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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1 Датасет: NASA - Nearest Earth Objects\n",
"#### https://www.kaggle.com/datasets/sameepvani/nasa-nearest-earth-objects \n",
"\n",
"There is an infinite number of objects in the outer space. Some of them are closer than we think. Even though we might think that a distance of 70,000 Km can not potentially harm us, but at an astronomical scale, this is a very small distance and can disrupt many natural phenomena. These objects/asteroids can thus prove to be harmful. Hence, it is wise to know what is surrounding us and what can harm us amongst those. Thus, this dataset compiles the list of NASA certified asteroids that are classified as the nearest earth object.\n",
"\n",
"В
"\n",
"- Из этого описания очевидно что объектами иследования являются околоземные объекты.\n",
"- Атрибуты объектов: id, name, est_diameter_min, est_diameter_max, relative_velocity, miss_distance, orbiting_body, sentry_object, absolute_magnitude, hazardous\n",
"- Очевидная цель этого датасета - это научиться определять опасность объекта автоматически.\n"
]
},
{
"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"количество колонок: 10\n",
"колонки: id, name, est_diameter_min, est_diameter_max, relative_velocity, miss_distance, orbiting_body, sentry_object, absolute_magnitude, hazardous\n"
]
}
],
"source": [
"import pandas as pd\n",
"df = pd.read_csv(\"..//static//csv//neo_v2.csv\", sep=\",\")\n",
"print('количество колонок: ' + str(df.columns.size)) \n",
"print('колонки: ' + ', '.join(df.columns))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Получение сведений о
"\n",
"Типы пропущенных данных:\n",
"- None - представление пустых данных в Python\n",
"- NaN - представление пустых данных в Pandas\n",
"- '' - пустая строка"
]
},
{
"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"id 0\n",
"name 0\n",
"est_diameter_min 0\n",
"est_diameter_max 0\n",
"relative_velocity 0\n",
"miss_distance 0\n",
"orbiting_body 0\n",
"sentry_object 0\n",
"absolute_magnitude 0\n",
"hazardous 0\n",
"dtype: int64\n",
"\n",
"id False\n",
"name False\n",
"est_diameter_min False\n",
"est_diameter_max False\n",
"relative_velocity False\n",
"miss_distance False\n",
"orbiting_body False\n",
"sentry_object False\n",
"absolute_magnitude False\n",
"hazardous False\n",
"dtype: bool\n",
"\n"
]
}
],
"source": [
"# Количество пустых значений признаков\n",
"print(df.isnull().sum())\n",
"\n",
"print()\n",
"\n",
"# Есть ли пустые значения признаков\n",
"print(df.isnull().any())\n",
"\n",
"print()\n",
"\n",
"# Процент пустых значений признаков\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": [
"Итог: пропущеных значений нет"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"инфографика на сайте и в datawrangelere показывает, что в столбцах orbiting_body и sentry_object у
]
},
{
"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"количество колонок: 8\n",
"колонки: id, name, est_diameter_min, est_diameter_max, relative_velocity, miss_distance, absolute_magnitude, hazardous\n"
]
}
],
"source": [
"df = df.drop(columns=['orbiting_body'])\n",
"df = df.drop(columns=['sentry_object'])\n",
"print('количество колонок: ' + str(df.columns.size)) \n",
"print('колонки: ' + ', '.join(df.columns))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"я быстро посмотрев данные зашумленности не выявил"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"поля id и name в предсказании не помогут, но я их пока выкидывать не буду"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
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"посмотрим выбросы и усредним их:"
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]
},
{
"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Колонка est_diameter_min:\n",
" Есть выбросы: Да\n",
" Количество выбросов: 8306\n",
" Минимальное значение: 0.0006089126\n",
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" Максимальное значение: 0.32962154705\n",
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" 1-й квартиль (Q1): 0.0192555078\n",
" 3-й квартиль (Q3): 0.1434019235\n",
"\n",
"Колонка est_diameter_max:\n",
" Есть выбросы: Да\n",
" Количество выбросов: 8306\n",
" Минимальное значение: 0.00136157\n",
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" Максимальное значение: 0.7370561859\n",
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" 1-й квартиль (Q1): 0.0430566244\n",
" 3-й квартиль (Q3): 0.320656449\n",
"\n",
"Колонка relative_velocity:\n",
" Есть выбросы: Да\n",
" Количество выбросов: 1574\n",
" Минимальное значение: 203.34643253\n",
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" Максимальное значение: 114380.48061454494\n",
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" 1-й квартиль (Q1): 28619.02064490995\n",
" 3-й квартиль (Q3): 62923.60463276395\n",
"\n",
"Колонка miss_distance:\n",
" Есть выбросы: Нет\n",
" Количество выбросов: 0\n",
" Минимальное значение: 6745.532515957\n",
" Максимальное значение: 74798651.4521972\n",
" 1-й квартиль (Q1): 17210820.23576468\n",
" 3-й квартиль (Q3): 56548996.45139917\n",
"\n",
"Колонка absolute_magnitude:\n",
" Есть выбросы: Да\n",
" Количество выбросов: 101\n",
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" Минимальное значение: 14.8\n",
" Максимальное значение: 32.239999999999995\n",
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" 1-й квартиль (Q1): 21.34\n",
" 3-й квартиль (Q3): 25.7\n",
"\n"
]
}
],
"source": [
"numeric_columns = ['est_diameter_min', 'est_diameter_max', 'relative_velocity', 'miss_distance', 'absolute_magnitude']\n",
"for column in numeric_columns:\n",
" if pd.api.types.is_numeric_dtype(df[column]): # Проверяем, является ли колонка числовой\n",
" q1 = df[column].quantile(0.25) # Находим 1-й квартиль (Q1)\n",
" q3 = df[column].quantile(0.75) # Находим 3-й квартиль (Q3)\n",
" iqr = q3 - q1 # Вычисляем межквартильный размах (IQR)\n",
"\n",
" # Определяем границы для выбросов\n",
" lower_bound = q1 - 1.5 * iqr # Нижняя граница\n",
" upper_bound = q3 + 1.5 * iqr # Верхняя граница\n",
"\n",
" # Подсчитываем количество выбросов\n",
" outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]\n",
" outlier_count = outliers.shape[0]\n",
"\n",
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" # Устраняем выбросы: заменяем значения ниже нижней границы на саму нижнюю границу, а
" df[column] = df[column].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)\n",
"\n",
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" print(f\"Колонка {column}:\")\n",
" print(f\" Есть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n",
" print(f\" Количество выбросов: {outlier_count}\")\n",
" print(f\" Минимальное значение: {df[column].min()}\")\n",
" print(f\" Максимальное значение: {df[column].max()}\")\n",
" print(f\" 1-й квартиль (Q1): {q1}\")\n",
" print(f\" 3-й квартиль (Q3): {q3}\\n\")\n"
]
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"построим графики в надежде найти какие то зависимости опасности от других колонок"
]
},
{
"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 400x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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
2024-10-04 22:01:22 +04:00
"text/plain": [
"<Figure size 400x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
2024-10-05 03:59:09 +04:00
"image/png": "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
2024-10-04 22:01:22 +04:00
"text/plain": [
"<Figure size 400x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 400x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 400x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"# Список числовых колонок, для которых мы будем строить графики\n",
"numeric_columns = ['est_diameter_min', 'est_diameter_max', 'relative_velocity', 'miss_distance', 'absolute_magnitude']\n",
"\n",
"# Создание диаграмм зависимости\n",
"for column in numeric_columns:\n",
" plt.figure(figsize=(4, 8)) # Установка размера графика\n",
" plt.scatter(df['hazardous'], df[column], alpha=0.5) # Создаем диаграмму рассеяния\n",
" plt.title(f'Зависимость {column} от hazardous')\n",
" plt.xlabel('hazardous (0 = нет, 1 = да)')\n",
" plt.ylabel(column)\n",
" plt.xticks([0, 1]) # Установка меток по оси X\n",
" plt.grid() # Добавление сетки для удобства восприятия\n",
" plt.show() # Отображение графика"
]
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Создадим выборки данных. разбивать будем относительно параметра опасный, ведь это тот самый параметр по которому наша выборка разбивается на классы. И собственно е г о
]
},
{
"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: scikit-learn in d:\\мии\\aim-pibd-31-kouvshinoff-t-a\\laba\\lib\\site-packages (1.5.2)\n",
"Requirement already satisfied: numpy>=1.19.5 in d:\\мии\\aim-pibd-31-kouvshinoff-t-a\\laba\\lib\\site-packages (from scikit-learn) (2.1.1)\n",
"Requirement already satisfied: scipy>=1.6.0 in d:\\мии\\aim-pibd-31-kouvshinoff-t-a\\laba\\lib\\site-packages (from scikit-learn) (1.14.1)\n",
"Requirement already satisfied: joblib>=1.2.0 in d:\\мии\\aim-pibd-31-kouvshinoff-t-a\\laba\\lib\\site-packages (from scikit-learn) (1.4.2)\n",
"Requirement already satisfied: threadpoolctl>=3.1.0 in d:\\мии\\aim-pibd-31-kouvshinoff-t-a\\laba\\lib\\site-packages (from scikit-learn) (3.5.0)\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"pip install scikit-learn"
]
},
{
"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
"outputs": [],
"source": [
"# Функция для создания выборок\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",
"):\n",
" \"\"\"\n",
" Splits a Pandas dataframe into three subsets (train, val, and test)\n",
" following fractional ratios provided by the user, where each subset is\n",
" stratified by the values in a specific column (that is, each subset has\n",
" the same relative frequency of the values in the column). It performs this\n",
" splitting by running train_test_split() twice.\n",
"\n",
" Parameters\n",
" ----------\n",
" df_input : Pandas dataframe\n",
" Input dataframe to be split.\n",
" stratify_colname : str\n",
" The name of the column that will be used for stratification. Usually\n",
" this column would be for the label.\n",
" frac_train : float\n",
" frac_val : float\n",
" frac_test : float\n",
" The ratios with which the dataframe will be split into train, val, and\n",
" test data. The values should be expressed as float fractions and should\n",
" sum to 1.0.\n",
" random_state : int, None, or RandomStateInstance\n",
" Value to be passed to train_test_split().\n",
"\n",
" Returns\n",
" -------\n",
" df_train, df_val, df_test :\n",
" Dataframes containing the three splits.\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",
"\n",
" if stratify_colname not in df_input.columns:\n",
" raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
"\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",
"\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",
"\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",
"\n",
" assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
"\n",
" return df_train, df_val, df_test"
]
},
{
"cell_type": "code",
2024-10-05 03:59:09 +04:00
"execution_count": 8,
2024-10-04 23:54:02 +04:00
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hazardous\n",
"False 81996\n",
"True 8840\n",
"Name: count, dtype: int64\n",
"\n",
"Обучающая выборка: (54501, 6)\n",
"hazardous\n",
"False 49197\n",
"True 5304\n",
"Name: count, dtype: int64\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 200x200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Контрольная выборка: (18167, 6)\n",
"hazardous\n",
"False 16399\n",
"True 1768\n",
"Name: count, dtype: int64\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 200x200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Тестовая выборка: (18168, 6)\n",
"hazardous\n",
"False 16400\n",
"True 1768\n",
"Name: count, dtype: int64\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 200x200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Вывод распределения количества наблюдений по меткам (классам)\n",
"print(df.hazardous.value_counts())\n",
"print()\n",
"\n",
"\n",
"data = df[['est_diameter_min', 'est_diameter_max', 'relative_velocity', 'miss_distance', 'absolute_magnitude', 'hazardous']].copy()\n",
"\n",
"df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
" data, stratify_colname=\"hazardous\", frac_train=0.60, frac_val=0.20, frac_test=0.20\n",
")\n",
"\n",
"print(\"Обучающая выборка: \", df_train.shape)\n",
"print(df_train.hazardous.value_counts())\n",
"hazardous_counts = df_train['hazardous'].value_counts()\n",
"plt.figure(figsize=(2, 2))# Установка размера графика\n",
"plt.pie(hazardous_counts, labels=hazardous_counts.index, autopct='%1.1f%%', startangle=90)# Построение круговой диаграммы\n",
"plt.title('Распределение классов hazardous в обучающей выборке')# Добавление заголовка\n",
"plt.show()# Отображение графика\n",
"\n",
"print(\"Контрольная выборка: \", df_val.shape)\n",
"print(df_val.hazardous.value_counts())\n",
"hazardous_counts = df_val['hazardous'].value_counts()\n",
"plt.figure(figsize=(2, 2))\n",
"plt.pie(hazardous_counts, labels=hazardous_counts.index, autopct='%1.1f%%', startangle=90)\n",
"plt.title('Распределение классов hazardous в контрольной выборке')\n",
"plt.show()\n",
"\n",
"print(\"Тестовая выборка: \", df_test.shape)\n",
"print(df_test.hazardous.value_counts())\n",
"hazardous_counts = df_test['hazardous'].value_counts()\n",
"plt.figure(figsize=(2, 2))\n",
"plt.pie(hazardous_counts, labels=hazardous_counts.index, autopct='%1.1f%%', startangle=90)\n",
"plt.title('Распределение классов hazardous в тестовой выборке')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"распределение плохое, соотношение классов сильно смещено, это может привести к проблемам в обучении модели, так как модель будет обучаться в основном на одном классе. В
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"аугментация данных оверсемплингом(Этот метод увеличивает количество примеров меньшинства)"
]
},
{
"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Requirement already satisfied: imblearn in d:\\мии\\aim-pibd-31-kouvshinoff-t-a\\laba\\lib\\site-packages (0.0)\n",
"Requirement already satisfied: imbalanced-learn in d:\\мии\\aim-pibd-31-kouvshinoff-t-a\\laba\\lib\\site-packages (from imblearn) (0.12.4)\n",
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"Requirement already satisfied: numpy>=1.17.3 in d:\\мии\\aim-pibd-31-kouvshinoff-t-a\\laba\\lib\\site-packages (from imbalanced-learn->imblearn) (2.1.1)\n",
"Requirement already satisfied: scipy>=1.5.0 in d:\\мии\\aim-pibd-31-kouvshinoff-t-a\\laba\\lib\\site-packages (from imbalanced-learn->imblearn) (1.14.1)\n",
"Requirement already satisfied: scikit-learn>=1.0.2 in d:\\мии\\aim-pibd-31-kouvshinoff-t-a\\laba\\lib\\site-packages (from imbalanced-learn->imblearn) (1.5.2)\n",
"Requirement already satisfied: joblib>=1.1.1 in d:\\мии\\aim-pibd-31-kouvshinoff-t-a\\laba\\lib\\site-packages (from imbalanced-learn->imblearn) (1.4.2)\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in d:\\мии\\aim-pibd-31-kouvshinoff-t-a\\laba\\lib\\site-packages (from imbalanced-learn->imblearn) (3.5.0)\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"pip install imblearn"
]
},
{
"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Обучающая выборка после oversampling: (100385, 6)\n",
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"hazardous\n",
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"True 51188\n",
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"False 49197\n",
"Name: count, dtype: int64\n"
]
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 200x200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from imblearn.over_sampling import ADASYN\n",
"\n",
"# Создание экземпляра ADASYN\n",
"ada = ADASYN()\n",
"\n",
"# Применение ADASYN\n",
"X_resampled, y_resampled = ada.fit_resample(df_train.drop(columns=['hazardous']), df_train['hazardous'])\n",
"\n",
"# Создание нового DataFrame\n",
"df_train_adasyn = pd.DataFrame(X_resampled)\n",
"df_train_adasyn['hazardous'] = y_resampled # Добавление целевой переменной\n",
"\n",
"# Вывод информации о
"print(\"Обучающая выборка после oversampling: \", df_train_adasyn.shape)\n",
"print(df_train_adasyn['hazardous'].value_counts())\n",
"hazardous_counts = df_train_adasyn['hazardous'].value_counts()\n",
"plt.figure(figsize=(2, 2))\n",
"plt.pie(hazardous_counts, labels=hazardous_counts.index, autopct='%1.1f%%', startangle=90)\n",
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"plt.title('Распределение классов hazardous в тренировачной выборке после ADASYN')\n",
2024-10-04 23:54:02 +04:00
"plt.show()"
]
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"P.S. можно было использовать ещё SMOTE, SVM-SMOTE, K-means SMOTE, SMOTE-N, SMOTE-NC, RandomOverSampler."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"проведём также балансировку данных методом андерсемплинга. Этот метод помогает сбалансировать выборку, уменьшая количество экземпляров класса большинства, чтобы привести е г о с
]
},
{
"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Обучающая выборка после undersampling: (10608, 6)\n",
"hazardous\n",
"False 5304\n",
"True 5304\n",
"Name: count, dtype: int64\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 200x200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from imblearn.under_sampling import RandomUnderSampler\n",
"\n",
"rus = RandomUnderSampler()# Создание экземпляра RandomUnderSampler\n",
"\n",
"# Применение RandomUnderSampler\n",
"X_resampled, y_resampled = rus.fit_resample(df_train.drop(columns=['hazardous']), df_train['hazardous'])\n",
"\n",
"# Создание нового DataFrame\n",
"df_train_undersampled = pd.DataFrame(X_resampled)\n",
"df_train_undersampled['hazardous'] = y_resampled # Добавление целевой переменной\n",
"\n",
"# Вывод информации о
"print(\"Обучающая выборка после undersampling: \", df_train_undersampled.shape)\n",
"print(df_train_undersampled['hazardous'].value_counts())\n",
"\n",
"# Визуализация распределения классов\n",
"hazardous_counts = df_train_undersampled['hazardous'].value_counts()\n",
"plt.figure(figsize=(2, 2))\n",
"plt.pie(hazardous_counts, labels=hazardous_counts.index, autopct='%1.1f%%', startangle=90)\n",
"plt.title('Распределение классов hazardous в тренировочной выборке после Undersampling')\n",
"plt.show()"
]
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2 Датасет: Pima Indians Diabetes Database\n",
"#### https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database\n",
"\n",
"This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.\n",
"\n",
"Этот набор данных изначально был получен из Национального института диабета, заболеваний пищеварительной системы и почек. Целью набора данных является диагностическое прогнозирование наличия или отсутствия у Н а В
"\n",
"- объект иследования - женьщины индейци пима\n",
"- очевидно цель датасета это предсказание диабета.\n",
"- атрибуты: Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age, Outcome"
]
},
{
"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"количество колонок: 9\n",
"колонки: Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age, Outcome\n"
]
}
],
"source": [
"df = pd.read_csv(\"..//static//csv//diabetes.csv\", sep=\",\")\n",
"print('количество колонок: ' + str(df.columns.size)) \n",
"print('колонки: ' + ', '.join(df.columns))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Получение сведений о
"\n",
"Типы пропущенных данных:\n",
"- None - представление пустых данных в Python\n",
"- NaN - представление пустых данных в Pandas\n",
"- '' - пустая строка"
]
},
{
"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pregnancies 0\n",
"Glucose 0\n",
"BloodPressure 0\n",
"SkinThickness 0\n",
"Insulin 0\n",
"BMI 0\n",
"DiabetesPedigreeFunction 0\n",
"Age 0\n",
"Outcome 0\n",
"dtype: int64\n",
"\n",
"Pregnancies False\n",
"Glucose False\n",
"BloodPressure False\n",
"SkinThickness False\n",
"Insulin False\n",
"BMI False\n",
"DiabetesPedigreeFunction False\n",
"Age False\n",
"Outcome False\n",
"dtype: bool\n",
"\n"
]
}
],
"source": [
"# Количество пустых значений признаков\n",
"print(df.isnull().sum())\n",
"\n",
"print()\n",
"\n",
"# Есть ли пустые значения признаков\n",
"print(df.isnull().any())\n",
"\n",
"print()\n",
"\n",
"# Процент пустых значений признаков\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": [
"Итог: пропущеных значений нет"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
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"посмотрим выбросы и усредним их:"
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]
},
{
"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Колонка Pregnancies:\n",
" Есть выбросы: Да\n",
" Количество выбросов: 4\n",
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" Минимальное значение: 0.0\n",
" Максимальное значение: 13.5\n",
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" 1-й квартиль (Q1): 1.0\n",
" 3-й квартиль (Q3): 6.0\n",
"\n",
"Колонка Glucose:\n",
" Есть выбросы: Да\n",
" Количество выбросов: 5\n",
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" Минимальное значение: 37.125\n",
" Максимальное значение: 199.0\n",
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" 1-й квартиль (Q1): 99.0\n",
" 3-й квартиль (Q3): 140.25\n",
"\n",
"Колонка BloodPressure:\n",
" Есть выбросы: Да\n",
" Количество выбросов: 45\n",
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" Минимальное значение: 35.0\n",
" Максимальное значение: 107.0\n",
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" 1-й квартиль (Q1): 62.0\n",
" 3-й квартиль (Q3): 80.0\n",
"\n",
"Колонка SkinThickness:\n",
" Есть выбросы: Да\n",
" Количество выбросов: 1\n",
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" Минимальное значение: 0.0\n",
" Максимальное значение: 80.0\n",
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" 1-й квартиль (Q1): 0.0\n",
" 3-й квартиль (Q3): 32.0\n",
"\n",
"Колонка Insulin:\n",
" Есть выбросы: Да\n",
" Количество выбросов: 34\n",
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" Минимальное значение: 0.0\n",
" Максимальное значение: 318.125\n",
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" 1-й квартиль (Q1): 0.0\n",
" 3-й квартиль (Q3): 127.25\n",
"\n",
"Колонка BMI:\n",
" Есть выбросы: Да\n",
" Количество выбросов: 19\n",
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" Минимальное значение: 13.35\n",
" Максимальное значение: 50.550000000000004\n",
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" 1-й квартиль (Q1): 27.3\n",
" 3-й квартиль (Q3): 36.6\n",
"\n",
"Колонка DiabetesPedigreeFunction:\n",
" Есть выбросы: Да\n",
" Количество выбросов: 29\n",
" Минимальное значение: 0.078\n",
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" Максимальное значение: 1.2\n",
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" 1-й квартиль (Q1): 0.24375\n",
" 3-й квартиль (Q3): 0.62625\n",
"\n",
"Колонка Age:\n",
" Есть выбросы: Да\n",
" Количество выбросов: 9\n",
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" Минимальное значение: 21.0\n",
" Максимальное значение: 66.5\n",
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" 1-й квартиль (Q1): 24.0\n",
" 3-й квартиль (Q3): 41.0\n",
"\n"
]
}
],
"source": [
"numeric_columns = ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']\n",
"for column in numeric_columns:\n",
" if pd.api.types.is_numeric_dtype(df[column]): # Проверяем, является ли колонка числовой\n",
" q1 = df[column].quantile(0.25) # Находим 1-й квартиль (Q1)\n",
" q3 = df[column].quantile(0.75) # Находим 3-й квартиль (Q3)\n",
" iqr = q3 - q1 # Вычисляем межквартильный размах (IQR)\n",
"\n",
" # Определяем границы для выбросов\n",
" lower_bound = q1 - 1.5 * iqr # Нижняя граница\n",
" upper_bound = q3 + 1.5 * iqr # Верхняя граница\n",
"\n",
" # Подсчитываем количество выбросов\n",
" outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]\n",
" outlier_count = outliers.shape[0]\n",
"\n",
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" # Устраняем выбросы: заменяем значения ниже нижней границы на саму нижнюю границу, а
" df[column] = df[column].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)\n",
"\n",
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" print(f\"Колонка {column}:\")\n",
" print(f\" Есть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n",
" print(f\" Количество выбросов: {outlier_count}\")\n",
" print(f\" Минимальное значение: {df[column].min()}\")\n",
" print(f\" Максимальное значение: {df[column].max()}\")\n",
" print(f\" 1-й квартиль (Q1): {q1}\")\n",
" print(f\" 3-й квартиль (Q3): {q3}\\n\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"построим графики в надежде найти какие то зависимости опасности от других колонок"
]
},
{
"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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
2024-10-05 01:43:27 +04:00
"text/plain": [
"<Figure size 400x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
2024-10-05 03:59:09 +04:00
"image/png": "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
2024-10-05 01:43:27 +04:00
"text/plain": [
"<Figure size 400x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
2024-10-05 03:59:09 +04:00
"image/png": "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
2024-10-05 01:43:27 +04:00
"text/plain": [
"<Figure size 400x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
2024-10-05 03:59:09 +04:00
"image/png": "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
2024-10-05 01:43:27 +04:00
"text/plain": [
"<Figure size 400x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
2024-10-05 03:59:09 +04:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAK9CAYAAADouSHAAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAACZhklEQVR4nOzdeZxcVZnw8d9dauuqrt53knQ2spDESNjCviZsLiwq6EBYlFcMOoAvKoMLAWfiqCOKCoyjgAo486IjjAyBsCMQlhAREkhIQjoJ6X3vqq7t1j3vH9VVpLekq+lQtzrP9/PpT7punao6XVW5zz3Lc46mlFIIIYQQE0jPdQWEEEJMPhJchBBCTDgJLkIIISacBBchhBATToKLEEKICSfBRQghxIST4CKEEGLCSXARQggx4SS4CCGEmHASXIQQIg+Fw2F2795NV1dXrqsyIgkuQoxA0zRuvvnmzO17770XTdNoaGjIWZ3ER+PVV1/F7Xazc+fOXFdlmAcffJDTTjuNwsJCAoEAU6dO5Yc//OEBe723334b0zTZuHFj1o/Ny+By1113sXz5cqqqqnC5XFRXV3PSSSfxu9/9Dtu2c109MU6XXXYZgUAg19WYdBKJBLfffjtHHnlk5qR05JFHcvvtt5NIJMb9vC+99BI333wz3d3dE1dZB7jpppu4+OKLmTZtWq6rMsi3vvUtPvvZz1JYWMh//Md/8MQTT/Dkk0/yla985YC95vz58znnnHP47ne/m/2DVR465phj1Hnnnad+/vOfq9/+9rfqZz/7mbr44ouVpmnqoosuynX1xDitWLFC+f3+XFdDKaUUoL73ve9lbluWpSKRiLJtO3eVGodQKKROOukkBahzzz1X/eIXv1B33HGH+uQnP6kAddJJJ6lQKDSu5/7Rj36kALVjx46JrXQO/e1vf1OAeumll3JdlUGeffZZBajVq1d/5K/96KOPKkBt27Ytq8flZXCJx+MjHr/mmmsm3Zf9YOLk4JKvrrrqKgWon//858Pu+8UvfqEA9eUvf3lczz0Zg8vXvvY1NXXqVMddRJx77rnq2GOPzclrx+NxVVJSor7zne9k9bi87BZzuVwjHq+vrwdA1z/4sx5++GHOOeccamtr8Xg8zJw5k1tvvZVkMjnosSeffDKapmV+ysvLOeecc4b1NQ7tiwf40Y9+hKZpnHzyyYOOR6NRbr75Zg499FC8Xi81NTWcf/75bN++HYCGhgY0TePee+8d9LiVK1eiaRqXXXZZ5li6z9/tdtPW1jao/Lp16zL1Xr9+/aD7HnzwQZYsWYLP56O8vJx/+Id/YM+ePcPeu82bN/PZz36WiooKfD4fc+bM4aabbgLg5ptvHvTejPTz7LPPZt7HBQsWDHv+8aqvr+fcc8/lhRde4KijjsLr9TJjxgx+97vfDSqXSCRYtWoVs2fPxuv1UlZWxvHHH88TTzyRKXPyyScP+4wg1R2X/u6MZqQxl7HWbTThcJivf/3rTJkyBY/Hw5w5c/jxj3+M2msXjP297yP9PWnvv/8+v/nNbzj11FO55pprht2/cuVKTjnlFH7961/z/vvvA6N/J9N1SX/3b775Zm644QYApk+fnqnP3u/Pfffdx1FHHUVBQQElJSWceOKJrF27dtBz3nHHHRx22GF4PB5qa2tZuXLlsG629HfqzTff5KSTTqKgoIBZs2bxxz/+EYDnnnuOo48+OvO9ffLJJ4fVfc+ePVxxxRVUVVXh8Xg47LDDuPvuu4eVe+ihhzj11FPRNG3Yfen3ZqSfvf/ukb4rtm2zaNGiYe/tm2++yWWXXcaMGTPwer1UV1dzxRVX0NHRMei1X375ZRYsWMBFF11EaWkpPp+PI488koceemhYPWOxGN/73veYNWsWHo+HKVOm8I1vfINYLJYpk833yuVycfLJJ/Pwww8Pe619MbMq7TDd3d1YlkVfXx+vv/46P/7xj7nooouYOnVqpsy9995LIBDg+uuvJxAI8PTTT/Pd736X3t5efvSjHw16vrlz53LTTTehlGL79u385Cc/4eyzz2bXrl37rMPq1auHHU8mk5x77rk89dRTXHTRRfzjP/4jfX19PPHEE2zcuJGZM2eO+Hzbtm3jP/7jP0Z9PcMwuO+++7juuusyx+655x68Xi/RaHRQ2XvvvZfLL7+cI488ktWrV9PS0sLPfvYzXnzxRf72t79RXFwMpL7gJ5xwAi6Xi6uuuor6+nq2b9/OX/7yF/75n/+Z888/n1mzZmWe97rrrmPevHlcddVVmWPz5s0btc4f1rZt27jwwgu58sorWbFiBXfffTeXXXYZS5Ys4bDDDgNSJ7vVq1fzxS9+kaOOOore3l7Wr1/Phg0bOOOMM3Jat5EopfjkJz/JM888w5VXXsnixYt5/PHHueGGG9izZw+33XYbAL///e8zj/nrX//Kr371K2677TbKy8sBqKqqGvU11qxZQzKZ5NJLLx21zKWXXsozzzzDY489xhe/+MUx/93nn38+7777Ln/4wx8G1aeiogKAVatWcfPNN3Psscdyyy234Ha7eeWVV3j66adZtmwZkPrMVq1axemnn87VV1/Nli1buPPOO3nttdd48cUXB11EdnV1ce6553LRRRfxmc98hjvvvJOLLrqI+++/n2uvvZYvf/nLfP7zn+dHP/oRF154Ibt376awsBCAlpYWjjnmGDRN45prrqGiooI1a9Zw5ZVX0tvby7XXXgukAtCuXbs4/PDD9/m3X3zxxZx99tkAPProo/zhD3/Y7/v1+9//nrfeemvY8SeeeIL33nuPyy+/nOrqajZt2sSvfvUrNm3axMsvv5wJch0dHfzqV78iEAjwta99jYqKCu677z7OP/987r//fi6++GIgFcQ++clP8sILL3DVVVcxb9483nrrLW677TbefffdTDDK9nu1ZMkSHn74YXp7ewkGg/v9e4H8HHNJmzNnjgIyP5deeqlKJBKDyvT39w973P/5P/9HFRQUqGg0mjl20kknqZNOOmlQuX/6p39SgGptbc0cY0h3yTe+8Q1VWVmplixZMujxd999twLUT37yk2Gvn25y79ixQwHqnnvuydz32c9+Vi1YsEBNmTJFrVixInP8nnvuUYC6+OKL1cKFCzPHw+GwCgaD6vOf/7wC1GuvvaaUSjVlKysr1YIFC1QkEsmUf+SRRxSgvvvd72aOnXjiiaqwsFDt3LlzxHoONW3atEF129tJJ52kDjvssBHv25+RusWmTZumAPX8889njrW2tiqPx6O+/vWvZ4597GMfU+ecc84+n3+kzzj9utOmTRt0bOjnnH7/9+4CGmvdRvLQQw8pQH3/+98fdPzCCy9UmqaN2L89Uh325dprr1WA+tvf/jZqmQ0bNihAXX/99Uqpkb+TaUPfk9G6xbZu3ap0XVfnnXeeSiaTg+5Lf6daW1uV2+1Wy5YtG1Qm3VV39913Z46lx4weeOCBzLHNmzcrQOm6rl5++eXM8ccff3xY/a+88kpVU1Oj2tvbB9XloosuUkVFRZlzxJNPPqkA9Ze//GXE9+rdd99VgPrxj3+8z/dg6OcUjUbV1KlT1VlnnTWsbiOdn/7whz8M+16lz3HPPvvsoMfOmzdPVVdXZ4YKfv/73ytd19Vf//rXQc951113KUC9+OKLw15vLN+rBx54QAHqlVdeGbXMUHnZLZZ2zz338MQTT3D//fdz5ZVXcv/99w+6mgbw+XyZ3/v6+mhvb+eEE06gv7+fzZs3DyqbSCRob2+nra2NdevW8ec//5lFixZlovlQe/bs4ec//znf+c53hs1y+tOf/kR5eTlf/epXhz1upCY3wOuvv86DDz7I6tWrB3Xt7e2SSy5h8+bNme6vP/3pTxQVFXHaaacNKrd+/XpaW1v5yle+gtfrzRw/55xzmDt3Lv/7v/8LQFtbG88//zxXXHHFoBbfvuq5P8lkkvb2dtrb24nH4+N6jr3Nnz+fE044IXO7oqKCOXPm8N5772WOFRcXs2nTJrZu3fqhX2+i6zaSRx99FMMw+NrXvjbo+Ne//nWUUqxZs+ZD162vrw8gcwU/kvR9vb29H/r10h5
2024-10-05 01:43:27 +04:00
"text/plain": [
"<Figure size 400x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
2024-10-05 03:59:09 +04:00
"image/png": "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
2024-10-05 01:43:27 +04:00
"text/plain": [
"<Figure size 400x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
2024-10-05 03:59:09 +04:00
"image/png": "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"text/plain": [
"<Figure size 400x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 400x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Создание диаграмм зависимости\n",
"for column in numeric_columns:\n",
" plt.figure(figsize=(4, 8)) # Установка размера графика\n",
" plt.scatter(df['Outcome'], df[column], alpha=0.5) # Создаем диаграмму рассеяния\n",
" plt.title(f'Зависимость {column} от Outcome(диабет)')\n",
" plt.xlabel('Outcome (0 = нет, 1 = да)')\n",
" plt.ylabel(column)\n",
" plt.xticks([0, 1]) # Установка меток по оси X\n",
" plt.grid() # Добавление сетки для удобства восприятия\n",
" plt.show() # Отображение графика"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Создадим выборки данных. разбивать будем относительно параметра Outcome, ведь это тот самый параметр по которому наша выборка разбивается на классы. И собственно е г о
]
},
{
"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Outcome\n",
"0 500\n",
"1 268\n",
"Name: count, dtype: int64\n",
"\n",
"Обучающая выборка: (460, 9)\n",
"Outcome\n",
"0 299\n",
"1 161\n",
"Name: count, dtype: int64\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 200x200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Контрольная выборка: (154, 9)\n",
"Outcome\n",
"0 101\n",
"1 53\n",
"Name: count, dtype: int64\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 200x200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Тестовая выборка: (154, 9)\n",
"Outcome\n",
"0 100\n",
"1 54\n",
"Name: count, dtype: int64\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 200x200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Вывод распределения количества наблюдений по меткам (классам)\n",
"print(df.Outcome.value_counts())\n",
"print()\n",
"\n",
"data = df.copy()\n",
"\n",
"df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
" data, stratify_colname=\"Outcome\", frac_train=0.60, frac_val=0.20, frac_test=0.20\n",
")\n",
"\n",
"print(\"Обучающая выборка: \", df_train.shape)\n",
"print(df_train.Outcome.value_counts())\n",
"counts = df_train['Outcome'].value_counts()\n",
"plt.figure(figsize=(2, 2))# Установка размера графика\n",
"plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)# Построение круговой диаграммы\n",
"plt.title('Распределение классов Outcome в обучающей выборке')# Добавление заголовка\n",
"plt.show()# Отображение графика\n",
"\n",
"print(\"Контрольная выборка: \", df_val.shape)\n",
"print(df_val.Outcome.value_counts())\n",
"counts = df_val['Outcome'].value_counts()\n",
"plt.figure(figsize=(2, 2))\n",
"plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
"plt.title('Распределение классов Outcome в контрольной выборке')\n",
"plt.show()\n",
"\n",
"print(\"Тестовая выборка: \", df_test.shape)\n",
"print(df_test.Outcome.value_counts())\n",
"counts = df_test['Outcome'].value_counts()\n",
"plt.figure(figsize=(2, 2))\n",
"plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
"plt.title('Распределение классов Outcome в тестовой выборке')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"распределение неплохое но могло бы быть лучше. Поэтому применим балансировку "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"аугментация данных оверсемплингом(Этот метод увеличивает количество примеров меньшинства)"
]
},
{
"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Обучающая выборка после oversampling: (579, 9)\n",
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"Outcome\n",
"0 299\n",
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"1 280\n",
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"Name: count, dtype: int64\n"
]
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 200x200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Применение ADASYN\n",
"X_resampled, y_resampled = ada.fit_resample(df_train.drop(columns=['Outcome']), df_train['Outcome'])\n",
"\n",
"# Создание нового DataFrame\n",
"df_train_adasyn = pd.DataFrame(X_resampled)\n",
"df_train_adasyn['Outcome'] = y_resampled # Добавление целевой переменной\n",
"\n",
"# Вывод информации о
"print(\"Обучающая выборка после oversampling: \", df_train_adasyn.shape)\n",
"print(df_train_adasyn['Outcome'].value_counts())\n",
"counts = df_train_adasyn['Outcome'].value_counts()\n",
"plt.figure(figsize=(2, 2))\n",
"plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
"plt.title('Распределение классов Outcome в тренировачной выборке после ADASYN')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"проведём также балансировку данных методом андерсемплинга. Этот метод помогает сбалансировать выборку, уменьшая количество экземпляров класса большинства, чтобы привести е г о с
]
},
{
"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Обучающая выборка после undersampling: (322, 9)\n",
"Outcome\n",
"0 161\n",
"1 161\n",
"Name: count, dtype: int64\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 200x200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Применение RandomUnderSampler\n",
"X_resampled, y_resampled = rus.fit_resample(df_train.drop(columns=['Outcome']), df_train['Outcome'])\n",
"\n",
"# Создание нового DataFrame\n",
"df_train_undersampled = pd.DataFrame(X_resampled)\n",
"df_train_undersampled['Outcome'] = y_resampled # Добавление целевой переменной\n",
"\n",
"# Вывод информации о
"print(\"Обучающая выборка после undersampling: \", df_train_undersampled.shape)\n",
"print(df_train_undersampled['Outcome'].value_counts())\n",
"\n",
"# Визуализация распределения классов\n",
"counts = df_train_undersampled['Outcome'].value_counts()\n",
"plt.figure(figsize=(2, 2))\n",
"plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
"plt.title('Распределение классов Outcome в тренировочной выборке после Undersampling')\n",
"plt.show()"
]
2024-10-05 03:59:09 +04:00
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3 Датасет: Stroke Prediction Dataset\n",
"#### https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset\n",
"\n",
"According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths.\n",
"This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. Each row in the data provides relavant information about the patient.\n",
"\n",
"- Из этого описания очевидно что объектами иследования являются реальные пациенты.\n",
"- Атрибуты объектов: id, gender, age, hypertension, heart_disease, ever_married, work_type, Residence_type, avg_glucose_level, bmi, smoking_status, stroke\n",
"- Очевидная цель этого датасета - это научиться определять будет у
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"количество колонок: 12\n",
"колонки: id, gender, age, hypertension, heart_disease, ever_married, work_type, Residence_type, avg_glucose_level, bmi, smoking_status, stroke\n"
]
}
],
"source": [
"import pandas as pd\n",
"df = pd.read_csv(\"..//static//csv//healthcare-dataset-stroke-data.csv\", sep=\",\")\n",
"print('количество колонок: ' + str(df.columns.size)) \n",
"print('колонки: ' + ', '.join(df.columns))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Получение сведений о
"\n",
"Типы пропущенных данных:\n",
"- None - представление пустых данных в Python\n",
"- NaN - представление пустых данных в Pandas\n",
"- '' - пустая строка"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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 1544\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 True\n",
"stroke False\n",
"dtype: bool\n",
"\n",
"bmi процент пустых значений: %3.93\n",
"smoking_status процент пустых значений: %30.22\n"
]
}
],
"source": [
"import numpy as np\n",
"# Замена значений 'Unknown' на np.nan\n",
"df.replace('Unknown', np.nan, inplace=True)\n",
"\n",
"# Количество пустых значений признаков\n",
"print(df.isnull().sum())\n",
"print()\n",
"\n",
"# Есть ли пустые значения признаков\n",
"print(df.isnull().any())\n",
"print()\n",
"\n",
"# Процент пустых значений признаков\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": [
"пропуски есть и их не мало, с
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Удаление наблюдений с
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(3426, 12)\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 False\n",
"smoking_status False\n",
"stroke False\n",
"dtype: bool\n"
]
}
],
"source": [
"dropna_df = df.dropna()\n",
"print(dropna_df.shape)\n",
"print(dropna_df.isnull().any())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"срезалось ~2000 записей, что не круто когда записей всего ~5000.\n",
"- Попробуем дополнить базу данных заполнив пропуски"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['formerly smoked' 'never smoked' 'smokes' nan]\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",
"\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 False\n",
"smoking_status False\n",
"stroke False\n",
"dtype: bool\n",
"\n",
"['formerly smoked' 'never smoked' 'smokes']\n"
]
}
],
"source": [
"print(df['smoking_status'].unique())\n",
"# Замена пустых данных на медиану\n",
"df[\"bmi\"] = df[\"bmi\"].fillna(df[\"bmi\"].median())\n",
"\n",
"# Заполнение с
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.pipeline import Pipeline\n",
"\n",
"# Определение категориальных и числовых столбцов\n",
"categorical_features = ['gender', 'ever_married', 'work_type', 'Residence_type'] \n",
"numeric_features = ['avg_glucose_level', 'hypertension', 'heart_disease', 'age', 'bmi']\n",
"\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"# Добавляем StandardScaler для числовых признаков\n",
"preprocessor = ColumnTransformer(\n",
" transformers=[\n",
" ('cat', OneHotEncoder(), categorical_features),\n",
" ('num', StandardScaler(), numeric_features) # Применение скейлера к числовым признакам\n",
" ],\n",
" remainder='passthrough'\n",
")\n",
"\n",
"# Создание пайплайна для обучения модели\n",
"pipeline = Pipeline(steps=[\n",
" ('preprocessor', preprocessor),\n",
" ('classifier', LogisticRegression())\n",
"])\n",
"\n",
"# Разделение данных на обучающую выборку и тестовую, исключая smoking_status = 0 из обучающей выборки\n",
"known_smoking_status = df.dropna() # только те строки, где smoking_status != 0\n",
"unknown_smoking_status = df[df['smoking_status'].isnull()] # строки с
"\n",
"# Определяем признаки для обучения (категориальные и числовые)\n",
"X_train = known_smoking_status[categorical_features + numeric_features]\n",
"y_train = known_smoking_status['smoking_status']\n",
"\n",
"# Данные для предсказания\n",
"X_test = unknown_smoking_status[categorical_features + numeric_features]\n",
"\n",
"# Обучение модели через пайплайн\n",
"pipeline.fit(X_train, y_train)\n",
"\n",
"# Предсказание пропущенных значений\n",
"df.loc[df['smoking_status'].isnull(), 'smoking_status'] = pipeline.predict(X_test)\n",
"\n",
"# Количество пустых значений признаков\n",
"print(df.isnull().sum())\n",
"print()\n",
"\n",
"# Есть ли пустые значения признаков\n",
"print(df.isnull().any())\n",
"print()\n",
"print(df['smoking_status'].unique())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"посмотрим выбросы и усредним их:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Колонка age:\n",
" Есть выбросы: Нет\n",
" Количество выбросов: 0\n",
" Минимальное значение: 0.08\n",
" Максимальное значение: 82.0\n",
" 1-й квартиль (Q1): 25.0\n",
" 3-й квартиль (Q3): 61.0\n",
"\n",
"Колонка avg_glucose_level:\n",
" Есть выбросы: Да\n",
" Количество выбросов: 627\n",
" Минимальное значение: 55.12\n",
" Максимальное значение: 169.35750000000002\n",
" 1-й квартиль (Q1): 77.245\n",
" 3-й квартиль (Q3): 114.09\n",
"\n",
"Колонка bmi:\n",
" Есть выбросы: Да\n",
" Количество выбросов: 126\n",
" Минимальное значение: 10.300000000000006\n",
" Максимальное значение: 46.29999999999999\n",
" 1-й квартиль (Q1): 23.8\n",
" 3-й квартиль (Q3): 32.8\n",
"\n"
]
}
],
"source": [
"numeric_columns = ['age', 'avg_glucose_level', 'bmi']\n",
"for column in numeric_columns:\n",
" if pd.api.types.is_numeric_dtype(df[column]): # Проверяем, является ли колонка числовой\n",
" q1 = df[column].quantile(0.25) # Находим 1-й квартиль (Q1)\n",
" q3 = df[column].quantile(0.75) # Находим 3-й квартиль (Q3)\n",
" iqr = q3 - q1 # Вычисляем межквартильный размах (IQR)\n",
"\n",
" # Определяем границы для выбросов\n",
" lower_bound = q1 - 1.5 * iqr # Нижняя граница\n",
" upper_bound = q3 + 1.5 * iqr # Верхняя граница\n",
"\n",
" # Подсчитываем количество выбросов\n",
" outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]\n",
" outlier_count = outliers.shape[0]\n",
" \n",
" # Устраняем выбросы: заменяем значения ниже нижней границы на саму нижнюю границу, а
" df[column] = df[column].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)\n",
"\n",
" print(f\"Колонка {column}:\")\n",
" print(f\" Есть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n",
" print(f\" Количество выбросов: {outlier_count}\")\n",
" print(f\" Минимальное значение: {df[column].min()}\")\n",
" print(f\" Максимальное значение: {df[column].max()}\")\n",
" print(f\" 1-й квартиль (Q1): {q1}\")\n",
" print(f\" 3-й квартиль (Q3): {q3}\\n\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"построим графики в надежде найти какие то зависимости инсульта от других колонок"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAq4AAAIjCAYAAADC0ZkAAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABXbUlEQVR4nO3deXwU9f3H8ffskd3cB5CEcAVBQQVBQBER8UCoovU+URCp2oontVarIuKBR1U8UItW1Ao/LbXUVgVBBA9EVBAEFQoURDkSQsid7G525/dHzOqSDazsZJc1r+fjwQN39p2ZTzbtw7eT78wYpmmaAgAAAA5wtngPAAAAAESC4goAAICEQHEFAABAQqC4AgAAICFQXAEAAJAQKK4AAABICBRXAAAAJASKKwAAABICxRUAAAAJgeIKAMB+uPzyy5WWlhbvMYBWheIKtDLPPvusRowYoby8PDmdTuXn52vo0KF6+eWXFQgE4j0eYKlZs2Zp6tSp8R4DgEUc8R4AQGy99NJLat++ve68805lZGSorKxMn3zyiS6//HLNnTtX//d//xfvEQHLzJo1S2vWrNGNN94Y71EAWIDiCrQyH3zwgZxOZ8i266+/Xm3atNFTTz2lKVOmqLCwMD7DAXFUV1enpKQk2Wz8MhI4UPH/TqCV2bO0Nmosqz/9l/Ybb7yhkSNHqqCgQC6XS926ddM999wjv98f8rUnnHCCDMMI/mnbtq1GjhypNWvWhOQMw9CkSZNCtj388MMyDEMnnHBCyPa6ujpNmjRJhxxyiNxut9q3b69zzjlHGzdulCRt3rxZhmHoxRdfDPm68ePHyzAMXX755cFtL774ogzDUFJSknbu3BmSX7p0aXDuzz//POS92bNnq3///kpOTlbbtm116aWXauvWrU0+u7Vr1+qCCy5Qu3btlJycrB49euj222+XJE2aNCnkswn3Z/HixcHPsVevXk32H4kPP/xQ559/vjp37iyXy6VOnTrppptuUm1tbZPs7Nmzddhhh8ntdqtXr16aM2eOLr/88ib/wRIIBDR16lQdfvjhcrvdysvL09VXX63du3dHNNN7772nIUOGKDU1VVlZWTrzzDP1zTffBN//OZ9NOJWVlbrxxhtVWFgol8ul3NxcnXLKKVqxYoWkhs/zrbfe0rfffhvcX+P3uHjxYhmGoVdffVV33HGHOnTooJSUFFVUVAQ/o0h+9ntauXKl2rVrpxNOOEFVVVWSpK1bt+qKK65QXl6eXC6XDj/8cL3wwgsRfYYAQnHGFWilysrKVF9fr8rKSi1fvlx//vOfddFFF6lz587BzIsvvqi0tDRNmDBBaWlpeu+99zRx4kRVVFTo4YcfDtlfz549dfvtt8s0TW3cuFGPPvqoTjvtNG3ZsmWvM0yZMqXJdr/fr9NPP10LFy7URRddpBtuuEGVlZVasGCB1qxZo27duoXd34YNG/Tcc881ezy73a5XXnlFN910U3DbjBkz5Ha7VVdXF5J98cUXNXbsWB111FGaMmWKioqK9Pjjj2vJkiX64osvlJWVJUn68ssvNWTIEDmdTl111VUqLCzUxo0b9Z///Ef33XefzjnnHHXv3j2435tuukmHHnqorrrqquC2Qw89tNmZIzV79mzV1NTod7/7ndq0aaNPP/1UTz75pL7//nvNnj07mHvrrbd04YUXqnfv3poyZYp2796tcePGqUOHDk32efXVVwc/h+uvv16bNm3SU089pS+++EJLlixp9j+CJOndd9/VqaeeqoMOOkiTJk1SbW2tnnzySQ0ePFgrVqxQYWFh1J/Nb3/7W/3jH//Qtddeq8MOO0y7du3SRx99pG+++Ub9+vXT7bffrvLycn3//fd67LHHJKnJxVT33HOPkpKSdPPNN8vj8SgpKSnin/2ePvvsM40YMUIDBgzQG2+8oeTkZBUVFemYY46RYRi69tpr1a5dO82dO1fjxo1TRUUFSxiAn8sE0Cr16NHDlBT8M3r0aNPn84Vkampqmnzd1VdfbaakpJh1dXXBbUOHDjWHDh0akvvTn/5kSjKLi4uD2ySZd911V/D1LbfcYubm5pr9+/cP+foXXnjBlGQ++uijTY4fCARM0zTNTZs2mZLMGTNmBN+74IILzF69epmdOnUyx4wZE9w+Y8YMU5J58cUXm7179w5ur66uNjMyMsxLLrnElGR+9tlnpmmaptfrNXNzc81evXqZtbW1wfybb75pSjInTpwY3Hb88ceb6enp5rfffht2zj116dIlZLafGjp0qHn44YeHfW9fwv2spkyZYhqGETJb7969zY4dO5qVlZXBbYsXLzYlmV26dAlu+/DDD01J5syZM0P2OW/evLDb99S3b18zNzfX3LVrV3DbqlWrTJvNZo4ePTrs1+ztswknMzPTHD9+/F4zI0eODPm+Gi1atMiUZB500EEhn93P+dmPGTPGTE1NNU3TND/66CMzIyPDHDlyZMj/N8aNG2e2b9/eLCkpCTn+RRddZGZmZob9uQFoHksFgFZqxowZWrBggWbOnKlx48Zp5syZIWe6JCk5OTn4z5WVlSopKdGQIUNUU1OjtWvXhmR9Pp9KSkq0c+dOLV26VHPmzNERRxyhtm3bhj3+1q1b9eSTT+rOO+9schbs9ddfV9u2bXXdddc1+TrDMMLub/ny5Zo9e7amTJnS7BrFyy67TGvXrg0uCXj99deVmZmpk08+OST3+eefq7i4WNdcc43cbndw+8iRI9WzZ0+99dZbkqSdO3fqgw8+0BVXXBFypnpvc+6L3+9XSUmJSkpK5PV6I/66n/6sqqurVVJSomOPPVamaeqLL76QJG3btk2rV6/W6NGjQz7zoUOHqnfv3iH7mz17tjIzM3XKKacE5ykpKVH//v2VlpamRYsWNTvL9u3btXLlSl1++eXKyckJbj/iiCN0yimn6O233474+9qbrKwsLVu2TNu2bdvvfYwZMybks4v0Z/9TixYt0ogRI3TyySfrn//8p1wulyTJNE29/vrrOuOMM2SaZsjnOGLECJWXlweXNQCIDMUVaKUGDRqkYcOG6ZJLLtHzzz+vyZMna8aMGVqyZEkw89VXX+nss89WZmamMjIy1K5dO1166aWSpPLy8pD9ffzxx2rXrp1yc3N17LHHqr6+XrNnz262wN11110qKCjQ1Vdf3eS9jRs3qkePHnI4Il/NdOutt2rIkCE6/fTTm820a9dOI0eODK4vfOGFFzRmzJgmRffbb7+VJPXo0aPJPnr27Bl8/3//+58k7fe61HDWrl2rdu3ahayXnTVr1j6/bsuWLcGimJaWpnbt2mno0KGSfvxZNc7901/PN9pz2/r161VeXq7c3NzgPI1/qqqqVFxc3Owse/v8Dj30UJWUlKi6unqf39O+PPTQQ1qzZo06deqko48+WpMmTQr+TCLVtWvXkNeR/uwb1dXVaeTIkTryyCP197//XUlJScH3du7cqbKyMk2fPr3JZzh27FhJ2uvnCKAp1rgCkCSdd955uv3227Vs2TINHjxYZWVlGjp0qDIyMjR58mR169ZNbrdbK1as0B//+Mcm93w94ogj9Mgjj0hq+Bf2E088oRNOOEErVqxQfn5+SPabb77Riy++qFdeeWWv6yQjNX/+fL377rtaunTpPrNXXHGFRo8ereuuu04ffPCBnn/+eX344YdRz2CVwsLC4DrdXbt26YknntBll12mgw46SMccc0zYr/H7/TrllFNUWlqqP/7xj+rZs6dSU1O1detWXX755ft1f95AIKDc3FzNnDkz7Pvt2rX72fu02gUXXKAhQ4Zozpw5mj9/vh5++GE9+OCD+uc//6lTTz01on389Gzr/nC5XDrttNP0xhtvaN68eSH/4dT4uV966aUaM2ZM2K8/4ogjojo+0NpQXAFIUvDqc7vdLqnhqutdu3bpn//8p44//vhgbtOmTWG/Pjs7W8OGDQu+PuGEE1RQUKAZM2botttuC8nedttt6tu3ry688MKw++rWrZuWLVsmn8+3z2JrmqZuvfVWnX322c0Wu5869dRT5Xa7ddFFF+m4445Tt27dmhTXLl26SJLWrVunk04
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAsAAAAJ4CAYAAABrtlX3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABrpklEQVR4nO3deVRU9ePG8WdAQRQBSRBNRRQ3XHItcd8SlyLTFpdS0zJNM8VM/VaumVvlkpatWrlmaZqWG+WO5m7uSyhuoGWIuIDA/f3RcX5NoDE2w6D3/TpnznHu/czMM8OUj5fP/VyLYRiGAAAAAJNwc3UAAAAAICdRgAEAAGAqFGAAAACYCgUYAAAApkIBBgAAgKlQgAEAAGAqFGAAAACYCgUYAAAApkIBBgAAgKlQgAEADpWcnKzJkydb7ycmJmr69OmuC3QXGzFihCwWi37//XdXRwHuKRRgIBeYNWuWLBaLtm/fnmnfJ598IovForZt2yo9Pd0F6QD7eHl56Y033tCcOXN06tQpjRgxQt9//72rY5nagQMHNGLECJ04ccLVUYBcIY+rAwC4tcWLF6t3795q0KCB5s+fL3d3d1dHAv6Vu7u7Ro4cqS5duigjI0M+Pj5avny5q2OZ2oEDBzRy5Eg1btxYpUqVcnUcwOU4AgzkUmvXrlXHjh0VFham77//Xvny5XN1JCDbBg4cqJMnT2rz5s06efKk6tev7+pId5UrV664OgJwT6MAA7nQ7t279dhjj6lo0aJauXKlfH19M41ZuHChatasKS8vLxUuXFjPPPOMzpw5k+XzWSyWLG9//3WoxWLRiBEjbB43ceJEWSwWNW7c2Lrt5pzEfypVqpS6detmsy0xMVH9+/dXiRIl5OnpqdDQUI0fP14ZGRk24zIyMjRlyhRVqVJF+fLlU0BAgFq2bGmdEnKr/DdvN/OtXbvWZrunp6fKlSunsWPHyjAMm9fctWuXWrVqJR8fH3l7e6tZs2basmVLlp/f3x0+fFhNmzZVUFCQPD09VaJECfXq1UsXL160jrmZ45tvvsn0eG9vb5vP6eLFi3r11VdVpUoVeXt7y8fHR61atdKePXtsHufoz/3EiROyWCx65513Mj1n5cqVbX7mN9/P2rVrbca1adMm0/fm7zmLFy+u8PBw5cmTR0FBQVk+x600btw4y5/1P7+j0v9PIbrV9+KmXbt2qWXLlgoICLAZ98gjj9w2S40aNdSuXTubbVWqVJHFYtHevXut2xYsWCCLxaKDBw/avOa/fc9u5l+3bp1eeuklBQYGqnjx4rfMc/LkSYWGhqpy5cpKSEi4bfabz//kk09Kkpo0aWJ932vXrlXXrl1VuHBh3bhxI9PjWrRoofLly1vvWywW9e3bV3PmzFH58uWVL18+1axZU+vXr8/02DNnzqh79+4qUqSIPD09ValSJX3++ef/mhXIKUyBAHKZ48ePq2XLlvL09NTKlStVtGjRTGNmzZql5557TrVr19bYsWOVkJCgKVOmaNOmTdq1a5f8/PwyPebxxx+3/iW+YcMGffzxx7fNkZiYqLFjx97x+7h69aoaNWqkM2fO6MUXX1TJkiW1efNmDR06VOfOnbM5SapHjx6aNWuWWrVqpeeff15paWnasGGDtmzZolq1aumrr76yjr2ZfdKkSSpcuLAkqUiRIjav/b///U8VK1bUtWvXtGDBAv3vf/9TYGCgevToIUnav3+/GjRoIB8fH7322mvKmzevPvroIzVu3Fjr1q3TQw89dMv3deXKFRUvXlyPPvqofHx8tG/fPk2fPl1nzpy5o3muv/32m7777js9+eSTCgkJUUJCgj766CM1atRIBw4cULFixex6Pns+9/9i/fr1+uGHH7I19t13381WUfun4sWLW7+DycnJ6t27923H//07MWbMGJt9ly5dUqtWrWQYhqKiolSiRAlJ0oABA/41R4MGDTRv3jzr/YsXL2r//v1yc3PThg0bVLVqVUl/fTcDAgJUsWJFSfZ/z1566SUFBARo2LBhtzwCfPz4cTVt2lT+/v5avXq19f3eTsOGDdWvXz9NnTrV+t+GJFWsWFHPPvusvvzyS61cudLmHwLx8fH66aefNHz4cJvnWrdunRYsWKB+/frJ09NTH3zwgVq2bKlffvlFlStXliQlJCSoTp061sIcEBCgH3/8UT169FBSUpL69+//r5kBpzMAuNzMmTMNScayZcuMMmXKGJKMFi1aZDk2NTXVCAwMNCpXrmxcu3bNun3ZsmWGJGPYsGE242/cuGFIMkaOHJnp9WJjY63bJBnDhw+33n/ttdeMwMBAo2bNmkajRo2s20eOHGlIMjIyMmxeJzg42Ojatav1/ujRo40CBQoYR44csRk3ZMgQw93d3YiLizMMwzB++uknQ5LRr1+/TO/1n69xq+w3/fzzz4Yk4+eff7Zuu379uuHm5ma89NJL1m1t27Y1PDw8jOPHj1u3nT171ihYsKDRsGHDTM/7b1566SXD29s7U46FCxdmGlugQAGbz+n69etGenq6zZjY2FjD09PTGDVqlHWboz/32NhYQ5IxceLETBkrVapk8zPP6nN96KGHjFatWmX63gwfPtz4+18t58+fNwoWLGgd+/fnuJ26desalStXtt6/cOFCpte66ZNPPjEkGSdPnrRua9Sokc17WLlypSHJmDdvns1jg4ODjTZt2tw2y8KFCw1JxoEDBwzDMIylS5canp6eRmRkpPH0009bx1WtWtV4/PHHrfez+z27+Z2uX7++kZaWZvPaNz/PCxcuGAcPHjSKFStm1K5d27h48eJtM9/qPfzz809PTzeKFy9u8z4MwzDee+89w2KxGL/99pt1myRDkrF9+3brtpMnTxr58uWzed89evQwihYtavz+++82z9mhQwfD19fXuHr1ql3ZAWdgCgSQi3Tr1k2nTp1Sp06dtGrVKi1cuDDTmO3bt+v8+fN66aWXbOYFt2nTRhUqVMh0slFqaqokydPTM9s5zpw5o/fff19vvvmmvL29bfYFBgZKkk6fPn3b51i4cKEaNGigQoUK6ffff7femjdvrvT0dOuvTb/99ltZLJZMR5okZfkr/+y4dOmSfv/9d8XFxWnChAnKyMhQ06ZNJUnp6elatWqV2rZtq9KlS1sfU7RoUXXq1EkbN25UUlJStl4jISFB0dHRWr58uRo2bJhpzOXLl23ee1ZLWXl6esrNzc2a7Y8//pC3t7fKly+vnTt3Wsc5+nO/6erVq5ky/ttqI4sWLdK2bds0bty4246TpNGjR8vX11f9+vX717F/d/369WzPe8/Od/zy5cuSpPvuu8+uHNJfR4AlWT+7DRs2qHbt2nr44Ye1YcMGSX/9xmTfvn3WsXfyPXvhhRdueaLrvn371KhRI5UqVUpr1qxRoUKF7H4fWXFzc1Pnzp21dOlS62ckSXPmzFHdunUVEhJiMz48PFw1a9a03i9ZsqQee+wxrVy5Uunp6TIMQ99++60effRRGYZh872KiIjQpUuXbL7XgKtQgIFc5OLFi5o9e7a++OILVatWTa+88oouXbpkM+bkyZOSZDM376YKFSpY99+UmJgoSZmK7O0MHz5cxYoV04svvphpX3h4uCwWi4YOHaoTJ05Y/3L757zeo0ePasWKFQoICLC5NW/eXJJ0/vx5SX/9SrdYsWLy9/fPdr5/07ZtWwUEBCg4OFgjRozQG2+8ofbt20uSLly4oKtXr2b5+VWsWFEZGRk6derUv75GRESEgoKC1Lx5c1WsWFELFizINKZ79+6Z3v8/f7WdkZGhSZMmqWzZsvL09FThwoUVEBCgvXv32vzsHf253zR8+PBMYw8dOnTL952enq7//e9/6ty5s/VX/7cSGxurjz76SCNHjrT7JM7ff/89y7nvWcnOd7xWrVrKmzevRowYoV27dt3y88tKkSJFVLZsWWvZ3bBhgxo0aKCGDRvq7Nmz+u2337Rp0yZlZGRYC/CdfM/+WTb/7tFHH1XBggW1cuVK+fj4/Gtme3Tp0kXXrl3T4sWLJf01z33Hjh169tlnM40tW7Zspm3lypXT1atXdeHCBV24cEGJiYn
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Список колонок для построения графиков\n",
"columns = ['gender', 'age', 'hypertension', 'heart_disease', 'ever_married',\n",
" 'work_type', 'Residence_type', 'avg_glucose_level', 'bmi',\n",
" 'smoking_status']\n",
"\n",
"# Создание диаграмм зависимости\n",
"for column in columns:\n",
" plt.figure(figsize=(8, 6)) # Установка размера графика\n",
" if pd.api.types.is_numeric_dtype(df[column]): # Проверяем, является ли колонка числовой\n",
" # Проверяем, содержит ли колонка только два уникальных значения (0 и 1)\n",
" if df[column].nunique() == 2 and set(df[column].unique()).issubset({0, 1}):\n",
" # Если да, то строим столбчатую диаграмму\n",
" counts = df[column].value_counts() # Считаем количество повторений каждого значения\n",
" counts.plot(kind='bar') # Создаем столбчатую диаграмму\n",
" plt.title(f'Количество значений для {column}')\n",
" plt.xlabel(column)\n",
" plt.ylabel('Количество повторений')\n",
" else:\n",
" # Если колонка числовая, создаем диаграмму рассеяния\n",
" plt.scatter(df['stroke'], df[column], alpha=0.5) # Создаем диаграмму рассеяния\n",
" plt.title(f'Зависимость {column} от stroke')\n",
" plt.xlabel('stroke (0 = нет, 1 = да)')\n",
" plt.ylabel(column)\n",
" plt.xticks([0, 1]) # Установка меток по оси X\n",
" plt.grid() # Добавление сетки для удобства восприятия\n",
" else:\n",
" # Если колонка не числовая, строим столбчатую диаграмму\n",
" counts = df[column].value_counts() # Считаем количество повторений каждого значения\n",
" counts.plot(kind='bar') # Создаем столбчатую диаграмму\n",
" plt.title(f'Количество значений для {column}')\n",
" plt.xlabel(column)\n",
" plt.ylabel('Количество повторений')\n",
"\n",
" plt.show() # Отображение графика"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Создадим выборки данных. разбивать будем относительно параметра инсульта, ведь это тот самый параметр по которому наша выборка разбивается на классы. И собственно е г о
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"stroke\n",
"0 4861\n",
"1 249\n",
"Name: count, dtype: int64\n",
"\n",
"Обучающая выборка: (3066, 12)\n",
"stroke\n",
"0 2917\n",
"1 149\n",
"Name: count, dtype: int64\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 200x200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Контрольная выборка: (1022, 12)\n",
"stroke\n",
"0 972\n",
"1 50\n",
"Name: count, dtype: int64\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 200x200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Тестовая выборка: (1022, 12)\n",
"stroke\n",
"0 972\n",
"1 50\n",
"Name: count, dtype: int64\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 200x200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Вывод распределения количества наблюдений по меткам (классам)\n",
"print(df.stroke.value_counts())\n",
"print()\n",
"\n",
"data = df.copy()\n",
"\n",
"df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
" data, stratify_colname=\"stroke\", frac_train=0.60, frac_val=0.20, frac_test=0.20\n",
")\n",
"\n",
"print(\"Обучающая выборка: \", df_train.shape)\n",
"print(df_train.stroke.value_counts())\n",
"counts = df_train['stroke'].value_counts()\n",
"plt.figure(figsize=(2, 2))# Установка размера графика\n",
"plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)# Построение круговой диаграммы\n",
"plt.title('Распределение классов stroke в обучающей выборке')# Добавление заголовка\n",
"plt.show()# Отображение графика\n",
"\n",
"print(\"Контрольная выборка: \", df_val.shape)\n",
"print(df_val.stroke.value_counts())\n",
"counts = df_val['stroke'].value_counts()\n",
"plt.figure(figsize=(2, 2))\n",
"plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
"plt.title('Распределение классов stroke в контрольной выборке')\n",
"plt.show()\n",
"\n",
"print(\"Тестовая выборка: \", df_test.shape)\n",
"print(df_test.stroke.value_counts())\n",
"counts = df_test['stroke'].value_counts()\n",
"plt.figure(figsize=(2, 2))\n",
"plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
"plt.title('Распределение классов stroke в тестовой выборке')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"плохое распределение, надо сбалансировать"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"аугментация данных оверсемплингом(Этот метод увеличивает количество примеров меньшинства)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Обучающая выборка после oversampling: (5831, 17)\n",
"stroke\n",
"0 2917\n",
"1 2914\n",
"Name: count, dtype: int64\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 600x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"categorical_features = ['gender', 'ever_married', 'work_type', 'Residence_type'] # Ваши категориальные признаки\n",
"numeric_features = ['age', 'hypertension', 'heart_disease', 'avg_glucose_level', 'bmi'] # Ваши числовые признаки\n",
"\n",
"# Создание пайплайна для обработки категориальных данных\n",
"preprocessor = ColumnTransformer(\n",
" transformers=[\n",
" ('cat', OneHotEncoder(), categorical_features), # OneHotEncoder для категориальных данных\n",
" ('num', 'passthrough', numeric_features) # Оставляем числовые колонки без изменений\n",
" ]\n",
")\n",
"\n",
"# Создание экземпляра ADASYN\n",
"ada = ADASYN()\n",
"\n",
"# Преобразование данных с
"X = preprocessor.fit_transform(df_train.drop(columns=['stroke']))\n",
"y = df_train['stroke']\n",
"\n",
"# Применение ADASYN\n",
"X_resampled, y_resampled = ada.fit_resample(X, y)\n",
"\n",
"# Создание нового DataFrame\n",
"df_train_adasyn = pd.DataFrame(X_resampled)\n",
"# Восстанавливаем названия столбцов для DataFrame\n",
"ohe_columns = preprocessor.named_transformers_['cat'].get_feature_names_out(categorical_features)\n",
"new_column_names = list(ohe_columns) + numeric_features\n",
"df_train_adasyn.columns = new_column_names\n",
"\n",
"# Добавление целевой переменной\n",
"df_train_adasyn['stroke'] = y_resampled\n",
"\n",
"# Вывод информации о
"print(\"Обучающая выборка после oversampling: \", df_train_adasyn.shape)\n",
"print(df_train_adasyn['stroke'].value_counts())\n",
"\n",
"# Визуализация\n",
"counts = df_train_adasyn['stroke'].value_counts()\n",
"plt.figure(figsize=(6, 6))\n",
"plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
"plt.title('Распределение классов Outcome в тренировачной выборке после ADASYN')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"проведём также балансировку данных методом андерсемплинга. Этот метод помогает сбалансировать выборку, уменьшая количество экземпляров класса большинства, чтобы привести е г о с
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Обучающая выборка после undersampling: (298, 12)\n",
"stroke\n",
"0 149\n",
"1 149\n",
"Name: count, dtype: int64\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 200x200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"array(['Male', 'Female'], dtype=object)"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Применение RandomUnderSampler\n",
"X_resampled, y_resampled = rus.fit_resample(df_train.drop(columns=['stroke']), df_train['stroke'])\n",
"\n",
"# Создание нового DataFrame\n",
"df_train_undersampled = pd.DataFrame(X_resampled)\n",
"df_train_undersampled['stroke'] = y_resampled # Добавление целевой переменной\n",
"\n",
"# Вывод информации о
"print(\"Обучающая выборка после undersampling: \", df_train_undersampled.shape)\n",
"print(df_train_undersampled['stroke'].value_counts())\n",
"\n",
"# Визуализация распределения классов\n",
"counts = df_train_undersampled['stroke'].value_counts()\n",
"plt.figure(figsize=(2, 2))\n",
"plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
"plt.title('Распределение классов Outcome в тренировочной выборке после Undersampling')\n",
"plt.show()"
]
2024-10-04 21:59:56 +04:00
}
],
"metadata": {
"kernelspec": {
"display_name": "laba",
"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": "3.12.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
