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AIM-PIbd-32-Shabunov-O-A/lab_3/lab3.ipynb
olshab da44b6c41a Роддом. У мужа рожает жена, он сильно волнуется... 
Роддом. У мужа рожает жена, он сильно волнуется.
Выходит медсестра с ребенком на руках, берет его за ногу и бьет головой об пол.
Мужик в афиге.
А медесестра смотрит на него и говорит:
- Шучу, шучу, он мертвым родился.
2024-12-07 13:09:06 +04:00

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"# Лабораторная работа №3. Конструирование признаков.\n",
"\n",
"## Датасет \"Набор данных для анализа и прогнозирования сердечного приступа\".\n",
"\n",
"[**Ссылка**](https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease)\n",
"\n",
"### Описание датасета\n",
"\n",
"**Проблемная область**: Датасет связан с медицинской статистикой и направлен на анализ факторов, связанных с риском сердечного приступа. Это важно для прогнозирования и разработки стратегий профилактики сердечно-сосудистых заболеваний.\n",
"\n",
"**Актуальность**: Сердечно-сосудистые заболевания являются одной из ведущих причин смертности во всем мире. Анализ данных об образе жизни, состоянии здоровья и наследственных факторах позволяет выделить ключевые предикторы, влияющие на развитие сердечно-сосудистых заболеваний. Этот датасет предоставляет инструменты для анализа таких факторов и может быть полезен в создании прогнозных моделей, направленных на снижение рисков и своевременную диагностику.\n",
"\n",
"**Объекты наблюдения**: Каждая запись представляет собой данные о человеке, включая информацию об их состоянии здоровья, образе жизни, демографических характеристиках и наличию определенных заболеваний. Объекты наблюдений — это индивидуальные пациенты.\n",
"\n",
"**Атрибуты объектов:**\n",
"- `HeartDisease` — наличие сердечного приступа (Yes/No) (целевая переменная).\n",
"- `BMI` — индекс массы тела (Body Mass Index), числовой показатель.\n",
"- `Smoking` — курение (Yes/No).\n",
"- `AlcoholDrinking` — употребление алкоголя (Yes/No).\n",
"- `Stroke` — наличие инсульта (Yes/No).\n",
"- `PhysicalHealth` — количество дней в месяц, когда физическое здоровье было неудовлетворительным.\n",
"- `MentalHealth` — количество дней в месяц, когда психическое здоровье было неудовлетворительным.\n",
"- `DiffWalking` — трудности при ходьбе (Yes/No).\n",
"- `Sex` — пол (Male/Female).\n",
"- `AgeCategory` — возрастная категория (например, 55-59, 80 or older).\n",
"- `Race` — расовая принадлежность (например, White, Black).\n",
"- `Diabetic` — наличие диабета (Yes/No/No, borderline diabetes).\n",
"- `PhysicalActivity` — физическая активность (Yes/No).\n",
"- `GenHealth` — общее состояние здоровья (от Excellent до Poor).\n",
"- `SleepTime` — среднее количество часов сна за сутки.\n",
"- `Asthma` — наличие астмы (Yes/No).\n",
"- `KidneyDisease` — наличие заболеваний почек (Yes/No).\n",
"- `SkinCancer` — наличие кожного рака (Yes/No).\n",
"\n",
"### Бизнес-цели и соответствующие цели технического проекта\n",
"\n",
"**Бизнес-цель 1: Разработка персонализированных программ профилактики сердечно-сосудистых заболеваний**\n",
"\n",
"Снижение числа сердечно-сосудистых заболеваний в группе риска благодаря внедрению программ профилактики уменьшает затраты на медицинское обслуживание (страховые выплаты, лечение). Компании, предоставляющие страховые или медицинские услуги, могут минимизировать убытки и увеличить доходы за счет раннего выявления риска у клиентов.\n",
"\n",
"*Цели технического проекта*:\n",
"1. Построить предиктивную модель машинного обучения для прогнозирования риска сердечного приступа на основе предоставленных данных.\n",
"2. Разработать алгоритм классификации пациентов по группам риска с учетом их образа жизни, состояния здоровья и наследственных факторов.\n",
"3. Выявить наиболее значимые факторы риска для рекомендации адресных изменений в образе жизни.\n",
"\n",
"**Бизнес-цель 2: Создание коммерческого продукта для оценки здоровья сотрудников компаний**\n",
"\n",
"Продукт может быть предложен корпоративным клиентам для оценки состояния здоровья их сотрудников и снижения риска долгосрочных больничных листов, что положительно скажется на производительности и снизит страховые выплаты работодателей. Компании смогут предлагать услуги в формате подписки или единовременной оценки.\n",
"\n",
"*Цели технического проекта*:\n",
"1. Разработать инструмент визуализации здоровья сотрудников с использованием анализа ключевых факторов из датасета (например, курение, индекс массы тела, физическая активность).\n",
"2. Обучить и оптимизировать модель прогнозирования вероятности сердечного приступа в зависимости от корпоративного контекста (возрастные группы сотрудников, стрессовые факторы).\n",
"3. Интегрировать предиктивную аналитику в продукт, предоставляющий персонализированные отчеты и рекомендации по здоровью.\n",
"\n",
"**Бизнес-цель**: Улучшенное прогнозирование цен поможет продавцам устанавливать конкурентные цены, а покупателям — принимать более взвешенные решения о покупке. Это также даст риелторам возможность лучше ориентироваться на рынке и оптимизировать стратегию продажи.\n",
"\n",
"**Техническая цель**: Прогнозирование цен на жилье\n",
"\n",
"**Входные данные**: Исторические данные о продажах домов, включая все признаки (количество комнат, площадь, состояние, местоположение и др.).\n",
"\n",
"**Целевая переменная**: Столбец `HeartDisease`, который указывает на наличие сердечного приступа у пациента (`Yes` или `No`)."
]
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{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>HeartDisease</th>\n",
" <th>BMI</th>\n",
" <th>Smoking</th>\n",
" <th>AlcoholDrinking</th>\n",
" <th>Stroke</th>\n",
" <th>PhysicalHealth</th>\n",
" <th>MentalHealth</th>\n",
" <th>DiffWalking</th>\n",
" <th>Sex</th>\n",
" <th>AgeCategory</th>\n",
" <th>Race</th>\n",
" <th>Diabetic</th>\n",
" <th>PhysicalActivity</th>\n",
" <th>GenHealth</th>\n",
" <th>SleepTime</th>\n",
" <th>Asthma</th>\n",
" <th>KidneyDisease</th>\n",
" <th>SkinCancer</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>No</td>\n",
" <td>16.60</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>3.0</td>\n",
" <td>30.0</td>\n",
" <td>No</td>\n",
" <td>Female</td>\n",
" <td>55-59</td>\n",
" <td>White</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Very good</td>\n",
" <td>5.0</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>No</td>\n",
" <td>20.34</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>No</td>\n",
" <td>Female</td>\n",
" <td>80 or older</td>\n",
" <td>White</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Very good</td>\n",
" <td>7.0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>No</td>\n",
" <td>26.58</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>20.0</td>\n",
" <td>30.0</td>\n",
" <td>No</td>\n",
" <td>Male</td>\n",
" <td>65-69</td>\n",
" <td>White</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Fair</td>\n",
" <td>8.0</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>No</td>\n",
" <td>24.21</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>No</td>\n",
" <td>Female</td>\n",
" <td>75-79</td>\n",
" <td>White</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Good</td>\n",
" <td>6.0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>No</td>\n",
" <td>23.71</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>28.0</td>\n",
" <td>0.0</td>\n",
" <td>Yes</td>\n",
" <td>Female</td>\n",
" <td>40-44</td>\n",
" <td>White</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Very good</td>\n",
" <td>8.0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" HeartDisease BMI Smoking AlcoholDrinking Stroke PhysicalHealth \\\n",
"0 No 16.60 Yes No No 3.0 \n",
"1 No 20.34 No No Yes 0.0 \n",
"2 No 26.58 Yes No No 20.0 \n",
"3 No 24.21 No No No 0.0 \n",
"4 No 23.71 No No No 28.0 \n",
"\n",
" MentalHealth DiffWalking Sex AgeCategory Race Diabetic \\\n",
"0 30.0 No Female 55-59 White Yes \n",
"1 0.0 No Female 80 or older White No \n",
"2 30.0 No Male 65-69 White Yes \n",
"3 0.0 No Female 75-79 White No \n",
"4 0.0 Yes Female 40-44 White No \n",
"\n",
" PhysicalActivity GenHealth SleepTime Asthma KidneyDisease SkinCancer \n",
"0 Yes Very good 5.0 Yes No Yes \n",
"1 Yes Very good 7.0 No No No \n",
"2 Yes Fair 8.0 Yes No No \n",
"3 No Good 6.0 No No Yes \n",
"4 Yes Very good 8.0 No No No "
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"df = pd.read_csv(\".//static//csv//heart_2020_cleaned.csv\")\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Устранение проблемы пропущенных данных\n",
"\n",
"Для начала определим, присутствуют ли в датасете пропущенные значения признаков:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"HeartDisease 0\n",
"BMI 0\n",
"Smoking 0\n",
"AlcoholDrinking 0\n",
"Stroke 0\n",
"PhysicalHealth 0\n",
"MentalHealth 0\n",
"DiffWalking 0\n",
"Sex 0\n",
"AgeCategory 0\n",
"Race 0\n",
"Diabetic 0\n",
"PhysicalActivity 0\n",
"GenHealth 0\n",
"SleepTime 0\n",
"Asthma 0\n",
"KidneyDisease 0\n",
"SkinCancer 0\n",
"dtype: int64\n",
"\n",
"HeartDisease False\n",
"BMI False\n",
"Smoking False\n",
"AlcoholDrinking False\n",
"Stroke False\n",
"PhysicalHealth False\n",
"MentalHealth False\n",
"DiffWalking False\n",
"Sex False\n",
"AgeCategory False\n",
"Race False\n",
"Diabetic False\n",
"PhysicalActivity False\n",
"GenHealth False\n",
"SleepTime False\n",
"Asthma False\n",
"KidneyDisease False\n",
"SkinCancer 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": [
"Пропущенных данных в датасете **не обнаружено**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Устранение проблемы зашумленности данных\n",
"\n",
"**Зашумленность** – это наличие случайных ошибок или вариаций в данных, которые могут затруднить выявление истинных закономерностей. Шум может возникать из-за ошибок измерений, неправильных записей или других факторов.\n",
"\n",
"**Выбросы** – это значения, которые значительно отличаются от остальных наблюдений в наборе данных. Выбросы могут указывать на ошибки в данных или на редкие, но важные события. Их наличие может повлиять на статистические методы анализа.\n",
"\n",
"Представленный ниже код помогает определить наличие выбросов в наборе данных и устранить их (при наличии), заменив значения ниже нижней границы (рассматриваемого минимума) на значения нижней границы, а значения выше верхней границы (рассматриваемого максимума) – на значения верхней границы:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Проверка наличия выбросов в колонках:\n",
"Колонка BMI:\n",
"\tЕсть выбросы: Да\n",
"\tКоличество выбросов: 10396\n",
"\tМинимальное значение: 12.02\n",
"\tМаксимальное значение: 94.85\n",
"\t1-й квартиль (Q1): 24.03\n",
"\t3-й квартиль (Q3): 31.42\n",
"\n",
"Колонка PhysicalHealth:\n",
"\tЕсть выбросы: Да\n",
"\tКоличество выбросов: 47146\n",
"\tМинимальное значение: 0.0\n",
"\tМаксимальное значение: 30.0\n",
"\t1-й квартиль (Q1): 0.0\n",
"\t3-й квартиль (Q3): 2.0\n",
"\n",
"Колонка MentalHealth:\n",
"\tЕсть выбросы: Да\n",
"\tКоличество выбросов: 51576\n",
"\tМинимальное значение: 0.0\n",
"\tМаксимальное значение: 30.0\n",
"\t1-й квартиль (Q1): 0.0\n",
"\t3-й квартиль (Q3): 3.0\n",
"\n",
"Колонка SleepTime:\n",
"\tЕсть выбросы: Да\n",
"\tКоличество выбросов: 4543\n",
"\tМинимальное значение: 1.0\n",
"\tМаксимальное значение: 24.0\n",
"\t1-й квартиль (Q1): 6.0\n",
"\t3-й квартиль (Q3): 8.0\n",
"\n"
]
},
{
"data": {
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",
"text/plain": [
"<Figure size 1500x1000 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from math import ceil\n",
"\n",
"# Проверка выбросов в DataFrame\n",
"def check_outliers(dataframe, columns):\n",
" for column in columns:\n",
" if not pd.api.types.is_numeric_dtype(dataframe[column]): # Проверяем, является ли колонка числовой\n",
" continue\n",
" \n",
" Q1 = dataframe[column].quantile(0.25) # 1-й квартиль (25%)\n",
" Q3 = dataframe[column].quantile(0.75) # 3-й квартиль (75%)\n",
" IQR = Q3 - Q1 # Вычисляем межквартильный размах\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 = dataframe[(dataframe[column] < lower_bound) | (dataframe[column] > upper_bound)]\n",
" outlier_count = outliers.shape[0]\n",
"\n",
" print(f\"Колонка {column}:\")\n",
" print(f\"\\tЕсть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n",
" print(f\"\\tКоличество выбросов: {outlier_count}\")\n",
" print(f\"\\tМинимальное значение: {dataframe[column].min()}\")\n",
" print(f\"\\tМаксимальное значение: {dataframe[column].max()}\")\n",
" print(f\"\\t1-й квартиль (Q1): {Q1}\")\n",
" print(f\"\\t3-й квартиль (Q3): {Q3}\\n\")\n",
"\n",
"# Визуализация выбросов\n",
"def visualize_outliers(dataframe, columns):\n",
" # Диаграммы размахов\n",
" plt.figure(figsize=(15, 10))\n",
" rows = ceil(len(columns) / 3)\n",
" for index, column in enumerate(columns, 1):\n",
" plt.subplot(rows, 3, index)\n",
" plt.boxplot(dataframe[column], vert=True, patch_artist=True)\n",
" plt.title(f\"Диаграмма размаха для \\\"{column}\\\"\")\n",
" plt.xlabel(column)\n",
" \n",
" # Отображение графиков\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
"\n",
"# Числовые столбцы DataFrame\n",
"numeric_columns = [\n",
" 'BMI',\n",
" 'PhysicalHealth',\n",
" 'MentalHealth',\n",
" 'SleepTime'\n",
"]\n",
"\n",
"# Проверка наличия выбросов в колонках\n",
"print('Проверка наличия выбросов в колонках:')\n",
"check_outliers(df, numeric_columns)\n",
"visualize_outliers(df, numeric_columns)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Признаки `BMI` и `SleepTime` имеют достаточное количество выбросов, которое стоит **устранить**. Также числовые признаки `PhysicalHealth` и `MentalHealth` имеют большое количество выбросов, но так как количество таких наблюдений по сравнению с общим количеством объектов велико, а диапазон значений, которые эти признаки принимают, сравнительно небольшой, то удаление такого объема важной информации, как состояние здоровья, может **негативно сказаться на способности прогнозировать сердечный приступ**.\n",
"\n",
"Для решения проблемы выбросов у признаков `BMI` и `SleepTime` воспользуемся методом отсечения слишком отклоняющихся значений путем **замены на экстремальное значение соответствующей границы**:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Проверка наличия выбросов в колонках после их устранения:\n",
"Колонка BMI:\n",
"\tЕсть выбросы: Нет\n",
"\tКоличество выбросов: 0\n",
"\tМинимальное значение: 12.945\n",
"\tМаксимальное значение: 42.505\n",
"\t1-й квартиль (Q1): 24.03\n",
"\t3-й квартиль (Q3): 31.42\n",
"\n",
"Колонка SleepTime:\n",
"\tЕсть выбросы: Нет\n",
"\tКоличество выбросов: 0\n",
"\tМинимальное значение: 3.0\n",
"\tМаксимальное значение: 11.0\n",
"\t1-й квартиль (Q1): 6.0\n",
"\t3-й квартиль (Q3): 8.0\n",
"\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1500x1000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Устранить выборсы в DataFrame\n",
"def remove_outliers(dataframe, columns):\n",
" for column in columns:\n",
" if not pd.api.types.is_numeric_dtype(dataframe[column]): # Проверяем, является ли колонка числовой\n",
" continue\n",
" \n",
" Q1 = dataframe[column].quantile(0.25) # 1-й квартиль (25%)\n",
" Q3 = dataframe[column].quantile(0.75) # 3-й квартиль (75%)\n",
" IQR = Q3 - Q1 # Вычисляем межквартильный размах\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",
" # Устраняем выбросы:\n",
" # Заменяем значения ниже нижней границы на нижнюю границу\n",
" # А значения выше верхней границы – на верхнюю\n",
" dataframe[column] = dataframe[column].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)\n",
" \n",
" return dataframe\n",
"\n",
"# Cтолбцы, которые нужно исправить\n",
"columns_to_fix = [\n",
" 'BMI',\n",
" 'SleepTime'\n",
"]\n",
"\n",
"# Устраняем выборсы\n",
"df = remove_outliers(df, columns_to_fix)\n",
"\n",
"# Проверка наличия выбросов в колонках\n",
"print('Проверка наличия выбросов в колонках после их устранения:')\n",
"check_outliers(df, columns_to_fix)\n",
"visualize_outliers(df, columns_to_fix)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Разбиение датасета на выборки\n",
"\n",
"Разделим выборку данных на 3 группы:\n",
"1. *Обучающая* выборка (70%).\n",
"2. *Контрольная* выборка (15%).\n",
"3. *Тестовая* выборка (15%)."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Проверка сбалансированности выборок:\n",
"Обучающая выборка: (191877, 18)\n",
"Распределение выборки данных по классам в колонке \"HeartDisease\":\n",
" HeartDisease\n",
"No 175453\n",
"Yes 16424\n",
"Name: count, dtype: int64\n",
"Процент объектов класса \"No\": 91.44%\n",
"Процент объектов класса \"Yes\": 8.56%\n",
"\n",
"Контрольная выборка: (63959, 18)\n",
"Распределение выборки данных по классам в колонке \"HeartDisease\":\n",
" HeartDisease\n",
"No 58484\n",
"Yes 5475\n",
"Name: count, dtype: int64\n",
"Процент объектов класса \"No\": 91.44%\n",
"Процент объектов класса \"Yes\": 8.56%\n",
"\n",
"Тестовая выборка: (63959, 18)\n",
"Распределение выборки данных по классам в колонке \"HeartDisease\":\n",
" HeartDisease\n",
"No 58485\n",
"Yes 5474\n",
"Name: count, dtype: int64\n",
"Процент объектов класса \"No\": 91.44%\n",
"Процент объектов класса \"Yes\": 8.56%\n",
"\n",
"Проверка необходимости аугментации выборок:\n",
"Для обучающей выборки аугментация данных требуется\n",
"Для контрольной выборки аугментация данных требуется\n",
"Для тестовой выборки аугментация данных требуется\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 600x800 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"def split_stratified_into_train_val_test(\n",
" df_input,\n",
" stratify_colname,\n",
" frac_train,\n",
" frac_val,\n",
" frac_test,\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\n",
"\n",
"# Оценка сбалансированности\n",
"def check_balance(dataframe, dataframe_name, column):\n",
" counts = dataframe[column].value_counts()\n",
" print(dataframe_name + \": \", dataframe.shape)\n",
" print(f\"Распределение выборки данных по классам в колонке \\\"{column}\\\":\\n\", counts)\n",
" total_count = len(dataframe)\n",
" for value in counts.index:\n",
" percentage: float = counts[value] / total_count * 100\n",
" print(f\"Процент объектов класса \\\"{value}\\\": {percentage:.2f}%\")\n",
" print()\n",
" \n",
"# Определение необходимости аугментации данных\n",
"def need_augmentation(dataframe,\n",
" column, \n",
" first_value, second_value):\n",
" counts = dataframe[column].value_counts()\n",
" ratio: float = counts[first_value] / counts[second_value]\n",
" return ratio > 1.5 or ratio < 0.67\n",
" \n",
" # Визуализация сбалансированности классов\n",
"def visualize_balance(dataframe_train,\n",
" dataframe_val,\n",
" dataframe_test, \n",
" column: str):\n",
" fig, axes = plt.subplots(3, 1, figsize=(6, 8))\n",
"\n",
" # Обучающая выборка\n",
" counts_train = dataframe_train[column].value_counts()\n",
" axes[0].pie(counts_train, labels=counts_train.index, autopct='%1.1f%%', startangle=90)\n",
" axes[0].set_title(f\"Распределение классов \\\"{column}\\\" в обучающей выборке\")\n",
"\n",
" # Контрольная выборка\n",
" counts_val = dataframe_val[column].value_counts()\n",
" axes[1].pie(counts_val, labels=counts_val.index, autopct='%1.1f%%', startangle=90)\n",
" axes[1].set_title(f\"Распределение классов \\\"{column}\\\" в контрольной выборке\")\n",
"\n",
" # Тестовая выборка\n",
" counts_test = dataframe_test[column].value_counts()\n",
" axes[2].pie(counts_test, labels=counts_test.index, autopct='%1.1f%%', startangle=90)\n",
" axes[2].set_title(f\"Распределение классов \\\"{column}\\\" в тренировочной выборке\")\n",
"\n",
" # Отображение графиков\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
"\n",
"df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
" df, \n",
" stratify_colname=\"HeartDisease\", \n",
" frac_train=0.60, \n",
" frac_val=0.20, \n",
" frac_test=0.20\n",
")\n",
"\n",
"# Проверка сбалансированности выборок\n",
"print('Проверка сбалансированности выборок:')\n",
"check_balance(df_train, 'Обучающая выборка', 'HeartDisease')\n",
"check_balance(df_val, 'Контрольная выборка', 'HeartDisease')\n",
"check_balance(df_test, 'Тестовая выборка', 'HeartDisease')\n",
"\n",
"# Проверка необходимости аугментации выборок\n",
"print('Проверка необходимости аугментации выборок:')\n",
"print(f\"Для обучающей выборки аугментация данных {'не ' if not need_augmentation(df_train, 'HeartDisease', 'No', 'Yes') else ''}требуется\")\n",
"print(f\"Для контрольной выборки аугментация данных {'не ' if not need_augmentation(df_val, 'HeartDisease', 'No', 'Yes') else ''}требуется\")\n",
"print(f\"Для тестовой выборки аугментация данных {'не ' if not need_augmentation(df_test, 'HeartDisease', 'No', 'Yes') else ''}требуется\")\n",
" \n",
"# Визуализация сбалансированности классов\n",
"visualize_balance(df_train, df_val, df_test, 'HeartDisease')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Выборки оказались **недостаточно сбалансированными**. Используем методы приращения данных *с избытком* (**oversampling**) – копирование наблюдений или генерация новых наблюдений на основе существующих с помощью алгоритмов SMOTE и ADASYN (нахождение k-ближайших соседей):"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Проверка сбалансированности выборок:\n",
"Обучающая выборка: (350906, 51)\n",
"Распределение выборки данных по классам в колонке \"HeartDisease\":\n",
" HeartDisease\n",
"No 175453\n",
"Yes 175453\n",
"Name: count, dtype: int64\n",
"Процент объектов класса \"No\": 50.00%\n",
"Процент объектов класса \"Yes\": 50.00%\n",
"\n",
"Контрольная выборка: (116968, 51)\n",
"Распределение выборки данных по классам в колонке \"HeartDisease\":\n",
" HeartDisease\n",
"No 58484\n",
"Yes 58484\n",
"Name: count, dtype: int64\n",
"Процент объектов класса \"No\": 50.00%\n",
"Процент объектов класса \"Yes\": 50.00%\n",
"\n",
"Тестовая выборка: (116970, 51)\n",
"Распределение выборки данных по классам в колонке \"HeartDisease\":\n",
" HeartDisease\n",
"No 58485\n",
"Yes 58485\n",
"Name: count, dtype: int64\n",
"Процент объектов класса \"No\": 50.00%\n",
"Процент объектов класса \"Yes\": 50.00%\n",
"\n",
"Проверка необходимости аугментации выборок:\n",
"Для обучающей выборки аугментация данных не требуется\n",
"Для контрольной выборки аугментация данных не требуется\n",
"Для тестовой выборки аугментация данных не требуется\n"
]
},
{
"data": {
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",
"text/plain": [
"<Figure size 600x800 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from imblearn.over_sampling import SMOTE\n",
"\n",
"# Метод приращения с избытком (oversampling)\n",
"def oversample(df, column):\n",
" X = pd.get_dummies(df.drop(column, axis=1))\n",
" y = df[column]\n",
" \n",
" smote = SMOTE()\n",
" X_resampled, y_resampled = smote.fit_resample(X, y)\n",
" \n",
" df_resampled = pd.concat([X_resampled, y_resampled], axis=1)\n",
" return df_resampled\n",
"\n",
"\n",
"# Приращение данных (oversampling)\n",
"df_train_oversampled = oversample(df_train, 'HeartDisease')\n",
"df_val_oversampled = oversample(df_val, 'HeartDisease')\n",
"df_test_oversampled = oversample(df_test, 'HeartDisease')\n",
"\n",
"# Проверка сбалансированности выборок\n",
"print('Проверка сбалансированности выборок:')\n",
"check_balance(df_train_oversampled, 'Обучающая выборка', 'HeartDisease')\n",
"check_balance(df_val_oversampled, 'Контрольная выборка', 'HeartDisease')\n",
"check_balance(df_test_oversampled, 'Тестовая выборка', 'HeartDisease')\n",
"\n",
"# Проверка необходимости аугментации выборок\n",
"print('Проверка необходимости аугментации выборок:')\n",
"print(f\"Для обучающей выборки аугментация данных {'не ' if not need_augmentation(df_train_oversampled, 'HeartDisease', 'No', 'Yes') else ''}требуется\")\n",
"print(f\"Для контрольной выборки аугментация данных {'не ' if not need_augmentation(df_val_oversampled, 'HeartDisease', 'No', 'Yes') else ''}требуется\")\n",
"print(f\"Для тестовой выборки аугментация данных {'не ' if not need_augmentation(df_test_oversampled, 'HeartDisease', 'No', 'Yes') else ''}требуется\")\n",
" \n",
"# Визуализация сбалансированности классов\n",
"visualize_balance(df_train_oversampled, df_val_oversampled, df_test_oversampled, 'HeartDisease')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Конструирование признаков\n",
"\n",
"**Конструирование признаков** (*feature engineering*) – процесс использования знаний об особенностях решаемой задачи и предметной области для определения признаков, которые будут использованы для обучения статистической модели.\n",
"\n",
"Методы конструирования признаков:\n",
"1. Для категориальных данных:\n",
" - **Унитарное кодирование категориальных признаков** (one-hot encoding) – метод, который применяется для преобразования категориальных переменных в числовой формат. Каждая характеристика представляется в виде бинарного вектора, где для каждой категории выделяется отдельный признак (столбец) со значением 1 (True), если объект принадлежит этой категории, и 0 (False) в противном случае.\n",
"2. Для числовых данных:\n",
" - **Дискретизация** – процесс преобразования непрерывных числовых значений в категориальные группы или интервалы (дискретные значения).\n",
" - **Ручной синтез** – процесс создания новых признаков на основе существующих данных. Это может включать в себя комбинирование нескольких признаков, использование математических операций (например, сложение, вычитание), а также создание полиномиальных или логарифмических признаков.\n",
" - **Масштабирование признаков на основе нормировки и стандартизации** – метод, который позволяет привести все числовые признаки к одинаковым или очень похожим диапазонам значений либо распределениям.\n",
" - **С применением фреймворка FeatureTools** – библиотека для автоматизированного создания признаков (features) из структурированных данных. Подходит для задач машинного обучения, когда нужно быстро извлекать полезные признаки из больших объемов данных."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Унитарное кодирование\n",
"\n",
"Преобразование уже было выполнено на этапе приращения с избытком (метод `pd.get_dummies(...)`), так как метод `fit_resample` требовал для работы признаки типа число с плавающей точкой. Были преобразованы категориальные признаки `Smoking`, `AlcoholDrinking`, `Stroke`, `DiffWalking` и т.д. в бинарные признаки:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>BMI</th>\n",
" <th>PhysicalHealth</th>\n",
" <th>MentalHealth</th>\n",
" <th>SleepTime</th>\n",
" <th>Smoking_No</th>\n",
" <th>Smoking_Yes</th>\n",
" <th>AlcoholDrinking_No</th>\n",
" <th>AlcoholDrinking_Yes</th>\n",
" <th>Stroke_No</th>\n",
" <th>Stroke_Yes</th>\n",
" <th>...</th>\n",
" <th>GenHealth_Good</th>\n",
" <th>GenHealth_Poor</th>\n",
" <th>GenHealth_Very good</th>\n",
" <th>Asthma_No</th>\n",
" <th>Asthma_Yes</th>\n",
" <th>KidneyDisease_No</th>\n",
" <th>KidneyDisease_Yes</th>\n",
" <th>SkinCancer_No</th>\n",
" <th>SkinCancer_Yes</th>\n",
" <th>HeartDisease</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>24.28</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>8.0</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>34.44</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>8.0</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>25.86</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>8.0</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>19.47</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>8.0</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>34.70</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>8.0</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>29.05</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>32.45</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>7.0</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>26.25</td>\n",
" <td>0.0</td>\n",
" <td>30.0</td>\n",
" <td>6.0</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>30.67</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>7.0</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>34.96</td>\n",
" <td>14.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>No</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows × 51 columns</p>\n",
"</div>"
],
"text/plain": [
" BMI PhysicalHealth MentalHealth SleepTime Smoking_No Smoking_Yes \\\n",
"0 24.28 2.0 3.0 8.0 True False \n",
"1 34.44 0.0 0.0 8.0 False True \n",
"2 25.86 0.0 5.0 8.0 True False \n",
"3 19.47 0.0 2.0 8.0 False True \n",
"4 34.70 0.0 0.0 8.0 False True \n",
"5 29.05 0.0 0.0 6.0 True False \n",
"6 32.45 0.0 5.0 7.0 True False \n",
"7 26.25 0.0 30.0 6.0 False True \n",
"8 30.67 2.0 3.0 7.0 True False \n",
"9 34.96 14.0 0.0 6.0 True False \n",
"\n",
" AlcoholDrinking_No AlcoholDrinking_Yes Stroke_No Stroke_Yes ... \\\n",
"0 True False True False ... \n",
"1 True False True False ... \n",
"2 True False True False ... \n",
"3 True False True False ... \n",
"4 True False True False ... \n",
"5 False True True False ... \n",
"6 True False True False ... \n",
"7 True False True False ... \n",
"8 True False True False ... \n",
"9 True False True False ... \n",
"\n",
" GenHealth_Good GenHealth_Poor GenHealth_Very good Asthma_No Asthma_Yes \\\n",
"0 False False True False True \n",
"1 True False False True False \n",
"2 False False True True False \n",
"3 False False True True False \n",
"4 False False True True False \n",
"5 True False False True False \n",
"6 True False False True False \n",
"7 False False True True False \n",
"8 True False False True False \n",
"9 True False False True False \n",
"\n",
" KidneyDisease_No KidneyDisease_Yes SkinCancer_No SkinCancer_Yes \\\n",
"0 True False True False \n",
"1 True False True False \n",
"2 True False True False \n",
"3 True False True False \n",
"4 True False True False \n",
"5 True False True False \n",
"6 True False True False \n",
"7 True False True False \n",
"8 True False True False \n",
"9 True False True False \n",
"\n",
" HeartDisease \n",
"0 No \n",
"1 No \n",
"2 No \n",
"3 No \n",
"4 No \n",
"5 No \n",
"6 No \n",
"7 No \n",
"8 No \n",
"9 No \n",
"\n",
"[10 rows x 51 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"categorical_features = [\n",
" 'Smoking', 'AlcoholDrinking', 'Stroke', 'DiffWalking', 'Sex', 'AgeCategory', 'Race',\n",
" 'Diabetic', 'PhysicalActivity', 'GenHealth', 'Asthma', 'KidneyDisease', 'SkinCancer'\n",
"]\n",
"\n",
"df_train_oversampled.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Дискретизация числовых признаков\n",
"\n",
"Распределим значения признака `BMI` по интервалам, преобразуя его из числового представления в категориальное. Будем использовать метод **Равномерная группировка**:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>BMI</th>\n",
" <th>BMI_Category</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>24.280</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>34.440</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>25.860</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>19.470</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>34.700</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>29.050</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>32.450</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>26.250</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>30.670</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>34.960</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>27.810</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>20.360</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>27.400</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>42.505</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>21.520</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>36.260</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>23.490</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>28.190</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>28.290</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20.800</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" BMI BMI_Category\n",
"0 24.280 1\n",
"1 34.440 3\n",
"2 25.860 2\n",
"3 19.470 1\n",
"4 34.700 3\n",
"5 29.050 2\n",
"6 32.450 3\n",
"7 26.250 2\n",
"8 30.670 2\n",
"9 34.960 3\n",
"10 27.810 2\n",
"11 20.360 1\n",
"12 27.400 2\n",
"13 42.505 4\n",
"14 21.520 1\n",
"15 36.260 3\n",
"16 23.490 1\n",
"17 28.190 2\n",
"18 28.290 2\n",
"19 20.800 1"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Функция для дискретизации числовых признаков\n",
"def discretize_features(df, features, bins=5, labels=False):\n",
" for feature in features:\n",
" df[f'{feature}_Category'] = pd.cut(df[feature], bins=bins, labels=labels)\n",
" return df\n",
"\n",
"# Определение числовых признаков для дискретизации\n",
"numerical_features = ['BMI']\n",
"\n",
"# Применение дискретизации к обучающей, контрольной и тестовой выборкам\n",
"df_train_oversampled = discretize_features(df_train_oversampled, numerical_features)\n",
"df_val_oversampled = discretize_features(df_val_oversampled, numerical_features)\n",
"df_test_oversampled = discretize_features(df_test_oversampled, numerical_features)\n",
"\n",
"df_train_oversampled[['BMI', 'BMI_Category']].head(20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Ручной синтез признаков\n",
"\n",
"Будем синтезировать новый признак `HealthScore`, являющийся числовым показателем здоровья на основе таких признаков, как `PhysicalHealth`, `MentalHealth`, `SleepTime`:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>BMI</th>\n",
" <th>PhysicalHealth</th>\n",
" <th>MentalHealth</th>\n",
" <th>SleepTime</th>\n",
" <th>Smoking_No</th>\n",
" <th>Smoking_Yes</th>\n",
" <th>AlcoholDrinking_No</th>\n",
" <th>AlcoholDrinking_Yes</th>\n",
" <th>Stroke_No</th>\n",
" <th>Stroke_Yes</th>\n",
" <th>...</th>\n",
" <th>GenHealth_Very good</th>\n",
" <th>Asthma_No</th>\n",
" <th>Asthma_Yes</th>\n",
" <th>KidneyDisease_No</th>\n",
" <th>KidneyDisease_Yes</th>\n",
" <th>SkinCancer_No</th>\n",
" <th>SkinCancer_Yes</th>\n",
" <th>HeartDisease</th>\n",
" <th>BMI_Category</th>\n",
" <th>HealthScore</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>24.28</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>8.0</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>No</td>\n",
" <td>1</td>\n",
" <td>21.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>34.44</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>8.0</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>No</td>\n",
" <td>3</td>\n",
" <td>23.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>25.86</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>8.0</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>No</td>\n",
" <td>2</td>\n",
" <td>21.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>19.47</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>8.0</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>No</td>\n",
" <td>1</td>\n",
" <td>22.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>34.70</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>8.0</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>No</td>\n",
" <td>3</td>\n",
" <td>23.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>29.05</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>No</td>\n",
" <td>2</td>\n",
" <td>22.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>32.45</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>7.0</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>No</td>\n",
" <td>3</td>\n",
" <td>21.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>26.25</td>\n",
" <td>0.0</td>\n",
" <td>30.0</td>\n",
" <td>6.0</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>No</td>\n",
" <td>2</td>\n",
" <td>13.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>30.67</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>7.0</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>No</td>\n",
" <td>2</td>\n",
" <td>21.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>34.96</td>\n",
" <td>14.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>No</td>\n",
" <td>3</td>\n",
" <td>17.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows × 53 columns</p>\n",
"</div>"
],
"text/plain": [
" BMI PhysicalHealth MentalHealth SleepTime Smoking_No Smoking_Yes \\\n",
"0 24.28 2.0 3.0 8.0 True False \n",
"1 34.44 0.0 0.0 8.0 False True \n",
"2 25.86 0.0 5.0 8.0 True False \n",
"3 19.47 0.0 2.0 8.0 False True \n",
"4 34.70 0.0 0.0 8.0 False True \n",
"5 29.05 0.0 0.0 6.0 True False \n",
"6 32.45 0.0 5.0 7.0 True False \n",
"7 26.25 0.0 30.0 6.0 False True \n",
"8 30.67 2.0 3.0 7.0 True False \n",
"9 34.96 14.0 0.0 6.0 True False \n",
"\n",
" AlcoholDrinking_No AlcoholDrinking_Yes Stroke_No Stroke_Yes ... \\\n",
"0 True False True False ... \n",
"1 True False True False ... \n",
"2 True False True False ... \n",
"3 True False True False ... \n",
"4 True False True False ... \n",
"5 False True True False ... \n",
"6 True False True False ... \n",
"7 True False True False ... \n",
"8 True False True False ... \n",
"9 True False True False ... \n",
"\n",
" GenHealth_Very good Asthma_No Asthma_Yes KidneyDisease_No \\\n",
"0 True False True True \n",
"1 False True False True \n",
"2 True True False True \n",
"3 True True False True \n",
"4 True True False True \n",
"5 False True False True \n",
"6 False True False True \n",
"7 True True False True \n",
"8 False True False True \n",
"9 False True False True \n",
"\n",
" KidneyDisease_Yes SkinCancer_No SkinCancer_Yes HeartDisease \\\n",
"0 False True False No \n",
"1 False True False No \n",
"2 False True False No \n",
"3 False True False No \n",
"4 False True False No \n",
"5 False True False No \n",
"6 False True False No \n",
"7 False True False No \n",
"8 False True False No \n",
"9 False True False No \n",
"\n",
" BMI_Category HealthScore \n",
"0 1 21.7 \n",
"1 3 23.4 \n",
"2 2 21.9 \n",
"3 1 22.8 \n",
"4 3 23.4 \n",
"5 2 22.8 \n",
"6 3 21.6 \n",
"7 2 13.8 \n",
"8 2 21.4 \n",
"9 3 17.2 \n",
"\n",
"[10 rows x 53 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Рассчитаем новый признак HealthScore\n",
"# Используем взвешенную сумму физического, ментального здоровья и количества сна\n",
"df_train_oversampled[\"HealthScore\"] = (\n",
" (30.0 - df_train_oversampled[\"PhysicalHealth\"]) * 0.4 + # Чем меньше проблем с физическим здоровьем, тем лучше\n",
" (30.0 - df_train_oversampled[\"MentalHealth\"]) * 0.3 + # Чем меньше проблем с ментальным здоровьем, тем лучше\n",
" df_train_oversampled[\"SleepTime\"] * 0.3 # Оптимальное время сна\n",
")\n",
"\n",
"df_train_oversampled.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Масштабирование признаков на основе нормировки и стандартизации\n",
"\n",
"Методы масштабирования признаков:\n",
"- *Нормировка* – обычно применяется для равномерного распределения;\n",
"- *Стандартизация* – обычно применяется для нормального распределения.\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
" <th>BMI</th>\n",
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" <th>MentalHealth</th>\n",
" <th>SleepTime</th>\n",
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" </tr>\n",
" <tr>\n",
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" <td>0.436908</td>\n",
" <td>0.000000</td>\n",
" <td>0.166667</td>\n",
" <td>0.625</td>\n",
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" <th>3</th>\n",
" <td>0.220737</td>\n",
" <td>0.000000</td>\n",
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" <td>0.544824</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.659844</td>\n",
" <td>0.000000</td>\n",
" <td>0.166667</td>\n",
" <td>0.500</td>\n",
" </tr>\n",
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" <th>7</th>\n",
" <td>0.450101</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.375</td>\n",
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" <tr>\n",
" <th>8</th>\n",
" <td>0.599628</td>\n",
" <td>0.066667</td>\n",
" <td>0.100000</td>\n",
" <td>0.500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.744756</td>\n",
" <td>0.466667</td>\n",
" <td>0.000000</td>\n",
" <td>0.375</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" BMI PhysicalHealth MentalHealth SleepTime\n",
"0 0.383457 0.066667 0.100000 0.625\n",
"1 0.727165 0.000000 0.000000 0.625\n",
"2 0.436908 0.000000 0.166667 0.625\n",
"3 0.220737 0.000000 0.066667 0.625\n",
"4 0.735961 0.000000 0.000000 0.625\n",
"5 0.544824 0.000000 0.000000 0.375\n",
"6 0.659844 0.000000 0.166667 0.500\n",
"7 0.450101 0.000000 1.000000 0.375\n",
"8 0.599628 0.066667 0.100000 0.500\n",
"9 0.744756 0.466667 0.000000 0.375"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.preprocessing import MinMaxScaler\n",
"\n",
"scaler = MinMaxScaler()\n",
"\n",
"# Применяем масштабирование к выбранным признакам\n",
"df_train_oversampled_normalized = df_train_oversampled\n",
"df_train_oversampled_normalized[numeric_columns] = scaler.fit_transform(df_train_oversampled_normalized[numeric_columns])\n",
"\n",
"df_train_oversampled_normalized[numeric_columns].head(10)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['BMI', 'PhysicalHealth', 'MentalHealth', 'SleepTime', 'Smoking_No',\n",
" 'Smoking_Yes', 'AlcoholDrinking_No', 'AlcoholDrinking_Yes', 'Stroke_No',\n",
" 'Stroke_Yes', 'DiffWalking_No', 'DiffWalking_Yes', 'Sex_Female',\n",
" 'Sex_Male', 'AgeCategory_18-24', 'AgeCategory_25-29',\n",
" 'AgeCategory_30-34', 'AgeCategory_35-39', 'AgeCategory_40-44',\n",
" 'AgeCategory_45-49', 'AgeCategory_50-54', 'AgeCategory_55-59',\n",
" 'AgeCategory_60-64', 'AgeCategory_65-69', 'AgeCategory_70-74',\n",
" 'AgeCategory_75-79', 'AgeCategory_80 or older',\n",
" 'Race_American Indian/Alaskan Native', 'Race_Asian', 'Race_Black',\n",
" 'Race_Hispanic', 'Race_Other', 'Race_White', 'Diabetic_No',\n",
" 'Diabetic_No, borderline diabetes', 'Diabetic_Yes',\n",
" 'Diabetic_Yes (during pregnancy)', 'PhysicalActivity_No',\n",
" 'PhysicalActivity_Yes', 'GenHealth_Excellent', 'GenHealth_Fair',\n",
" 'GenHealth_Good', 'GenHealth_Poor', 'GenHealth_Very good', 'Asthma_No',\n",
" 'Asthma_Yes', 'KidneyDisease_No', 'KidneyDisease_Yes', 'SkinCancer_No',\n",
" 'SkinCancer_Yes', 'HeartDisease', 'BMI_Category', 'HealthScore'],\n",
" dtype='object')"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train_oversampled_normalized.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Конструирование с применением FeatureTools"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/oleg/aim_labs/lab_3/aimenv/lib/python3.12/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",
"/home/oleg/aim_labs/lab_3/aimenv/lib/python3.12/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"
]
},
{
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" <th>MentalHealth</th>\n",
" <th>SleepTime</th>\n",
" <th>Smoking_No</th>\n",
" <th>Smoking_Yes</th>\n",
" <th>AlcoholDrinking_No</th>\n",
" <th>AlcoholDrinking_Yes</th>\n",
" <th>Stroke_No</th>\n",
" <th>Stroke_Yes</th>\n",
" <th>...</th>\n",
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" <th>HealthScore * MentalHealth</th>\n",
" <th>HealthScore * PhysicalHealth</th>\n",
" <th>HealthScore * SleepTime</th>\n",
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" <th>MentalHealth * SleepTime</th>\n",
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" <th>Id</th>\n",
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" <td>1.875</td>\n",
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" <th>3</th>\n",
" <td>0.436908</td>\n",
" <td>0.000000</td>\n",
" <td>0.166667</td>\n",
" <td>0.625</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>43.8</td>\n",
" <td>0.333333</td>\n",
" <td>0.000000</td>\n",
" <td>1.250</td>\n",
" <td>3.65</td>\n",
" <td>0.000000</td>\n",
" <td>13.6875</td>\n",
" <td>0.000000</td>\n",
" <td>0.104167</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.220737</td>\n",
" <td>0.000000</td>\n",
" <td>0.066667</td>\n",
" <td>0.625</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>22.8</td>\n",
" <td>0.066667</td>\n",
" <td>0.000000</td>\n",
" <td>0.625</td>\n",
" <td>1.52</td>\n",
" <td>0.000000</td>\n",
" <td>14.2500</td>\n",
" <td>0.000000</td>\n",
" <td>0.041667</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.735961</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.625</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>70.2</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.875</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>14.6250</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.544824</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.375</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>45.6</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.750</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>8.5500</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.659844</td>\n",
" <td>0.000000</td>\n",
" <td>0.166667</td>\n",
" <td>0.500</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>64.8</td>\n",
" <td>0.500000</td>\n",
" <td>0.000000</td>\n",
" <td>1.500</td>\n",
" <td>3.60</td>\n",
" <td>0.000000</td>\n",
" <td>10.8000</td>\n",
" <td>0.000000</td>\n",
" <td>0.083333</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.450101</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.375</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>27.6</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.750</td>\n",
" <td>13.80</td>\n",
" <td>0.000000</td>\n",
" <td>5.1750</td>\n",
" <td>0.000000</td>\n",
" <td>0.375000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.599628</td>\n",
" <td>0.066667</td>\n",
" <td>0.100000</td>\n",
" <td>0.500</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>42.8</td>\n",
" <td>0.200000</td>\n",
" <td>0.133333</td>\n",
" <td>1.000</td>\n",
" <td>2.14</td>\n",
" <td>1.426667</td>\n",
" <td>10.7000</td>\n",
" <td>0.006667</td>\n",
" <td>0.050000</td>\n",
" <td>0.033333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0.744756</td>\n",
" <td>0.466667</td>\n",
" <td>0.000000</td>\n",
" <td>0.375</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>51.6</td>\n",
" <td>0.000000</td>\n",
" <td>1.400000</td>\n",
" <td>1.125</td>\n",
" <td>0.00</td>\n",
" <td>8.026667</td>\n",
" <td>6.4500</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.175000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows × 68 columns</p>\n",
"</div>"
],
"text/plain": [
" BMI PhysicalHealth MentalHealth SleepTime Smoking_No \\\n",
"Id \n",
"1 0.383457 0.066667 0.100000 0.625 True \n",
"2 0.727165 0.000000 0.000000 0.625 False \n",
"3 0.436908 0.000000 0.166667 0.625 True \n",
"4 0.220737 0.000000 0.066667 0.625 False \n",
"5 0.735961 0.000000 0.000000 0.625 False \n",
"6 0.544824 0.000000 0.000000 0.375 True \n",
"7 0.659844 0.000000 0.166667 0.500 True \n",
"8 0.450101 0.000000 1.000000 0.375 False \n",
"9 0.599628 0.066667 0.100000 0.500 True \n",
"10 0.744756 0.466667 0.000000 0.375 True \n",
"\n",
" Smoking_Yes AlcoholDrinking_No AlcoholDrinking_Yes Stroke_No \\\n",
"Id \n",
"1 False True False True \n",
"2 True True False True \n",
"3 False True False True \n",
"4 True True False True \n",
"5 True True False True \n",
"6 False False True True \n",
"7 False True False True \n",
"8 True True False True \n",
"9 False True False True \n",
"10 False True False True \n",
"\n",
" Stroke_Yes ... BMI_Category * HealthScore BMI_Category * MentalHealth \\\n",
"Id ... \n",
"1 False ... 21.7 0.100000 \n",
"2 False ... 70.2 0.000000 \n",
"3 False ... 43.8 0.333333 \n",
"4 False ... 22.8 0.066667 \n",
"5 False ... 70.2 0.000000 \n",
"6 False ... 45.6 0.000000 \n",
"7 False ... 64.8 0.500000 \n",
"8 False ... 27.6 2.000000 \n",
"9 False ... 42.8 0.200000 \n",
"10 False ... 51.6 0.000000 \n",
"\n",
" BMI_Category * PhysicalHealth BMI_Category * SleepTime \\\n",
"Id \n",
"1 0.066667 0.625 \n",
"2 0.000000 1.875 \n",
"3 0.000000 1.250 \n",
"4 0.000000 0.625 \n",
"5 0.000000 1.875 \n",
"6 0.000000 0.750 \n",
"7 0.000000 1.500 \n",
"8 0.000000 0.750 \n",
"9 0.133333 1.000 \n",
"10 1.400000 1.125 \n",
"\n",
" HealthScore * MentalHealth HealthScore * PhysicalHealth \\\n",
"Id \n",
"1 2.17 1.446667 \n",
"2 0.00 0.000000 \n",
"3 3.65 0.000000 \n",
"4 1.52 0.000000 \n",
"5 0.00 0.000000 \n",
"6 0.00 0.000000 \n",
"7 3.60 0.000000 \n",
"8 13.80 0.000000 \n",
"9 2.14 1.426667 \n",
"10 0.00 8.026667 \n",
"\n",
" HealthScore * SleepTime MentalHealth * PhysicalHealth \\\n",
"Id \n",
"1 13.5625 0.006667 \n",
"2 14.6250 0.000000 \n",
"3 13.6875 0.000000 \n",
"4 14.2500 0.000000 \n",
"5 14.6250 0.000000 \n",
"6 8.5500 0.000000 \n",
"7 10.8000 0.000000 \n",
"8 5.1750 0.000000 \n",
"9 10.7000 0.006667 \n",
"10 6.4500 0.000000 \n",
"\n",
" MentalHealth * SleepTime PhysicalHealth * SleepTime \n",
"Id \n",
"1 0.062500 0.041667 \n",
"2 0.000000 0.000000 \n",
"3 0.104167 0.000000 \n",
"4 0.041667 0.000000 \n",
"5 0.000000 0.000000 \n",
"6 0.000000 0.000000 \n",
"7 0.083333 0.000000 \n",
"8 0.375000 0.000000 \n",
"9 0.050000 0.033333 \n",
"10 0.000000 0.175000 \n",
"\n",
"[10 rows x 68 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import featuretools as ft\n",
"\n",
"# Создание EntitySet\n",
"\n",
"df_testing = df_train_oversampled_normalized\n",
"# Создание уникального идентификатора для каждой строки\n",
"df_testing['Id'] = range(1, len(df_testing) + 1)\n",
"\n",
"es = ft.EntitySet(id='my-test-data')\n",
"es = es.add_dataframe(dataframe=df_testing, dataframe_name='my-name', index='Id')\n",
"\n",
"# Указываем, какие трансформации нужно применить\n",
"trans_primitives = ['multiply_numeric']\n",
"\n",
"# Генерация признаков с помощью глубокого синтеза признаков\n",
"feature_matrix, feature_defs = ft.dfs(\n",
" entityset=es, \n",
" target_dataframe_name='my-name', \n",
" max_depth=1,\n",
" trans_primitives=trans_primitives\n",
")\n",
"\n",
"# Выводим первые 10 строк сгенерированного набора признаков\n",
"feature_matrix.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Оценка качества каждого набора признаков\n",
"\n",
"**Предсказательная способность**: Способность набора признаков успешно прогнозировать целевую переменную. Это определяется через метрики, такие как RMSE, MAE, R², которые показывают, насколько хорошо модель использует признаки для достижения точных результатов. Для определения качества необходимо провести обучение модели на обучающей выборке и сравнить с оценкой прогнозирования на контрольной и тестовой выборках.\n",
"\n",
"**Скорость вычисления**: Время, необходимое для обработки данных и выполнения алгоритмов машинного обучения. Признаки должны быть вычисляемыми за разумный срок, чтобы обеспечить эффективность модели, особенно при работе с большими наборами данных. Для оценки качества необходимо провести измерение времени выполнения генерации признаков и обучения модели.\n",
"\n",
"**Надежность**: Устойчивость и воспроизводимость результатов при изменении входных данных. Надежные признаки должны давать схожие результаты независимо от случайных факторов или незначительных изменений в данных. Методы оценки: Кросс-валидация, анализ чувствительности модели к изменениям в данных.\n",
"\n",
"**Корреляция**: Степень взаимосвязи между признаками и целевой переменной, а также между самими признаками. Высокая корреляция с целевой переменной указывает на потенциальную предсказательную силу, тогда как высокая взаимосвязь между самими признаками может приводить к многоколлинеарности и снижению эффективности модели. Методы оценки: Анализ корреляционной матрицы признаков, удаление мультиколлинеарных признаков.\n",
"\n",
"**Цельность**: Не является производным от других признаков. Методы оценки: Проверка логической связи между признаками и целевой переменной, интерпретация результатов модели."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Размер обучающей выборки: 156699\n",
"Размер контрольной выборки: 67157\n",
"Размер тестовой выборки: 95939\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/oleg/aim_labs/lab_3/aimenv/lib/python3.12/site-packages/featuretools/entityset/entityset.py:1733: UserWarning: index id not found in dataframe, creating new integer column\n",
" warnings.warn(\n",
"/home/oleg/aim_labs/lab_3/aimenv/lib/python3.12/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",
"/home/oleg/aim_labs/lab_3/aimenv/lib/python3.12/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",
"/home/oleg/aim_labs/lab_3/aimenv/lib/python3.12/site-packages/featuretools/synthesis/deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
" warnings.warn(\n",
"/home/oleg/aim_labs/lab_3/aimenv/lib/python3.12/site-packages/featuretools/computational_backends/feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
" df = pd.concat([df, default_df], sort=True)\n",
"/home/oleg/aim_labs/lab_3/aimenv/lib/python3.12/site-packages/featuretools/computational_backends/feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
" df = pd.concat([df, default_df], sort=True)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Feature Importance:\n",
" feature importance\n",
"0 HeartDisease 0.851120\n",
"1 BMI 0.014203\n",
"9 Stroke_No 0.012600\n",
"2 PhysicalHealth 0.008628\n",
"12 DiffWalking_Yes 0.008111\n",
"11 DiffWalking_No 0.007721\n",
"36 Diabetic_Yes 0.007583\n",
"10 Stroke_Yes 0.007551\n",
"4 SleepTime 0.007525\n",
"43 GenHealth_Poor 0.006605\n",
"27 AgeCategory_80 or older 0.006269\n",
"34 Diabetic_No 0.005300\n",
"3 MentalHealth 0.005102\n",
"41 GenHealth_Fair 0.004277\n",
"48 KidneyDisease_Yes 0.003435\n",
"47 KidneyDisease_No 0.003086\n",
"13 Sex_Female 0.002607\n",
"26 AgeCategory_75-79 0.002567\n",
"25 AgeCategory_70-74 0.002462\n",
"14 Sex_Male 0.002457\n",
"6 Smoking_Yes 0.002127\n",
"5 Smoking_No 0.001934\n",
"42 GenHealth_Good 0.001787\n",
"44 GenHealth_Very good 0.001734\n",
"33 Race_White 0.001731\n",
"50 SkinCancer_Yes 0.001687\n",
"38 PhysicalActivity_No 0.001658\n",
"39 PhysicalActivity_Yes 0.001585\n",
"49 SkinCancer_No 0.001513\n",
"40 GenHealth_Excellent 0.001451\n",
"24 AgeCategory_65-69 0.001318\n",
"46 Asthma_Yes 0.001315\n",
"45 Asthma_No 0.001256\n",
"23 AgeCategory_60-64 0.001091\n",
"30 Race_Black 0.000885\n",
"22 AgeCategory_55-59 0.000853\n",
"31 Race_Hispanic 0.000825\n",
"21 AgeCategory_50-54 0.000715\n",
"32 Race_Other 0.000699\n",
"7 AlcoholDrinking_No 0.000560\n",
"8 AlcoholDrinking_Yes 0.000550\n",
"20 AgeCategory_45-49 0.000520\n",
"28 Race_American Indian/Alaskan Native 0.000503\n",
"35 Diabetic_No, borderline diabetes 0.000479\n",
"19 AgeCategory_40-44 0.000444\n",
"18 AgeCategory_35-39 0.000412\n",
"17 AgeCategory_30-34 0.000267\n",
"29 Race_Asian 0.000260\n",
"15 AgeCategory_18-24 0.000231\n",
"16 AgeCategory_25-29 0.000217\n",
"37 Diabetic_Yes (during pregnancy) 0.000184\n"
]
}
],
"source": [
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"from imblearn.over_sampling import RandomOverSampler\n",
"import featuretools as ft\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"\n",
"# Загрузка данных\n",
"df = pd.read_csv(\".//static//csv//heart_2020_cleaned.csv\")\n",
"\n",
"# Разделение на обучающую и тестовую выборки (например, 70% обучающая, 30% тестовая)\n",
"train_df, test_df = train_test_split(df, test_size=0.3, random_state=42)\n",
"\n",
"# Разделение обучающей выборки на обучающую и контрольную (например, 70% обучающая, 30% контрольная)\n",
"train_df, val_df = train_test_split(train_df, test_size=0.3, random_state=42)\n",
"\n",
"# Вывод размеров выборок\n",
"print(\"Размер обучающей выборки:\", len(train_df))\n",
"print(\"Размер контрольной выборки:\", len(val_df))\n",
"print(\"Размер тестовой выборки:\", len(test_df))\n",
"\n",
"# Определение категориальных признаков\n",
"categorical_features = [\n",
" 'Smoking', 'AlcoholDrinking', 'Stroke', 'DiffWalking', 'Sex', 'AgeCategory', 'Race',\n",
" 'Diabetic', 'PhysicalActivity', 'GenHealth', 'Asthma', 'KidneyDisease', 'SkinCancer'\n",
"]\n",
"\n",
"# Применение one-hot encoding к обучающей выборке\n",
"train_df_encoded = pd.get_dummies(train_df, columns=categorical_features)\n",
"\n",
"# Применение one-hot encoding к контрольной выборке\n",
"val_df_encoded = pd.get_dummies(val_df, columns=categorical_features)\n",
"\n",
"# Применение one-hot encoding к тестовой выборке\n",
"test_df_encoded = pd.get_dummies(test_df, columns=categorical_features)\n",
"\n",
"# Определение сущностей\n",
"es = ft.EntitySet(id='heart_data')\n",
"es = es.add_dataframe(dataframe_name='heart', dataframe=train_df_encoded, index='id')\n",
"\n",
"# Генерация признаков\n",
"feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='heart', max_depth=2)\n",
"\n",
"# Преобразование признаков для контрольной и тестовой выборок\n",
"val_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=val_df_encoded.index)\n",
"test_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=test_df_encoded.index)\n",
"\n",
"# Оценка важности признаков\n",
"X = feature_matrix\n",
"y = train_df_encoded['HeartDisease']\n",
"\n",
"# Разделение данных на обучающую и тестовую выборки\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"\n",
"# Обучение модели\n",
"model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
"model.fit(X_train, y_train)\n",
"\n",
"# Получение важности признаков\n",
"importances = model.feature_importances_\n",
"feature_names = feature_matrix.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": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Размер обучающей выборки: 15670\n",
"Размер контрольной выборки: 6716\n",
"Размер тестовой выборки: 9594\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/oleg/aim_labs/lab_3/aimenv/lib/python3.12/site-packages/featuretools/entityset/entityset.py:1733: UserWarning: index id not found in dataframe, creating new integer column\n",
" warnings.warn(\n",
"/home/oleg/aim_labs/lab_3/aimenv/lib/python3.12/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",
"/home/oleg/aim_labs/lab_3/aimenv/lib/python3.12/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",
"/home/oleg/aim_labs/lab_3/aimenv/lib/python3.12/site-packages/featuretools/computational_backends/feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
" df = pd.concat([df, default_df], sort=True)\n",
"/home/oleg/aim_labs/lab_3/aimenv/lib/python3.12/site-packages/featuretools/computational_backends/feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
" df = pd.concat([df, default_df], sort=True)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 1.0\n",
"Precision: 1.0\n",
"Recall: 1.0\n",
"F1 Score: 1.0\n",
"ROC AUC: 1.0\n",
"Cross-validated Accuracy: 0.906126356094448\n"
]
},
{
"data": {
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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: 0.9994894703254626\n",
"Train Precision: 0.9992816091954023\n",
"Train Recall: 0.9949928469241774\n",
"Train F1 Score: 0.9971326164874552\n",
"Train ROC AUC: 0.9974613898298016\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\n",
"from sklearn.model_selection import cross_val_score\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import featuretools as ft\n",
"\n",
"# Загрузка данных\n",
"df = pd.read_csv(\".//static//csv//heart_2020_cleaned.csv\")\n",
"\n",
"# Уменьшение размера выборки для ускорения работы (опционально)\n",
"df = df.sample(frac=0.1, random_state=42)\n",
"\n",
"# Разделение на обучающую и тестовую выборки (например, 70% обучающая, 30% тестовая)\n",
"train_df, test_df = train_test_split(df, test_size=0.3, random_state=42)\n",
"\n",
"# Разделение обучающей выборки на обучающую и контрольную (например, 70% обучающая, 30% контрольная)\n",
"train_df, val_df = train_test_split(train_df, test_size=0.3, random_state=42)\n",
"\n",
"# Вывод размеров выборок\n",
"print(\"Размер обучающей выборки:\", len(train_df))\n",
"print(\"Размер контрольной выборки:\", len(val_df))\n",
"print(\"Размер тестовой выборки:\", len(test_df))\n",
"\n",
"# Определение категориальных признаков\n",
"categorical_features = [\n",
" 'Smoking', 'AlcoholDrinking', 'Stroke', 'DiffWalking', 'Sex', 'AgeCategory', 'Race',\n",
" 'Diabetic', 'PhysicalActivity', 'GenHealth', 'Asthma', 'KidneyDisease', 'SkinCancer'\n",
"]\n",
"\n",
"# Применение one-hot encoding к обучающей выборке\n",
"train_df_encoded = pd.get_dummies(train_df, columns=categorical_features)\n",
"\n",
"# Применение one-hot encoding к контрольной выборке\n",
"val_df_encoded = pd.get_dummies(val_df, columns=categorical_features)\n",
"\n",
"# Применение one-hot encoding к тестовой выборке\n",
"test_df_encoded = pd.get_dummies(test_df, columns=categorical_features)\n",
"\n",
"# Определение сущностей\n",
"es = ft.EntitySet(id='heart_data')\n",
"es = es.add_dataframe(dataframe_name='heart', dataframe=train_df_encoded, index='id')\n",
"\n",
"# Генерация признаков с уменьшенной глубиной\n",
"feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='heart', max_depth=1)\n",
"\n",
"# Преобразование признаков для контрольной и тестовой выборок\n",
"val_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=val_df_encoded.index)\n",
"test_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=test_df_encoded.index)\n",
"\n",
"# Удаление строк с NaN\n",
"feature_matrix = feature_matrix.dropna()\n",
"val_feature_matrix = val_feature_matrix.dropna()\n",
"test_feature_matrix = test_feature_matrix.dropna()\n",
"\n",
"# Разделение данных на обучающую и тестовую выборки\n",
"X_train = feature_matrix.drop('HeartDisease', axis=1)\n",
"y_train = feature_matrix['HeartDisease']\n",
"X_val = val_feature_matrix.drop('HeartDisease', axis=1)\n",
"y_val = val_feature_matrix['HeartDisease']\n",
"X_test = test_feature_matrix.drop('HeartDisease', axis=1)\n",
"y_test = test_feature_matrix['HeartDisease']\n",
"\n",
"# Выбор модели\n",
"model = RandomForestClassifier(random_state=42)\n",
"\n",
"# Обучение модели\n",
"model.fit(X_train, y_train)\n",
"\n",
"# Предсказание и оценка\n",
"y_pred = model.predict(X_test)\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(model, X_train, y_train, cv=5, scoring='accuracy')\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.columns\n",
"\n",
"importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': feature_importances})\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)\n",
"\n",
"accuracy_train = accuracy_score(y_train, y_train_pred)\n",
"precision_train = precision_score(y_train, y_train_pred)\n",
"recall_train = recall_score(y_train, y_train_pred)\n",
"f1_train = f1_score(y_train, y_train_pred)\n",
"roc_auc_train = roc_auc_score(y_train, 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}\")\n",
"\n",
"# Визуализация результатов\n",
"plt.figure(figsize=(10, 6))\n",
"plt.scatter(y_test, y_pred, alpha=0.5)\n",
"plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
"plt.xlabel('Actual HeartDisease')\n",
"plt.ylabel('Predicted HeartDisease')\n",
"plt.title('Actual vs Predicted HeartDisease')\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "aimenv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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