"1. Разработка персонализированных программ профилактики сердечно-сосудистых заболеваний.\n",
"Цель технического проекта: создание модели машинного обучения, которая будет прогнозировать риск сердечного приступа для каждого пациента на основе его индивидуальных факторов риска, и разработка онлайн-платформы или приложения для предоставления персонализированных рекомендаций по профилактике.\n",
"2. Улучшение качества медицинской \n",
"Цель технического проекта: использование данных для выявления групп населения с наибольшим риском сердечного приступа и разработки целевых программ профилактики и раннего выявления заболеваний."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Выполним разбиение на 3 выборки: обучающую, контрольную и тестовую"
" print(f\"Распределение классов в {title}:\")\n",
" print(class_distribution)\n",
" sns.countplot(x='HeartDisease', data=df)\n",
" plt.title(f'Распределение классов в {title}')\n",
" plt.show()\n",
"\n",
"# Проверка сбалансированности для обучающей, контрольной и тестовой выборок\n",
"check_balance(train_df, 'Обучающей выборке')\n",
"check_balance(val_df, 'Контрольной выборке')\n",
"check_balance(test_df, 'Тестовой выборке')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Можно заметить, что данные не сбалансированы - во всех выборках количество значений \"No\" превышает \"Yes\" в среднем в 10 раз. Для балансировки данных будет применен метод upsampling "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Размер обучающей выборки до upsampling: 156699\n",
"Размер контрольной выборки: 67157\n",
"Размер тестовой выборки: 95939\n",
"\n",
"Распределение классов в всем датасете:\n",
"Класс No: 292422 (91.44%)\n",
"Класс Yes: 27373 (8.56%)\n",
"\n",
"Распределение классов в Обучающей выборке до upsampling:\n",
"Класс No: 143331 (91.47%)\n",
"Класс Yes: 13368 (8.53%)\n",
"Размер обучающей выборки после upsampling: 286662\n",
"\n",
"Распределение классов в Обучающей выборке после upsampling:\n",
"Создание новых признаков на основе экспертных знаний и логики предметной области. Например, для данных о продаже автомобилей можно создать признак \"возраст автомобиля\" как разницу между текущим годом и годом выпуска."
"Масштабирование признаков - это процесс преобразования числовых признаков таким образом, чтобы они имели одинаковый масштаб. Это важно для многих алгоритмов машинного обучения, которые чувствительны к масштабу признаков, таких как линейная регрессия, метод опорных векторов (SVM) и нейронные сети."
"Конструирование признаков с применением фреймворка Featuretools"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\featuretools\\entityset\\entityset.py:1733: UserWarning: index id not found in dataframe, creating new integer column\n",
" warnings.warn(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\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",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\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",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\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",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\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",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\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",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\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",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
" series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\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",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\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",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\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",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\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",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\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",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
" series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Обучающая выборка после конструирования признаков:\n",
"print(\"Обучающая выборка после конструирования признаков:\")\n",
"print(feature_matrix.head())\n",
"print(\"Контрольная выборка после конструирования признаков:\")\n",
"print(val_feature_matrix.head())\n",
"print(\"Тестовая выборка после конструирования признаков:\")\n",
"print(test_feature_matrix.head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Оценка качества каждого набора признаков\n",
"\n",
"Предсказательная способность\n",
"Метрики: RMSE, MAE, R²\n",
"\n",
"Методы: Обучение модели на обучающей выборке и оценка на контрольной и тестовой выборках.\n",
"\n",
"Скорость вычисления\n",
"Методы: Измерение времени выполнения генерации признаков и обучения модели.\n",
"\n",
"Надежность\n",
"Методы: Кросс-валидация, анализ чувствительности модели к изменениям в данных.\n",
"\n",
"Корреляция\n",
"Методы: Анализ корреляционной матрицы признаков, удаление мультиколлинеарных признаков.\n",
"\n",
"Цельность\n",
"Методы: Проверка логической связи между признаками и целевой переменной, интерпретация результатов модели."
]
},
{
"cell_type": "code",
"execution_count": 30,
"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": [
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\featuretools\\entityset\\entityset.py:1733: UserWarning: index id not found in dataframe, creating new integer column\n",
" warnings.warn(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\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",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\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",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\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",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\featuretools\\entityset\\entityset.py:1733: UserWarning: index id not found in dataframe, creating new integer column\n",
" warnings.warn(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\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",
"c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\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",
