smoking_status_formerly smoked smoking_status_never smoked \\\n",
"0 True False \n",
"1 True False \n",
"2 False True \n",
"3 False True \n",
"4 False True \n",
"\n",
" smoking_status_smokes \n",
"0 False \n",
"1 False \n",
"2 False \n",
"3 False \n",
"4 False \n"
]
}
],
"source": [
"# Определение категориальных признаков\n",
"categorical_features = [\n",
" \"gender\",\n",
" \"ever_married\",\n",
" \"work_type\",\n",
" \"Residence_type\",\n",
" \"smoking_status\",\n",
"]\n",
"\n",
"# Применение one-hot encoding к обучающей выборке\n",
"X_train_encoded = pd.get_dummies(\n",
" X_train_resampled, columns=categorical_features, drop_first=True\n",
")\n",
"\n",
"# Применение one-hot encoding к контрольной выборке\n",
"X_val_encoded = pd.get_dummies(\n",
" X_val_resampled, columns=categorical_features, drop_first=True\n",
")\n",
"\n",
"# Применение one-hot encoding к тестовой выборке\n",
"X_test_encoded = pd.get_dummies(X_test, columns=categorical_features, drop_first=True)\n",
"\n",
"print(X_train_encoded.head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Перейдем к числовым признакам, а именно к колонке age, применим дискретизацию (позволяет преобразовать данные из числового представления в категориальное):"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" hypertension heart_disease avg_glucose_level bmi gender_Male \\\n",
"0 0 0 190.70 36.0 True \n",
"1 0 0 99.73 36.7 True \n",
"2 1 1 70.09 27.4 True \n",
"3 0 0 59.43 25.4 False \n",
"4 0 0 69.53 26.2 True \n",
"\n",
" ever_married_Yes work_type_Never_worked work_type_Private \\\n",
"0 True False True \n",
"1 True False False \n",
"2 True False True \n",
"3 True False False \n",
"4 True False False \n",
"\n",
" work_type_Self-employed work_type_children Residence_type_Urban \\\n",
"0 False False True \n",
"1 True False False \n",
"2 False False False \n",
"3 False False True \n",
"4 True False True \n",
"\n",
" smoking_status_formerly smoked smoking_status_never smoked \\\n",
"0 True False \n",
"1 True False \n",
"2 False True \n",
"3 False True \n",
"4 False True \n",
"\n",
" smoking_status_smokes age_bin \n",
"0 False old \n",
"1 False old \n",
"2 False old \n",
"3 False middle-aged \n",
"4 False old \n"
]
}
],
"source": [
"# Определение числовых признаков для дискретизации\n",
"numerical_features = [\"age\"]\n",
"\n",
"\n",
"# Функция для дискретизации числовых признаков\n",
"def discretize_features(df, features, bins, labels):\n",
" for feature in features:\n",
" df[f\"{feature}_bin\"] = pd.cut(df[feature], bins=bins, labels=labels)\n",
" df.drop(columns=[feature], inplace=True)\n",
" return df\n",
"\n",
"\n",
"# Заданные интервалы и метки\n",
"age_bins = [0, 25, 55, 100]\n",
"age_labels = [\"young\", \"middle-aged\", \"old\"]\n",
"\n",
"# Применение дискретизации к обучающей, контрольной и тестовой выборкам\n",
"X_train_encoded = discretize_features(\n",
" X_train_encoded, numerical_features, bins=age_bins, labels=age_labels\n",
")\n",
"X_val_encoded = discretize_features(\n",
" X_val_encoded, numerical_features, bins=age_bins, labels=age_labels\n",
")\n",
"X_test_encoded = discretize_features(\n",
" X_test_encoded, numerical_features, bins=age_bins, labels=age_labels\n",
")\n",
"\n",
"print(X_train_encoded.head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Применим ручной синтез признаков. Например, в этом случае создадим признак, в котором вычисляется отклонение уровня глюкозы от среднего для определенной возрастной группы. Вышеуказанный признак может быть полезен для определения пациентов с аномальными данными."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" hypertension heart_disease avg_glucose_level bmi gender_Male \\\n",
"0 0 0 190.70 36.0 True \n",
"1 0 0 99.73 36.7 True \n",
"2 1 1 70.09 27.4 True \n",
"3 0 0 59.43 25.4 False \n",
"4 0 0 69.53 26.2 True \n",
"\n",
" ever_married_Yes work_type_Never_worked work_type_Private \\\n",
"0 True False True \n",
"1 True False False \n",
"2 True False True \n",
"3 True False False \n",
"4 True False False \n",
"\n",
" work_type_Self-employed work_type_children Residence_type_Urban \\\n",
"0 False False True \n",
"1 True False False \n",
"2 False False False \n",
"3 False False True \n",
"4 True False True \n",
"\n",
" smoking_status_formerly smoked smoking_status_never smoked \\\n",
"0 True False \n",
"1 True False \n",
"2 False True \n",
"3 False True \n",
"4 False True \n",
"\n",
" smoking_status_smokes age_bin glucose_age_deviation \n",
"0 False old 58.945260 \n",
"1 False old -32.024740 \n",
"2 False old -61.664740 \n",
"3 False middle-aged -46.635693 \n",
"4 False old -62.224740 \n"
]
}
],
"source": [
"age_glucose_mean = X_train_encoded.groupby(\"age_bin\", observed=False)[\n",
" \"avg_glucose_level\"\n",
"].transform(\"mean\")\n",
"X_train_encoded[\"glucose_age_deviation\"] = (\n",
" X_train_encoded[\"avg_glucose_level\"] - age_glucose_mean\n",
")\n",
"\n",
"age_glucose_mean = X_val_encoded.groupby(\"age_bin\", observed=False)[\n",
" \"avg_glucose_level\"\n",
"].transform(\"mean\")\n",
"X_val_encoded[\"glucose_age_deviation\"] = (\n",
" X_val_encoded[\"avg_glucose_level\"] - age_glucose_mean\n",
")\n",
"\n",
"age_glucose_mean = X_test_encoded.groupby(\"age_bin\", observed=False)[\n",
" \"avg_glucose_level\"\n",
"].transform(\"mean\")\n",
"X_test_encoded[\"glucose_age_deviation\"] = (\n",
" X_test_encoded[\"avg_glucose_level\"] - age_glucose_mean\n",
")\n",
"\n",
"print(X_train_encoded.head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Используем масштабирование признаков, для приведения всех числовых признаков к одинаковым или очень похожим диапазонам значений/распределениям. \n",
"### Масштабирование признаков позволяет получить более качественную модель за счет снижения доминирования одних признаков над другими."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" hypertension heart_disease avg_glucose_level bmi gender_Male \\\n",
"0 0 0 1.274173 0.891831 True \n",
"1 0 0 -0.349199 0.991754 True \n",
"2 1 1 -0.878129 -0.335787 True \n",
"3 0 0 -1.068358 -0.621280 False \n",
"4 0 0 -0.888122 -0.507083 True \n",
"\n",
" ever_married_Yes work_type_Never_worked work_type_Private \\\n",
"0 True False True \n",
"1 True False False \n",
"2 True False True \n",
"3 True False False \n",
"4 True False False \n",
"\n",
" work_type_Self-employed work_type_children Residence_type_Urban \\\n",
"0 False False True \n",
"1 True False False \n",
"2 False False False \n",
"3 False False True \n",
"4 True False True \n",
"\n",
" smoking_status_formerly smoked smoking_status_never smoked \\\n",
"0 True False \n",
"1 True False \n",
"2 False True \n",
"3 False True \n",
"4 False True \n",
"\n",
" smoking_status_smokes age_bin glucose_age_deviation \n",
"0 False old 1.092637 \n",
"1 False old -0.593625 \n",
"2 False old -1.143046 \n",
"3 False middle-aged -0.864461 \n",
"4 False old -1.153427 \n"
]
}
],
"source": [
"numerical_features = [\"avg_glucose_level\", \"bmi\", \"glucose_age_deviation\"]\n",
"\n",
"scaler = StandardScaler()\n",
"X_train_encoded[numerical_features] = scaler.fit_transform(\n",
" X_train_encoded[numerical_features]\n",
")\n",
"X_val_encoded[numerical_features] = scaler.transform(X_val_encoded[numerical_features])\n",
"X_test_encoded[numerical_features] = scaler.transform(\n",
" X_test_encoded[numerical_features]\n",
")\n",
"\n",
"print(X_train_encoded.head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Сконструируем признаки, используя фреймворк Featuretools:"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" hypertension heart_disease avg_glucose_level bmi gender_Male \\\n",
"index \n",
"0 0 0 1.274173 0.891831 True \n",
"1 0 0 -0.349199 0.991754 True \n",
"2 1 1 -0.878129 -0.335787 True \n",
"3 0 0 -1.068358 -0.621280 False \n",
"4 0 0 -0.888122 -0.507083 True \n",
"\n",
" ever_married_Yes work_type_Never_worked work_type_Private \\\n",
"index \n",
"0 True False True \n",
"1 True False False \n",
"2 True False True \n",
"3 True False False \n",
"4 True False False \n",
"\n",
" work_type_Self-employed work_type_children Residence_type_Urban \\\n",
"index \n",
"0 False False True \n",
"1 True False False \n",
"2 False False False \n",
"3 False False True \n",
"4 True False True \n",
"\n",
" smoking_status_formerly smoked smoking_status_never smoked \\\n",
"index \n",
"0 True False \n",
"1 True False \n",
"2 False True \n",
"3 False True \n",
"4 False True \n",
"\n",
" smoking_status_smokes age_bin glucose_age_deviation \n",
"index \n",
"0 False old 1.092637 \n",
"1 False old -0.593625 \n",
"2 False old -1.143046 \n",
"3 False middle-aged -0.864461 \n",
"4 False old -1.153427 \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"d:\\Users\\Leo\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\mai-S9i2J6c7-py3.12\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n"
]
}
],
"source": [
"data = X_train_encoded.copy() # Используем предобработанные данные\n",
"\n",
"es = ft.EntitySet(id=\"patients\")\n",
"\n",
"es = es.add_dataframe(\n",
" dataframe_name=\"strokes_data\", dataframe=data, index=\"index\", make_index=True\n",
")\n",
"\n",
"feature_matrix, feature_defs = ft.dfs(\n",
" entityset=es, target_dataframe_name=\"strokes_data\", max_depth=1\n",
")\n",
"\n",
"print(feature_matrix.head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Оценим качество набора признаков.\n",
"\n",
"1. Предсказательная способность (для задачи классификации)\n",
" - Метрики: Accuracy, Precision, Recall, F1-Score, ROC AUC\n",
" - Методы: Обучение модели на обучающей выборке и оценка на валидационной и тестовой выборках.\n",
"\n",
"2. Вычислительная эффективность\n",
" - Методы: Измерение времени, затраченного на генерацию признаков и обучение модели.\n",
"\n",
"3. Надежность\n",
" - Методы: Кросс-валидация и анализ чувствительности модели к изменениям в данных.\n",
"\n",
"4. Корреляция\n",
" - Методы: Анализ корреляционной матрицы признаков и исключение мультиколлинеарных признаков.\n",
"\n",
"5. Логическая согласованность\n",
" - Методы: Проверка логической связи признаков с целевой переменной и интерпретация результатов модели."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Время обучения модели: 0.55 секунд\n"
]
}
],
"source": [
"X_train_encoded = pd.get_dummies(X_train_encoded, drop_first=True)\n",
"X_val_encoded = pd.get_dummies(X_val_encoded, drop_first=True)\n",
"X_test_encoded = pd.get_dummies(X_test_encoded, drop_first=True)\n",
"\n",
"all_columns = X_train_encoded.columns\n",
"X_train_encoded = X_train_encoded.reindex(columns=all_columns, fill_value=0)\n",
"X_val_encoded = X_val_encoded.reindex(columns=all_columns, fill_value=0)\n",
"X_test_encoded = X_test_encoded.reindex(columns=all_columns, fill_value=0)\n",
"\n",
"# Выбор модели\n",
"model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
"\n",
"# Начинаем отсчет времени\n",
"start_time = time.time()\n",
"model.fit(X_train_encoded, y_train_resampled)\n",
"\n",
"# Время обучения модели\n",
"train_time = time.time() - start_time\n",
"\n",
"print(f\"Время обучения модели: {train_time:.2f} секунд\")"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Feature Importance:\n",
" feature importance\n",
"16 age_bin_old 0.199473\n",
"3 bmi 0.186638\n",
"14 glucose_age_deviation 0.172977\n",
"2 avg_glucose_level 0.171885\n",
"15 age_bin_middle-aged 0.037563\n",
"5 ever_married_Yes 0.036580\n",
"4 gender_Male 0.028419\n",
"0 hypertension 0.025984\n",
"8 work_type_Self-employed 0.022772\n",
"10 Residence_type_Urban 0.022221\n",
"1 heart_disease 0.020967\n",
"12 smoking_status_never smoked 0.017890\n",
"7 work_type_Private 0.016844\n",
"11 smoking_status_formerly smoked 0.015641\n",
"13 smoking_status_smokes 0.012366\n",
"9 work_type_children 0.011684\n",
"6 work_type_Never_worked 0.000096\n"
]
}
],
"source": [
"# Получение важности признаков\n",
"importances = model.feature_importances_\n",
"feature_names = X_train_encoded.columns\n",
"\n",
"# Сортировка признаков по важности\n",
"feature_importance = pd.DataFrame({\"feature\": feature_names, \"importance\": importances})\n",
"feature_importance = feature_importance.sort_values(by=\"importance\", ascending=False)\n",
"\n",
"print(\"Feature Importance:\")\n",
"print(feature_importance)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.9465101108936725\n",
"Precision: 0.21428571428571427\n",
"Recall: 0.09090909090909091\n",
"F1 Score: 0.1276595744680851\n",
"ROC AUC: 0.5379562496126913\n",
"Cross-validated Accuracy: 0.988641983507665\n"
]
},
{
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"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train Accuracy: 1.0\n",
"Train Precision: 1.0\n",
"Train Recall: 1.0\n",
"Train F1 Score: 1.0\n",
"Train ROC AUC: 1.0\n"
]
}
],
"source": [
"# Предсказание и оценка\n",
"y_pred = model.predict(X_test_encoded)\n",
"\n",
"accuracy = accuracy_score(y_test, y_pred)\n",
"precision = precision_score(y_test, y_pred)\n",
"recall = recall_score(y_test, y_pred)\n",
"f1 = f1_score(y_test, y_pred)\n",
"roc_auc = roc_auc_score(y_test, y_pred)\n",
"\n",
"print(f\"Accuracy: {accuracy}\")\n",
"print(f\"Precision: {precision}\")\n",
"print(f\"Recall: {recall}\")\n",
"print(f\"F1 Score: {f1}\")\n",
"print(f\"ROC AUC: {roc_auc}\")\n",
"\n",
"# Кросс-валидация\n",
"scores = cross_val_score(\n",
" model, X_train_encoded, y_train_resampled, cv=5, scoring=\"accuracy\"\n",
")\n",
"accuracy_cv = scores.mean()\n",
"print(f\"Cross-validated Accuracy: {accuracy_cv}\")\n",
"\n",
"# Анализ важности признаков\n",
"feature_importances = model.feature_importances_\n",
"feature_names = X_train_encoded.columns\n",
"\n",
"importance_df = pd.DataFrame(\n",
" {\"Feature\": feature_names, \"Importance\": feature_importances}\n",
")\n",
"importance_df = importance_df.sort_values(by=\"Importance\", ascending=False)\n",
"\n",
"plt.figure(figsize=(10, 6))\n",
"sns.barplot(x=\"Importance\", y=\"Feature\", data=importance_df)\n",
"plt.title(\"Feature Importance\")\n",
"plt.show()\n",
"\n",
"# Проверка на переобучение\n",
"y_train_pred = model.predict(X_train_encoded)\n",
"\n",
"accuracy_train = accuracy_score(y_train_resampled, y_train_pred)\n",
"precision_train = precision_score(y_train_resampled, y_train_pred)\n",
"recall_train = recall_score(y_train_resampled, y_train_pred)\n",
"f1_train = f1_score(y_train_resampled, y_train_pred)\n",
"roc_auc_train = roc_auc_score(y_train_resampled, y_train_pred)\n",
"\n",
"print(f\"Train Accuracy: {accuracy_train}\")\n",
"print(f\"Train Precision: {precision_train}\")\n",
"print(f\"Train Recall: {recall_train}\")\n",
"print(f\"Train F1 Score: {f1_train}\")\n",
"print(f\"Train ROC AUC: {roc_auc_train}\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
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Reference in New Issue Copy Permalink