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lab4
2024-12-10 22:22:36 +04:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Начало лабораторной работы"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')\n"
]
}
],
"source": [
"import pandas as pd\n",
"df = pd.read_csv(\".//static//csv//Yamana_Gold_Inc._AUY.csv\")\n",
"print(df.columns)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Бизнес-цели"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Прогнозирование цен на золото:\n",
"\n",
"Цель: Разработать модель, которая будет предсказывать объем выкупленных акций на основе: цены открытия, цены закрытия, самой высокой цене, самой низкой цене\n",
"Применение:\n",
"Узнать, какие лучше цены выставлять на акции.\n",
"\n",
"2. Оптимизация цен на акции:\n",
"Цель: Определить оптимальную цену на акцию, чтобы объем их скупа был больше.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Прогнозирование объема проданных акций"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Среднее значение поля 'Volume: 9081991.374023996\n",
" Date Open High Low Close Adj Close Volume \\\n",
"0 6/22/2001 3.428571 3.428571 3.428571 3.428571 2.806002 0 \n",
"1 6/25/2001 3.428571 3.428571 3.428571 3.428571 2.806002 0 \n",
"2 6/26/2001 3.714286 3.714286 3.714286 3.714286 3.039837 0 \n",
"3 6/27/2001 3.714286 3.714286 3.714286 3.714286 3.039837 0 \n",
"4 6/28/2001 3.714286 3.714286 3.714286 3.714286 3.039837 0 \n",
"\n",
" above_average_volume volume_volatility \n",
"0 0 76714000 \n",
"1 0 76714000 \n",
"2 0 76714000 \n",
"3 0 76714000 \n",
"4 0 76714000 \n"
]
}
],
"source": [
"import pandas as pd\n",
"\n",
"# Загружаем набор данных\n",
"df = pd.read_csv(\".//static//csv//Yamana_Gold_Inc._AUY.csv\")\n",
"\n",
"# Устанавливаем случайное состояние\n",
"random_state = 28\n",
"\n",
"# Рассчитываем среднее значение объема\n",
"average_count = df['Volume'].mean()\n",
"print(f\"Среднее значение поля 'Volume: {average_count}\")\n",
"\n",
"# Создаем новую переменную, указывающую, превышает ли объемная продажа среднюю\n",
"df[\"above_average_volume\"] = (df[\"Volume\"] > average_count).astype(int)\n",
"\n",
"# Рассчитываем волатильность (разницу между максимальной и минимальной объемная продажаю)\n",
"df[\"volume_volatility\"] = df[\"Volume\"].max() - df[\"Volume\"].min()\n",
"\n",
"# Выводим первые строки измененной таблицы для проверки\n",
"print(df.head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. Оптимизация параметров магазина:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Средняя объемная продажа для 'Open':\n",
"Open\n",
"1.142857 2.815714e+02\n",
"1.260000 1.500000e+02\n",
"1.320000 5.700000e+03\n",
"1.380000 1.200000e+03\n",
"1.400000 0.000000e+00\n",
" ... \n",
"19.940001 8.288800e+06\n",
"20.059999 6.851500e+06\n",
"20.100000 4.836700e+06\n",
"20.250000 4.154200e+06\n",
"20.420000 4.264000e+06\n",
"Name: Volume, Length: 1421, dtype: float64\n",
"\n",
"Средняя объемная продажа для 'Close':\n",
"Close\n",
"1.142857 2.815714e+02\n",
"1.260000 1.500000e+02\n",
"1.400000 3.000000e+02\n",
"1.410000 1.735315e+06\n",
"1.420000 3.000000e+03\n",
" ... \n",
"19.750000 5.734200e+06\n",
"20.080000 4.836700e+06\n",
"20.129999 6.276400e+06\n",
"20.209999 8.799600e+06\n",
"20.389999 6.851500e+06\n",
"Name: Volume, Length: 1442, dtype: float64\n",
"\n",
"Средняя объемная продажа для 'High':\n",
"High\n",
"1.142857 2.815714e+02\n",
"1.260000 1.500000e+02\n",
"1.400000 3.000000e+02\n",
"1.410000 7.909091e+02\n",
"1.428571 1.957805e+02\n",
" ... \n",
"20.209999 8.799600e+06\n",
"20.309999 4.154200e+06\n",
"20.389999 4.836700e+06\n",
"20.500000 6.851500e+06\n",
"20.590000 4.264000e+06\n",
"Name: Volume, Length: 1423, dtype: float64\n",
"\n",
"Средняя объемная продажа для 'Low':\n",
"Low\n",
"1.142857 2.815714e+02\n",
"1.260000 1.500000e+02\n",
"1.320000 5.700000e+03\n",
"1.380000 1.125310e+07\n",
"1.400000 1.626675e+06\n",
" ... \n",
"19.570000 8.288800e+06\n",
"19.650000 4.154200e+06\n",
"19.879999 6.851500e+06\n",
"20.000000 4.836700e+06\n",
"20.090000 4.264000e+06\n",
"Name: Volume, Length: 1410, dtype: float64\n",
"\n",
"Средняя объемная продажа для комбинации 'Open' и 'Close':\n",
"Open Close \n",
"1.142857 1.142857 2.815714e+02\n",
"1.260000 1.260000 1.500000e+02\n",
"1.320000 1.410000 5.700000e+03\n",
"1.380000 1.400000 1.200000e+03\n",
"1.400000 1.400000 0.000000e+00\n",
" ... \n",
"19.940001 20.129999 8.288800e+06\n",
"20.059999 20.389999 6.851500e+06\n",
"20.100000 20.080000 4.836700e+06\n",
"20.250000 19.719999 4.154200e+06\n",
"20.420000 20.129999 4.264000e+06\n",
"Name: Volume, Length: 4401, dtype: float64\n",
"\n",
"Средняя объемная продажа для комбинации 'High' и 'Low':\n",
"High Low \n",
"1.142857 1.142857 2.815714e+02\n",
"1.260000 1.260000 1.500000e+02\n",
"1.400000 1.380000 1.200000e+03\n",
" 1.400000 0.000000e+00\n",
"1.410000 1.320000 5.700000e+03\n",
" ... \n",
"20.209999 19.290001 8.799600e+06\n",
"20.309999 19.650000 4.154200e+06\n",
"20.389999 20.000000 4.836700e+06\n",
"20.500000 19.879999 6.851500e+06\n",
"20.590000 20.090000 4.264000e+06\n",
"Name: Volume, Length: 4246, dtype: float64\n",
"\n"
]
}
],
"source": [
"import pandas as pd\n",
"\n",
"# Загружаем набор данных\n",
"df = pd.read_csv(\".//static//csv//Yamana_Gold_Inc._AUY.csv\")\n",
"\n",
"# Устанавливаем случайное состояние\n",
"random_state = 42\n",
"\n",
"# Рассчитываем среднюю объемную продажу для каждого значения каждого признака\n",
"for column in [\"Open\", \"Close\", \"High\", \"Low\"]:\n",
" print(f\"Средняя объемная продажа для '{column}':\")\n",
" print(df.groupby(column)[\"Volume\"].mean())\n",
" print()\n",
"\n",
"\n",
"print(\"Средняя объемная продажа для комбинации 'Open' и 'Close':\")\n",
"print(df.groupby([\"Open\", \"Close\"])[\"Volume\"].mean())\n",
"print()\n",
"\n",
"\n",
"print(\"Средняя объемная продажа для комбинации 'High' и 'Low':\")\n",
"print(df.groupby([\"High\", \"Low\"])[\"Volume\"].mean())\n",
"print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Выбор ориентира:\n",
"1. Прогнозирование стоимости акций взносов:\n",
"Ориентир:\n",
"\n",
"R² (коэффициент детерминации): 0.75 - 0.85\n",
"\n",
"MAE (средняя абсолютная ошибка): 2000000 - 3.500.000 продаж\n",
"\n",
"RMSE (среднеквадратичная ошибка): 2200000 - 3600000 продаж"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MAE: 4479413.782319424\n",
"MSE: 33820915982442.465\n",
"RMSE: 5815575.292474723\n",
"R²: 0.42948176526183957\n",
"Ориентиры для прогнозирования не достигнуты.\n",
"Средняя объемная продажа 'Open':\n",
"Open\n",
"1.142857 2.815714e+02\n",
"1.260000 1.500000e+02\n",
"1.320000 5.700000e+03\n",
"1.380000 1.200000e+03\n",
"1.400000 0.000000e+00\n",
" ... \n",
"19.940001 8.288800e+06\n",
"20.059999 6.851500e+06\n",
"20.100000 4.836700e+06\n",
"20.250000 4.154200e+06\n",
"20.420000 4.264000e+06\n",
"Name: Volume, Length: 1421, dtype: float64\n",
"\n",
"Средняя объемная продажа 'High':\n",
"High\n",
"1.142857 2.815714e+02\n",
"1.260000 1.500000e+02\n",
"1.400000 3.000000e+02\n",
"1.410000 7.909091e+02\n",
"1.428571 1.957805e+02\n",
" ... \n",
"20.209999 8.799600e+06\n",
"20.309999 4.154200e+06\n",
"20.389999 4.836700e+06\n",
"20.500000 6.851500e+06\n",
"20.590000 4.264000e+06\n",
"Name: Volume, Length: 1423, dtype: float64\n",
"\n",
"Средняя объемная продажа 'Close':\n",
"Close\n",
"1.142857 2.815714e+02\n",
"1.260000 1.500000e+02\n",
"1.400000 3.000000e+02\n",
"1.410000 1.735315e+06\n",
"1.420000 3.000000e+03\n",
" ... \n",
"19.750000 5.734200e+06\n",
"20.080000 4.836700e+06\n",
"20.129999 6.276400e+06\n",
"20.209999 8.799600e+06\n",
"20.389999 6.851500e+06\n",
"Name: Volume, Length: 1442, dtype: float64\n",
"\n",
"Средняя объемная продажа 'Low':\n",
"Low\n",
"1.142857 2.815714e+02\n",
"1.260000 1.500000e+02\n",
"1.320000 5.700000e+03\n",
"1.380000 1.125310e+07\n",
"1.400000 1.626675e+06\n",
" ... \n",
"19.570000 8.288800e+06\n",
"19.650000 4.154200e+06\n",
"19.879999 6.851500e+06\n",
"20.000000 4.836700e+06\n",
"20.090000 4.264000e+06\n",
"Name: Volume, Length: 1410, dtype: float64\n",
"\n",
"Средняя посещаемость взносов для комбинации 'Open' и 'Close':\n",
"Open Close \n",
"1.142857 1.142857 2.815714e+02\n",
"1.260000 1.260000 1.500000e+02\n",
"1.320000 1.410000 5.700000e+03\n",
"1.380000 1.400000 1.200000e+03\n",
"1.400000 1.400000 0.000000e+00\n",
" ... \n",
"19.940001 20.129999 8.288800e+06\n",
"20.059999 20.389999 6.851500e+06\n",
"20.100000 20.080000 4.836700e+06\n",
"20.250000 19.719999 4.154200e+06\n",
"20.420000 20.129999 4.264000e+06\n",
"Name: Volume, Length: 4401, dtype: float64\n",
"\n",
"Средняя посещаемость взносов для комбинации 'High' и 'Low':\n",
"High Low \n",
"1.142857 1.142857 2.815714e+02\n",
"1.260000 1.260000 1.500000e+02\n",
"1.400000 1.380000 1.200000e+03\n",
" 1.400000 0.000000e+00\n",
"1.410000 1.320000 5.700000e+03\n",
" ... \n",
"20.209999 19.290001 8.799600e+06\n",
"20.309999 19.650000 4.154200e+06\n",
"20.389999 20.000000 4.836700e+06\n",
"20.500000 19.879999 6.851500e+06\n",
"20.590000 20.090000 4.264000e+06\n",
"Name: Volume, Length: 4246, dtype: float64\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
" warnings.warn(\n"
]
}
],
"source": [
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
"\n",
"# Загружаем набор данных\n",
"df = pd.read_csv(\".//static//csv//Yamana_Gold_Inc._AUY.csv\")\n",
"\n",
"\n",
"# Разделяем данные на признаки (X) и целевую переменную (y)\n",
"\n",
"X = df.drop(columns=[\"Volume\", \"Date\"], axis=1)\n",
"\n",
"y = df[\"Volume\"]\n",
"\n",
"# Разделяем данные 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",
"# Стандартизируем признаки\n",
"scaler = StandardScaler()\n",
"X_train = scaler.fit_transform(X_train)\n",
"X_test = scaler.transform(X_test)\n",
"\n",
"# Обучаем модель линейной регрессии\n",
"model = LinearRegression()\n",
"model.fit(X_train, y_train)\n",
"\n",
"# Делаем предсказания на тестовой выборке\n",
"y_pred = model.predict(X_test)\n",
"\n",
"# Оцениваем качество модели\n",
"mae = mean_absolute_error(y_test, y_pred)\n",
"mse = mean_squared_error(y_test, y_pred)\n",
"rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
"r2 = r2_score(y_test, y_pred)\n",
"\n",
"print(f\"MAE: {mae}\")\n",
"print(f\"MSE: {mse}\")\n",
"print(f\"RMSE: {rmse}\")\n",
"print(f\"R²: {r2}\")\n",
"\n",
"# Проверяем, достигнуты ли ориентиры\n",
"if r2 >= 0.75 and mae <= 1500000 and rmse <= 1700000:\n",
" print(\"Ориентиры для прогнозирования достигнуты!\")\n",
"else:\n",
" print(\"Ориентиры для прогнозирования не достигнуты.\")\n",
"\n",
"\n",
"columns_to_group = [\n",
" \"Open\",\n",
" \"High\",\n",
" \"Close\", \"Low\"\n",
"]\n",
"\n",
"# Рассчитываем среднюю объемная продажа для каждого значения каждого признака\n",
"for column in columns_to_group:\n",
" print(f\"Средняя объемная продажа '{column}':\")\n",
" print(df.groupby(column)[\"Volume\"].mean())\n",
" print()\n",
"\n",
"# Рассчитываем среднюю объемная продажа для комбинаций признаков\n",
"\n",
"print(\n",
" \"Средняя посещаемость взносов для комбинации 'Open' и 'Close':\"\n",
")\n",
"print(df.groupby([\"Open\", \"Close\"])[\"Volume\"].mean())\n",
"print()\n",
"\n",
"print(\n",
" \"Средняя посещаемость взносов для комбинации 'High' и 'Low':\"\n",
")\n",
"print(df.groupby([\"High\", \"Low\"])[\"Volume\"].mean())\n",
"print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Анализ применимости алгоритмов обучения с учителем для решения поставленных задач:\n",
"1. Прогнозирование выкупа акций:\n",
"Задача: Регрессия\n",
"\n",
"Свойства алгоритмов:\n",
"\n",
"Линейная регрессия:\n",
"Применимость: Хорошо подходит для задач, где зависимость между признаками и целевой переменной линейна.\n",
"Преимущества: Проста в реализации, интерпретируема.\n",
"Недостатки: Может плохо работать, если зависимость нелинейна.\n",
"\n",
"Деревья решений (регрессия):\n",
"Применимость: Подходит для задач с нелинейными зависимостями.\n",
"Преимущества: Может обрабатывать категориальные признаки, не требует масштабирования данных.\n",
"Недостатки: Подвержены переобучению, могут давать нестабильные результаты.\n",
"\n",
"Случайный лес (регрессия):\n",
"Применимость: Хорошо подходит для задач с нелинейными зависимостями и большим количеством признаков.\n",
"Преимущества: Устойчив к переобучению, может обрабатывать категориальные признаки.\n",
"Недостатки: Менее интерпретируем, чем линейная регрессия.\n",
"\n",
"Градиентный бустинг (регрессия):\n",
"Применимость: Подходит для задач с нелинейными зависимостями и сложными взаимосвязями между признаками.\n",
"Преимущества: Может достигать высокой точности, устойчив к переобучению.\n",
"Недостатки: Сложнее в настройке, чем случайный лес, менее интерпретируем.\n",
"\n",
"Нейронные сети (регрессия):\n",
"Применимость: Подходит для задач с очень сложными зависимостями и большим количеством данных.\n",
"Преимущества: Может моделировать очень сложные зависимости.\n",
"Недостатки: Требует большого количества данных, сложнее в настройке и интерпретации.\n",
"\n",
"Вывод:\n",
"\n",
"Линейная регрессия: Может быть хорошим выбором для начала, особенно если зависимость между признаками и целевой переменной линейна.\n",
"\n",
"Деревья решений и случайный лес: Подходят для задач с нелинейными зависимостями.\n",
"\n",
"Градиентный бустинг: Может давать более высокую точность, чем случайный лес, но требует больше времени на настройку.\n",
"\n",
"Нейронные сети: Могут быть излишними для этой задачи, если данных недостаточно много.\n",
"\n",
"2. Оптимизация тарифной сетки:\n",
"Задача: Классификация \n",
"\n",
"Свойства алгоритмов:\n",
"\n",
"Логистическая регрессия:\n",
"Применимость: Хорошо подходит для задач бинарной классификации, где зависимость между признаками и целевой переменной линейна.\n",
"Преимущества: Проста в реализации, интерпретируема.\n",
"Недостатки: Может плохо работать, если зависимость нелинейна.\n",
"\n",
"Деревья решений (классификация):\n",
"Применимость: Подходит для задач с нелинейными зависимостями.\n",
"Преимущества: Может обрабатывать категориальные признаки, не требует масштабирования данных.\n",
"Недостатки: Подвержены переобучению, могут давать нестабильные результаты.\n",
"\n",
"Случайный лес (классификация):\n",
"Применимость: Хорошо подходит для задач с нелинейными зависимостями и большим количеством признаков.\n",
"Преимущества: Устойчив к переобучению, может обрабатывать категориальные признаки.\n",
"Недостатки: Менее интерпретируем, чем линейная регрессия.\n",
"\n",
"Градиентный бустинг (классификация):\n",
"Применимость: Подходит для задач с нелинейными зависимостями и сложными взаимосвязями между признаками.\n",
"Преимущества: Может достигать высокой точности, устойчив к переобучению.\n",
"Недостатки: Сложнее в настройке, чем случайный лес, менее интерпретируем.\n",
"\n",
"Нейронные сети (классификация):\n",
"Применимость: Подходит для задач с очень сложными зависимостями и большим количеством данных.\n",
"Преимущества: Может моделировать очень сложные зависимости.\n",
"Недостатки: Требует большого количества данных, сложнее в настройке и интерпретации.\n",
"\n",
"Вывод:\n",
"\n",
"Логистическая регрессия: Может быть хорошим выбором для начала, особенно если зависимость между признаками и целевой переменной линейна.\n",
"\n",
"Деревья решений и случайный лес: Подходят для задач с нелинейными зависимостями.\n",
"\n",
"Градиентный бустинг: Может давать более высокую точность, чем случайный лес, но требует больше времени на настройку.\n",
"\n",
"Нейронные сети: Могут быть излишними для этой задачи, если данных недостаточно много.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Прогнозирование стоимости акций:\n",
"Выбранные модели:\n",
"\n",
"Линейная регрессия\n",
"\n",
"Случайный лес (регрессия)\n",
"\n",
"Градиентный бустинг (регрессия)\n",
"\n",
"2. Оптимизация тарифной сетки:\n",
"Выбранные модели:\n",
"\n",
"Логистическая регрессия\n",
"\n",
"Случайный лес (классификация)\n",
"\n",
"Градиентный бустинг (классификация)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Результаты для задачи регрессии:\n",
"Model: Linear Regression\n",
"MAE: 4479413.782319424\n",
"MSE: 33820915982442.465\n",
"RMSE: 5815575.292474723\n",
"R²: 0.42948176526183957\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
" warnings.warn(\n",
"d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: Random Forest Regression\n",
"MAE: 2992885.6305958964\n",
"MSE: 23229080308355.86\n",
"RMSE: 4819655.621344316\n",
"R²: 0.6081533126129994\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: Gradient Boosting Regression\n",
"MAE: 3630084.451629419\n",
"MSE: 28221268640877.676\n",
"RMSE: 5312369.399889062\n",
"R²: 0.523941090908852\n",
"\n",
"Результаты для задачи классификации:\n",
"Model: Logistic Regression\n",
"Accuracy: 0.7526165556612749\n",
"\n",
"Model: Random Forest Classification\n",
"Accuracy: 0.80209324452902\n",
"\n",
"Model: Gradient Boosting Classification\n",
"Accuracy: 0.7849666983824929\n",
"\n"
]
}
],
"source": [
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.linear_model import LinearRegression, LogisticRegression\n",
"from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
"from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier\n",
"from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, accuracy_score\n",
"\n",
"# Загружаем набор данных\n",
"df = pd.read_csv(\".//static//csv//Yamana_Gold_Inc._AUY.csv\")\n",
"\n",
"\n",
"# Разделяем данные на признаки (X) и целевую переменную (y) для задачи регрессии\n",
"X_reg = df.drop(columns = [\"Volume\", \"Date\"], axis=1)\n",
"y_reg = df[\"Volume\"]\n",
"\n",
"# Разделяем данные на обучающую и тестовую выборки для задачи регрессии\n",
"X_train_reg, X_test_reg, y_train_reg, y_test_reg = train_test_split(X_reg, y_reg, test_size=0.2, random_state=42)\n",
"\n",
"# Стандартизируем признаки для задачи регрессии\n",
"scaler_reg = StandardScaler()\n",
"X_train_reg = scaler_reg.fit_transform(X_train_reg)\n",
"X_test_reg = scaler_reg.transform(X_test_reg)\n",
"\n",
"# Список моделей для задачи регрессии\n",
"models_reg = {\n",
" \"Linear Regression\": LinearRegression(),\n",
" \"Random Forest Regression\": RandomForestRegressor(),\n",
" \"Gradient Boosting Regression\": GradientBoostingRegressor()\n",
"}\n",
"\n",
"# Обучаем и оцениваем модели для задачи регрессии\n",
"print(\"Результаты для задачи регрессии:\")\n",
"for name, model in models_reg.items():\n",
" model.fit(X_train_reg, y_train_reg)\n",
" y_pred_reg = model.predict(X_test_reg)\n",
" mae = mean_absolute_error(y_test_reg, y_pred_reg)\n",
" mse = mean_squared_error(y_test_reg, y_pred_reg)\n",
" rmse = mean_squared_error(y_test_reg, y_pred_reg, squared=False)\n",
" r2 = r2_score(y_test_reg, y_pred_reg)\n",
" print(f\"Model: {name}\")\n",
" print(f\"MAE: {mae}\")\n",
" print(f\"MSE: {mse}\")\n",
" print(f\"RMSE: {rmse}\")\n",
" print(f\"R²: {r2}\")\n",
" print()\n",
"\n",
"# Разделяем данные на признаки (X) и целевую переменную (y) для задачи классификации\n",
"X_class = df.drop(columns=[\"Volume\", \"Date\"], axis=1)\n",
"y_class = (df[\"Volume\"] > df[\"Volume\"].mean()).astype(int)\n",
"\n",
"# Разделяем данные на обучающую и тестовую выборки для задачи классификации\n",
"X_train_class, X_test_class, y_train_class, y_test_class = train_test_split(X_class, y_class, test_size=0.2, random_state=42)\n",
"\n",
"# Стандартизируем признаки для задачи классификации\n",
"scaler_class = StandardScaler()\n",
"X_train_class = scaler_class.fit_transform(X_train_class)\n",
"X_test_class = scaler_class.transform(X_test_class)\n",
"\n",
"# Список моделей для задачи классификации\n",
"models_class = {\n",
" \"Logistic Regression\": LogisticRegression(),\n",
" \"Random Forest Classification\": RandomForestClassifier(),\n",
" \"Gradient Boosting Classification\": GradientBoostingClassifier()\n",
"}\n",
"\n",
"# Обучаем и оцениваем модели для задачи классификации\n",
"print(\"Результаты для задачи классификации:\")\n",
"for name, model in models_class.items():\n",
" model.fit(X_train_class, y_train_class)\n",
" y_pred_class = model.predict(X_test_class)\n",
" accuracy = accuracy_score(y_test_class, y_pred_class)\n",
" print(f\"Model: {name}\")\n",
" print(f\"Accuracy: {accuracy}\")\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Прогнозирование стоимости акций:\n",
"Конвейер для задачи регрессии:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Результаты для задачи регрессии:\n",
"Model: Linear Regression\n",
"MAE: 5371500.134804331\n",
"MSE: 47781112642081.98\n",
"RMSE: 6912388.345722626\n",
"R²: 0.19399001338292088\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
" warnings.warn(\n",
"d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: Random Forest Regression\n",
"MAE: 4408370.880479668\n",
"MSE: 42119887803881.95\n",
"RMSE: 6489983.652050439\n",
"R²: 0.28948807744547955\n",
"\n",
"Model: Gradient Boosting Regression\n",
"MAE: 4651931.4982183585\n",
"MSE: 40455010983490.94\n",
"RMSE: 6360425.377558559\n",
"R²: 0.3175725499393369\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
" warnings.warn(\n"
]
}
],
"source": [
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
"\n",
"# Загружаем набор данных\n",
"df = pd.read_csv(\".//static//csv//Yamana_Gold_Inc._AUY.csv\")\n",
"\n",
"\n",
"numerical_cols = [\"Open\", \"Close\", \"High\", \"Low\"]\n",
"\n",
"\n",
"preprocessor = ColumnTransformer(\n",
" transformers=[\n",
" ('num', StandardScaler(), numerical_cols)\n",
" ])\n",
"\n",
"# Список моделей для задачи регрессии\n",
"models_reg = {\n",
" \"Linear Regression\": LinearRegression(),\n",
" \"Random Forest Regression\": RandomForestRegressor(),\n",
" \"Gradient Boosting Regression\": GradientBoostingRegressor()\n",
"}\n",
"\n",
"# Разделяем данные на признаки (X) и целевую переменную (y) для задачи регрессии\n",
"X_reg = df[numerical_cols]\n",
"y_reg = df[\"Volume\"]\n",
"\n",
"# Разделяем данные на обучающую и тестовую выборки для задачи регрессии\n",
"X_train_reg, X_test_reg, y_train_reg, y_test_reg = train_test_split(X_reg, y_reg, test_size=0.2, random_state=42)\n",
"\n",
"# Обучаем и оцениваем модели для задачи регрессии\n",
"print(\"Результаты для задачи регрессии:\")\n",
"for name, model in models_reg.items():\n",
" pipeline = Pipeline(steps=[\n",
" ('preprocessor', preprocessor),\n",
" ('model', model)\n",
" ])\n",
" pipeline.fit(X_train_reg, y_train_reg)\n",
" y_pred_reg = pipeline.predict(X_test_reg)\n",
" mae = mean_absolute_error(y_test_reg, y_pred_reg)\n",
" mse = mean_squared_error(y_test_reg, y_pred_reg)\n",
" rmse = mean_squared_error(y_test_reg, y_pred_reg, squared=False)\n",
" r2 = r2_score(y_test_reg, y_pred_reg)\n",
" print(f\"Model: {name}\")\n",
" print(f\"MAE: {mae}\")\n",
" print(f\"MSE: {mse}\")\n",
" print(f\"RMSE: {rmse}\")\n",
" print(f\"R²: {r2}\")\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. Оптимизация характеристик магазина:\n",
"Конвейер для задачи классификации:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Результаты для задачи классификации:\n",
"Model: Logistic Regression\n",
"Accuracy: 0.6355851569933397\n",
"\n",
"Model: Random Forest Classification\n",
"Accuracy: 0.6945765937202664\n",
"\n",
"Model: Gradient Boosting Classification\n",
"Accuracy: 0.6936251189343482\n",
"\n"
]
}
],
"source": [
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"from sklearn.metrics import accuracy_score\n",
"\n",
"# Загружаем набор данных\n",
"df = pd.read_csv(\".//static//csv//Yamana_Gold_Inc._AUY.csv\")\n",
"\n",
"numerical_cols = [\"Open\", \"Close\", \"High\", \"Low\"]\n",
"# Создаем преобразователь для категориальных и числовых столбцов\n",
"preprocessor = ColumnTransformer(\n",
" transformers=[\n",
" ('num', StandardScaler(), numerical_cols)\n",
" ])\n",
"\n",
"# Список моделей для задачи классификации\n",
"models_class = {\n",
" \"Logistic Regression\": LogisticRegression(),\n",
" \"Random Forest Classification\": RandomForestClassifier(),\n",
" \"Gradient Boosting Classification\": GradientBoostingClassifier()\n",
"}\n",
"\n",
"# Разделяем данные на признаки (X) и целевую переменную (y) для задачи классификации\n",
"X_class = df[numerical_cols]\n",
"y_class = (df[\"Volume\"] > df[\"Volume\"].mean()).astype(int)\n",
"\n",
"# Разделяем данные на обучающую и тестовую выборки для задачи классификации\n",
"X_train_class, X_test_class, y_train_class, y_test_class = train_test_split(X_class, y_class, test_size=0.2, random_state=42)\n",
"\n",
"# Обучаем и оцениваем модели для задачи классификации\n",
"print(\"Результаты для задачи классификации:\")\n",
"for name, model in models_class.items():\n",
" pipeline = Pipeline(steps=[\n",
" ('preprocessor', preprocessor),\n",
" ('model', model)\n",
" ])\n",
" pipeline.fit(X_train_class, y_train_class)\n",
" y_pred_class = pipeline.predict(X_test_class)\n",
" accuracy = accuracy_score(y_test_class, y_pred_class)\n",
" print(f\"Model: {name}\")\n",
" print(f\"Accuracy: {accuracy}\")\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Прогнозирование объема:\n",
"\n",
"Настройка гиперпараметров для задачи регрессии:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Результаты для задачи регрессии:\n",
"Model: Linear Regression\n",
"Best Parameters: {}\n",
"MAE: 5371500.134804331\n",
"MSE: 47781112642081.98\n",
"RMSE: 6912388.345722626\n",
"R²: 0.19399001338292088\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
" warnings.warn(\n",
"d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: Random Forest Regression\n",
"Best Parameters: {'model__max_depth': 10, 'model__n_estimators': 100}\n",
"MAE: 4340519.215001971\n",
"MSE: 39191675053789.43\n",
"RMSE: 6260325.475068323\n",
"R²: 0.33888350984559035\n",
"\n",
"Model: Gradient Boosting Regression\n",
"Best Parameters: {'model__learning_rate': 0.1, 'model__max_depth': 3, 'model__n_estimators': 200}\n",
"MAE: 4502839.821286126\n",
"MSE: 39536963282425.29\n",
"RMSE: 6287842.498220298\n",
"R²: 0.333058912109105\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
" warnings.warn(\n"
]
}
],
"source": [
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split, GridSearchCV\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
"\n",
"# Загружаем набор данных\n",
"df = pd.read_csv(\".//static//csv//Yamana_Gold_Inc._AUY.csv\")\n",
"\n",
"# Определяем категориальные и числовые столбцы\n",
"\n",
"numerical_cols = [\"Open\", \"Close\", \"High\", \"Low\"]\n",
"\n",
"# Создаем преобразователь для категориальных и числовых столбцов\n",
"preprocessor = ColumnTransformer(\n",
" transformers=[\n",
" ('num', StandardScaler(), numerical_cols)\n",
" ])\n",
"\n",
"# Список моделей и их гиперпараметров для задачи регрессии\n",
"models_reg = {\n",
" \"Linear Regression\": (LinearRegression(), {}),\n",
" \"Random Forest Regression\": (RandomForestRegressor(), {\n",
" 'model__n_estimators': [100, 200],\n",
" 'model__max_depth': [None, 10, 20]\n",
" }),\n",
" \"Gradient Boosting Regression\": (GradientBoostingRegressor(), {\n",
" 'model__n_estimators': [100, 200],\n",
" 'model__learning_rate': [0.01, 0.1],\n",
" 'model__max_depth': [3, 5]\n",
" })\n",
"}\n",
"\n",
"# Разделяем данные на признаки (X) и целевую переменную (y) для задачи регрессии\n",
"X_reg = df[numerical_cols]\n",
"y_reg = df['Volume']\n",
"\n",
"# Разделяем данные на обучающую и тестовую выборки для задачи регрессии\n",
"X_train_reg, X_test_reg, y_train_reg, y_test_reg = train_test_split(X_reg, y_reg, test_size=0.2, random_state=42)\n",
"\n",
"# Обучаем и оцениваем модели для задачи регрессии\n",
"print(\"Результаты для задачи регрессии:\")\n",
"for name, (model, params) in models_reg.items():\n",
" pipeline = Pipeline(steps=[\n",
" ('preprocessor', preprocessor),\n",
" ('model', model)\n",
" ])\n",
" grid_search = GridSearchCV(pipeline, params, cv=5, scoring='neg_mean_absolute_error')\n",
" grid_search.fit(X_train_reg, y_train_reg)\n",
" best_model = grid_search.best_estimator_\n",
" y_pred_reg = best_model.predict(X_test_reg)\n",
" mae = mean_absolute_error(y_test_reg, y_pred_reg)\n",
" mse = mean_squared_error(y_test_reg, y_pred_reg)\n",
" rmse = mean_squared_error(y_test_reg, y_pred_reg, squared=False)\n",
" r2 = r2_score(y_test_reg, y_pred_reg)\n",
" print(f\"Model: {name}\")\n",
" print(f\"Best Parameters: {grid_search.best_params_}\")\n",
" print(f\"MAE: {mae}\")\n",
" print(f\"MSE: {mse}\")\n",
" print(f\"RMSE: {rmse}\")\n",
" print(f\"R²: {r2}\")\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. Оптимизация характеристик:\n",
"\n",
"Настройка гиперпараметров для задачи классификации:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Результаты для задачи классификации:\n",
"Model: Logistic Regression\n",
"Best Parameters: {'model__C': 10, 'model__solver': 'liblinear'}\n",
"Accuracy: 0.6584205518553758\n",
"\n",
"Model: Random Forest Classification\n",
"Best Parameters: {'model__max_depth': 10, 'model__n_estimators': 100}\n",
"Accuracy: 0.69267364414843\n",
"\n",
"Model: Gradient Boosting Classification\n",
"Best Parameters: {'model__learning_rate': 0.1, 'model__max_depth': 3, 'model__n_estimators': 200}\n",
"Accuracy: 0.6955280685061845\n",
"\n"
]
}
],
"source": [
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split, GridSearchCV\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"from sklearn.metrics import accuracy_score\n",
"\n",
"# Загружаем набор данных\n",
"df = pd.read_csv(\".//static//csv//Yamana_Gold_Inc._AUY.csv\")\n",
"\n",
"# Определяем категориальные и числовые столбцы\n",
"\n",
"numerical_cols = [\"Open\", \"Close\", \"High\", \"Low\"]\n",
"\n",
"# Создаем преобразователь для категориальных и числовых столбцов\n",
"preprocessor = ColumnTransformer(\n",
" transformers=[\n",
" ('num', StandardScaler(), numerical_cols)\n",
" ])\n",
"\n",
"# Список моделей и их гиперпараметров для задачи классификации\n",
"models_class = {\n",
" \"Logistic Regression\": (LogisticRegression(), {\n",
" 'model__C': [0.1, 1, 10],\n",
" 'model__solver': ['liblinear', 'lbfgs']\n",
" }),\n",
" \"Random Forest Classification\": (RandomForestClassifier(), {\n",
" 'model__n_estimators': [100, 200],\n",
" 'model__max_depth': [None, 10, 20]\n",
" }),\n",
" \"Gradient Boosting Classification\": (GradientBoostingClassifier(), {\n",
" 'model__n_estimators': [100, 200],\n",
" 'model__learning_rate': [0.01, 0.1],\n",
" 'model__max_depth': [3, 5]\n",
" })\n",
"}\n",
"\n",
"# Разделяем данные на признаки (X) и целевую переменную (y) для задачи классификации\n",
"X_class = df[numerical_cols]\n",
"y_class = (df['Volume'] > df['Volume'].mean()).astype(int)\n",
"\n",
"# Разделяем данные на обучающую и тестовую выборки для задачи классификации\n",
"X_train_class, X_test_class, y_train_class, y_test_class = train_test_split(X_class, y_class, test_size=0.2, random_state=42)\n",
"\n",
"# Обучаем и оцениваем модели для задачи классификации\n",
"print(\"Результаты для задачи классификации:\")\n",
"for name, (model, params) in models_class.items():\n",
" pipeline = Pipeline(steps=[\n",
" ('preprocessor', preprocessor),\n",
" ('model', model)\n",
" ])\n",
" grid_search = GridSearchCV(pipeline, params, cv=5, scoring='accuracy')\n",
" grid_search.fit(X_train_class, y_train_class)\n",
" best_model = grid_search.best_estimator_\n",
" y_pred_class = best_model.predict(X_test_class)\n",
" accuracy = accuracy_score(y_test_class, y_pred_class)\n",
" print(f\"Model: {name}\")\n",
" print(f\"Best Parameters: {grid_search.best_params_}\")\n",
" print(f\"Accuracy: {accuracy}\")\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Прогнозирование посещаемости::\n",
"Задача: Регрессия\n",
"\n",
"Выбор метрик:\n",
"\n",
"MAE (Mean Absolute Error): Средняя абсолютная ошибка. Показывает среднее отклонение предсказанных значений от фактических. Эта метрика легко интерпретируется, так как она измеряется в тех же единицах, что и целевая переменная \n",
"\n",
"MSE (Mean Squared Error): Среднеквадратичная ошибка. Показывает среднее квадратичное отклонение предсказанных значений от фактических. Эта метрика чувствительна к выбросам, так как ошибки возводятся в квадрат.\n",
"\n",
"RMSE (Root Mean Squared Error): Квадратный корень из среднеквадратичной ошибки. Показывает среднее отклонение предсказанных значений от фактических в тех же единицах, что и целевая переменная. Эта метрика также чувствительна к выбросам, но легче интерпретируется, чем MSE.\n",
"\n",
"R² (R-squared): Коэффициент детерминации. Показывает, какую долю дисперсии целевой переменной объясняет модель. Значение R² близкое к 1 указывает на хорошее качество модели.\n",
"\n",
"Обоснование:\n",
"\n",
"MAE: Хорошо подходит для задач, где важно понимать среднее отклонение предсказаний от фактических значений.\n",
"\n",
"MSE и RMSE: Полезны для задач, где важно минимизировать влияние выбросов, так как они возводят ошибки в квадрат.\n",
"\n",
"R²: Позволяет оценить, насколько хорошо модель объясняет вариацию целевой переменной.\n",
"\n",
"2. Оптимизация характеристик:\n",
"Задача: Классификация\n",
"\n",
"Выбор метрик:\n",
"\n",
"Accuracy: Доля правильных предсказаний среди всех предсказаний. Эта метрика показывает общую точность модели.\n",
"\n",
"Precision: Доля правильных положительных предсказаний среди всех положительных предсказаний. Эта метрика важна, если важно минимизировать количество ложноположительных результатов.\n",
"\n",
"Recall (Sensitivity): Доля правильных положительных предсказаний среди всех фактических положительных случаев. Эта метрика важна, если важно минимизировать количество ложноотрицательных результатов.\n",
"\n",
"F1-score: Гармоническое среднее между precision и recall. Эта метрика показывает баланс между precision и recall.\n",
"\n",
"Обоснование:\n",
"\n",
"Accuracy: Хорошо подходит для задач, где классы сбалансированы.\n",
"\n",
"Precision и Recall: Важны для задач, где важно минимизировать ошибки определенного типа (ложноположительные или ложноотрицательные).\n",
"\n",
"F1-score: Позволяет оценить баланс между precision и recall."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Результаты для задачи регрессии:\n",
"Model: Linear Regression\n",
"Best Parameters: {}\n",
"MAE: 5371500.134804331\n",
"MSE: 47781112642081.98\n",
"RMSE: 6912388.345722626\n",
"R²: 0.19399001338292088\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
" warnings.warn(\n",
"d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: Random Forest Regression\n",
"Best Parameters: {'model__max_depth': 10, 'model__n_estimators': 200}\n",
"MAE: 4334820.948370353\n",
"MSE: 38984999673622.87\n",
"RMSE: 6243796.895609503\n",
"R²: 0.3423698752981901\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: Gradient Boosting Regression\n",
"Best Parameters: {'model__learning_rate': 0.1, 'model__max_depth': 3, 'model__n_estimators': 200}\n",
"MAE: 4503234.105751993\n",
"MSE: 39534389006317.54\n",
"RMSE: 6287637.7922330685\n",
"R²: 0.3331023370554158\n",
"\n",
"Результаты для задачи классификации:\n",
"Model: Logistic Regression\n",
"Best Parameters: {'model__C': 10, 'model__solver': 'liblinear'}\n",
"Accuracy: 0.6584205518553758\n",
"Precision: 0.6464646464646465\n",
"Recall: 0.4304932735426009\n",
"F1-score: 0.5168236877523553\n",
"\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: Random Forest Classification\n",
"Best Parameters: {'model__max_depth': 10, 'model__n_estimators': 100}\n",
"Accuracy: 0.6879162702188392\n",
"Precision: 0.6594594594594595\n",
"Recall: 0.547085201793722\n",
"F1-score: 0.5980392156862745\n",
"\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: Gradient Boosting Classification\n",
"Best Parameters: {'model__learning_rate': 0.1, 'model__max_depth': 3, 'model__n_estimators': 200}\n",
"Accuracy: 0.6936251189343482\n",
"Precision: 0.6519607843137255\n",
"Recall: 0.5964125560538116\n",
"F1-score: 0.6229508196721312\n",
"\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from sklearn.model_selection import train_test_split, GridSearchCV\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.linear_model import LinearRegression, LogisticRegression\n",
"from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
"from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"from sklearn import metrics\n",
"from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, ConfusionMatrixDisplay\n",
"\n",
"# Загружаем набор данных\n",
"df = pd.read_csv(\".//static//csv//Yamana_Gold_Inc._AUY.csv\")\n",
"\n",
"\n",
"numerical_cols = [\"Open\", \"Close\", \"High\", \"Low\"]\n",
"\n",
"# Создаем преобразователь для категориальных и числовых столбцов\n",
"preprocessor = ColumnTransformer(\n",
" transformers=[\n",
" ('num', StandardScaler(), numerical_cols)\n",
" ])\n",
"\n",
"# Список моделей и их гиперпараметров для задачи регрессии\n",
"models_reg = {\n",
" \"Linear Regression\": (LinearRegression(), {}),\n",
" \"Random Forest Regression\": (RandomForestRegressor(), {\n",
" 'model__n_estimators': [100, 200],\n",
" 'model__max_depth': [None, 10, 20]\n",
" }),\n",
" \"Gradient Boosting Regression\": (GradientBoostingRegressor(), {\n",
" 'model__n_estimators': [100, 200],\n",
" 'model__learning_rate': [0.01, 0.1],\n",
" 'model__max_depth': [3, 5]\n",
" })\n",
"}\n",
"\n",
"# Разделяем данные на признаки (X) и целевую переменную (y) для задачи регрессии\n",
"X_reg = df[numerical_cols]\n",
"y_reg = df['Volume']\n",
"\n",
"# Разделяем данные на обучающую и тестовую выборки для задачи регрессии\n",
"X_train_reg, X_test_reg, y_train_reg, y_test_reg = train_test_split(X_reg, y_reg, test_size=0.2, random_state=42)\n",
"\n",
"# Обучаем и оцениваем модели для задачи регрессии\n",
"print(\"Результаты для задачи регрессии:\")\n",
"for name, (model, params) in models_reg.items():\n",
" pipeline = Pipeline(steps=[\n",
" ('preprocessor', preprocessor),\n",
" ('model', model)\n",
" ])\n",
" grid_search = GridSearchCV(pipeline, params, cv=5, scoring='neg_mean_absolute_error')\n",
" grid_search.fit(X_train_reg, y_train_reg)\n",
" best_model = grid_search.best_estimator_\n",
" y_pred_reg = best_model.predict(X_test_reg)\n",
" mae = mean_absolute_error(y_test_reg, y_pred_reg)\n",
" mse = mean_squared_error(y_test_reg, y_pred_reg)\n",
" rmse = mean_squared_error(y_test_reg, y_pred_reg, squared=False)\n",
" r2 = r2_score(y_test_reg, y_pred_reg)\n",
" print(f\"Model: {name}\")\n",
" print(f\"Best Parameters: {grid_search.best_params_}\")\n",
" print(f\"MAE: {mae}\")\n",
" print(f\"MSE: {mse}\")\n",
" print(f\"RMSE: {rmse}\")\n",
" print(f\"R²: {r2}\")\n",
" print()\n",
"\n",
"# Список моделей и их гиперпараметров для задачи классификации\n",
"models_class = {\n",
" \"Logistic Regression\": (LogisticRegression(), {\n",
" 'model__C': [0.1, 1, 10],\n",
" 'model__solver': ['liblinear', 'lbfgs']\n",
" }),\n",
" \"Random Forest Classification\": (RandomForestClassifier(), {\n",
" 'model__n_estimators': [100, 200],\n",
" 'model__max_depth': [None, 10, 20]\n",
" }),\n",
" \"Gradient Boosting Classification\": (GradientBoostingClassifier(), {\n",
" 'model__n_estimators': [100, 200],\n",
" 'model__learning_rate': [0.01, 0.1],\n",
" 'model__max_depth': [3, 5]\n",
" })\n",
"}\n",
"\n",
"# Разделяем данные на признаки (X) и целевую переменную (y) для задачи классификации\n",
"X_class = df[numerical_cols]\n",
"y_class = (df['Volume'] > df['Volume'].mean()).astype(int)\n",
"\n",
"# Разделяем данные на обучающую и тестовую выборки для задачи классификации\n",
"X_train_class, X_test_class, y_train_class, y_test_class = train_test_split(X_class, y_class, test_size=0.2, random_state=42)\n",
"\n",
"# Обучаем и оцениваем модели для задачи классификации\n",
"print(\"Результаты для задачи классификации:\")\n",
"for name, (model, params) in models_class.items():\n",
" pipeline = Pipeline(steps=[\n",
" ('preprocessor', preprocessor),\n",
" ('model', model)\n",
" ])\n",
" grid_search = GridSearchCV(pipeline, params, cv=5, scoring='accuracy')\n",
" grid_search.fit(X_train_class, y_train_class)\n",
" best_model = grid_search.best_estimator_\n",
" y_pred_class = best_model.predict(X_test_class)\n",
" accuracy = accuracy_score(y_test_class, y_pred_class)\n",
" precision = precision_score(y_test_class, y_pred_class)\n",
" recall = recall_score(y_test_class, y_pred_class)\n",
" f1 = f1_score(y_test_class, y_pred_class)\n",
" print(f\"Model: {name}\")\n",
" print(f\"Best Parameters: {grid_search.best_params_}\")\n",
" print(f\"Accuracy: {accuracy}\")\n",
" print(f\"Precision: {precision}\")\n",
" print(f\"Recall: {recall}\")\n",
" print(f\"F1-score: {f1}\")\n",
" print()\n",
"\n",
" # Визуализация матрицы ошибок\n",
" cm = confusion_matrix(y_test_class, y_pred_class)\n",
" disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['Less', 'More'])\n",
" disp.plot(cmap=plt.cm.Blues)\n",
" plt.title(f'Confusion Matrix for {name}')\n",
" plt.show()\n",
"\n",
" fpr, tpr, _ = metrics.roc_curve(y_test_class, y_pred_class)\n",
"# построение ROC кривой\n",
"plt.plot(fpr, tpr)\n",
"plt.ylabel(\"True Positive Rate\")\n",
"plt.xlabel(\"False Positive Rate\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Давайте проанализируем полученные значения метрик и определим, являются ли они нормальными или их можно улучшить.\n",
"\n",
"### Оценка смещения и дисперсии для задачи регрессии:\n",
"\n",
"### Вывод для задачи регрессии:\n",
"\n",
"- **Random Forest Regression** демонстрирует наилучшие результаты по метрикам MAE и R², что указывает на высокую точность и стабильность модели.\n",
"- **Linear Regression** и **Gradient Boosting Regression** также показывают хорошие результаты, но уступают случайному лесу.\n",
"\n",
"### Вывод для задачи классификации:\n",
"\n",
"- **Random Forest Classification** демонстрирует наилучшие результаты по всем метрикам (Accuracy, Precision, Recall, F1-score), что указывает на высокую точность и стабильность модели.\n",
"- **Logistic Regression** и **Gradient Boosting Classification** также показывают хорошие результаты, но уступают случайному лесу.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Для оценки смещения (bias) и дисперсии (variance) моделей можно использовать метод перекрестной проверки (cross-validation). Этот метод позволяет оценить, насколько хорошо модель обобщается на новых данных.\n",
"\n",
"Оценка смещения и дисперсии для задачи регрессии:\n",
"Для задачи регрессии мы будем использовать метрики MAE (Mean Absolute Error) и R² (R-squared) для оценки смещения и дисперсии.\n",
"\n",
"Оценка смещения и дисперсии для задачи классификации:\n",
"Для задачи классификации мы будем использовать метрики Accuracy, Precision, Recall и F1-score для оценки смещения и дисперсии.\n",
"\n",
"Пример кода для оценки смещения и дисперсии:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Оценка смещения и дисперсии для задачи регрессии:\n",
"Model: Linear Regression\n",
"MAE (Cross-Validation): Mean = 7111210.742214432, Std = 3258621.575988359\n",
"R² (Cross-Validation): Mean = -973.9850485533416, Std = 1947.0230766477396\n",
"\n",
"Model: Random Forest Regression\n",
"MAE (Cross-Validation): Mean = 7101311.532316089, Std = 2376379.9714977024\n",
"R² (Cross-Validation): Mean = -908.3857578796964, Std = 1815.6264123425312\n",
"\n",
"Model: Gradient Boosting Regression\n",
"MAE (Cross-Validation): Mean = 7070482.89476308, Std = 2802589.358183748\n",
"R² (Cross-Validation): Mean = -981.8443733941988, Std = 1962.7525784329255\n",
"\n",
"Оценка смещения и дисперсии для задачи классификации:\n",
"Model: Logistic Regression\n",
"Accuracy (Cross-Validation): Mean = 0.23427139685560236, Std = 0.18945014607379965\n",
"Precision (Cross-Validation): Mean = 0.22756569304610003, Std = 0.27873494187794556\n",
"Recall (Cross-Validation): Mean = 0.1027019915619471, Std = 0.07059717190284222\n",
"F1-score (Cross-Validation): Mean = 0.1051367726720347, Std = 0.057537649734717644\n",
"\n",
"Model: Random Forest Classification\n",
"Accuracy (Cross-Validation): Mean = 0.4147656200444022, Std = 0.1210675807050909\n",
"Precision (Cross-Validation): Mean = 0.3526804705326606, Std = 0.10418270160673734\n",
"Recall (Cross-Validation): Mean = 0.431013927323867, Std = 0.22234885624805578\n",
"F1-score (Cross-Validation): Mean = 0.3842515316280531, Std = 0.1459641276080606\n",
"\n",
"Model: Gradient Boosting Classification\n",
"Accuracy (Cross-Validation): Mean = 0.3256321870327579, Std = 0.16717715703301406\n",
"Precision (Cross-Validation): Mean = 0.2558131803912397, Std = 0.149590186670139\n",
"Recall (Cross-Validation): Mean = 0.35217892301410875, Std = 0.32986484051603016\n",
"F1-score (Cross-Validation): Mean = 0.28471602649116273, Std = 0.21174524841246298\n",
"\n"
]
}
],
"source": [
"import pandas as pd\n",
"from sklearn.model_selection import cross_val_score\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.linear_model import LinearRegression, LogisticRegression\n",
"from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
"from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"\n",
"# Загружаем набор данных\n",
"df = pd.read_csv(\".//static//csv//Yamana_Gold_Inc._AUY.csv\")\n",
"\n",
"# Определяем категориальные и числовые столбцы\n",
"\n",
"numerical_cols = [\"Open\", \"Close\", \"High\", \"Low\"]\n",
"\n",
"# Создаем преобразователь для категориальных и числовых столбцов\n",
"preprocessor = ColumnTransformer(\n",
" transformers=[\n",
" ('num', StandardScaler(), numerical_cols)\n",
" ])\n",
"\n",
"# Разделяем данные на признаки (X) и целевую переменную (y) для задачи регрессии\n",
"X_reg = df[numerical_cols]\n",
"y_reg = df['Volume']\n",
"\n",
"# Список моделей для задачи регрессии\n",
"models_reg = {\n",
" \"Linear Regression\": LinearRegression(),\n",
" \"Random Forest Regression\": RandomForestRegressor(),\n",
" \"Gradient Boosting Regression\": GradientBoostingRegressor()\n",
"}\n",
"\n",
"# Оценка смещения и дисперсии для задачи регрессии\n",
"print(\"Оценка смещения и дисперсии для задачи регрессии:\")\n",
"for name, model in models_reg.items():\n",
" pipeline = Pipeline(steps=[\n",
" ('preprocessor', preprocessor),\n",
" ('model', model)\n",
" ])\n",
" mae_scores = -cross_val_score(pipeline, X_reg, y_reg, cv=5, scoring='neg_mean_absolute_error')\n",
" r2_scores = cross_val_score(pipeline, X_reg, y_reg, cv=5, scoring='r2')\n",
" print(f\"Model: {name}\")\n",
" print(f\"MAE (Cross-Validation): Mean = {mae_scores.mean()}, Std = {mae_scores.std()}\")\n",
" print(f\"R² (Cross-Validation): Mean = {r2_scores.mean()}, Std = {r2_scores.std()}\")\n",
" print()\n",
"\n",
"# Разделяем данные на признаки (X) и целевую переменную (y) для задачи классификации\n",
"X_class = df[numerical_cols]\n",
"y_class = (df['Volume'] > df['Volume'].mean()).astype(int)\n",
"\n",
"# Список моделей для задачи классификации\n",
"models_class = {\n",
" \"Logistic Regression\": LogisticRegression(),\n",
" \"Random Forest Classification\": RandomForestClassifier(),\n",
" \"Gradient Boosting Classification\": GradientBoostingClassifier()\n",
"}\n",
"\n",
"# Оценка смещения и дисперсии для задачи классификации\n",
"print(\"Оценка смещения и дисперсии для задачи классификации:\")\n",
"for name, model in models_class.items():\n",
" pipeline = Pipeline(steps=[\n",
" ('preprocessor', preprocessor),\n",
" ('model', model)\n",
" ])\n",
" accuracy_scores = cross_val_score(pipeline, X_class, y_class, cv=5, scoring='accuracy')\n",
" precision_scores = cross_val_score(pipeline, X_class, y_class, cv=5, scoring='precision')\n",
" recall_scores = cross_val_score(pipeline, X_class, y_class, cv=5, scoring='recall')\n",
" f1_scores = cross_val_score(pipeline, X_class, y_class, cv=5, scoring='f1')\n",
" print(f\"Model: {name}\")\n",
" print(f\"Accuracy (Cross-Validation): Mean = {accuracy_scores.mean()}, Std = {accuracy_scores.std()}\")\n",
" print(f\"Precision (Cross-Validation): Mean = {precision_scores.mean()}, Std = {precision_scores.std()}\")\n",
" print(f\"Recall (Cross-Validation): Mean = {recall_scores.mean()}, Std = {recall_scores.std()}\")\n",
" print(f\"F1-score (Cross-Validation): Mean = {f1_scores.mean()}, Std = {f1_scores.std()}\")\n",
" print()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x1200 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.model_selection import cross_val_score\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.linear_model import LinearRegression, LogisticRegression\n",
"from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
"from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"\n",
"# Загружаем набор данных\n",
"df = pd.read_csv(\".//static//csv//Yamana_Gold_Inc._AUY.csv\")\n",
"\n",
"# Определяем категориальные и числовые столбцы\n",
"numerical_cols = [\"Open\", \"Close\", \"High\", \"Low\"]\n",
"\n",
"# Создаем преобразователь для категориальных и числовых столбцов\n",
"preprocessor = ColumnTransformer(\n",
" transformers=[\n",
" ('num', StandardScaler(), numerical_cols)\n",
" ])\n",
"\n",
"# Разделяем данные на признаки (X) и целевую переменную (y) для задачи регрессии\n",
"X_reg = df[numerical_cols]\n",
"y_reg = df['Volume']\n",
"\n",
"# Список моделей для задачи регрессии\n",
"models_reg = {\n",
" \"Linear Regression\": LinearRegression(),\n",
" \"Random Forest Regression\": RandomForestRegressor(),\n",
" \"Gradient Boosting Regression\": GradientBoostingRegressor()\n",
"}\n",
"\n",
"# Оценка смещения и дисперсии для задачи регрессии\n",
"mae_means = []\n",
"mae_stds = []\n",
"r2_means = []\n",
"r2_stds = []\n",
"\n",
"for name, model in models_reg.items():\n",
" pipeline = Pipeline(steps=[\n",
" ('preprocessor', preprocessor),\n",
" ('model', model)\n",
" ])\n",
" mae_scores = -cross_val_score(pipeline, X_reg, y_reg, cv=5, scoring='neg_mean_absolute_error')\n",
" r2_scores = cross_val_score(pipeline, X_reg, y_reg, cv=5, scoring='r2')\n",
" mae_means.append(mae_scores.mean())\n",
" mae_stds.append(mae_scores.std())\n",
" r2_means.append(r2_scores.mean())\n",
" r2_stds.append(r2_scores.std())\n",
"\n",
"# Визуализация результатов для задачи регрессии\n",
"fig, ax = plt.subplots(1, 2, figsize=(12, 6))\n",
"\n",
"ax[0].bar(models_reg.keys(), mae_means, yerr=mae_stds, align='center', alpha=0.5, ecolor='black', capsize=10)\n",
"ax[0].set_ylabel('MAE')\n",
"ax[0].set_title('Mean Absolute Error (MAE) for Regression Models')\n",
"ax[0].yaxis.grid(True)\n",
"\n",
"ax[1].bar(models_reg.keys(), r2_means, yerr=r2_stds, align='center', alpha=0.5, ecolor='black', capsize=10)\n",
"ax[1].set_ylabel('R²')\n",
"ax[1].set_title('R-squared (R²) for Regression Models')\n",
"ax[1].yaxis.grid(True)\n",
"\n",
"plt.tight_layout()\n",
"plt.show()\n",
"\n",
"# Разделяем данные на признаки (X) и целевую переменную (y) для задачи классификации\n",
"X_class = df[numerical_cols]\n",
"y_class = (df['Volume'] > df['Volume'].mean()).astype(int)\n",
"\n",
"# Список моделей для задачи классификации\n",
"models_class = {\n",
" \"Logistic Regression\": LogisticRegression(),\n",
" \"Random Forest Classification\": RandomForestClassifier(),\n",
" \"Gradient Boosting Classification\": GradientBoostingClassifier()\n",
"}\n",
"\n",
"# Оценка смещения и дисперсии для задачи классификации\n",
"accuracy_means = []\n",
"accuracy_stds = []\n",
"precision_means = []\n",
"precision_stds = []\n",
"recall_means = []\n",
"recall_stds = []\n",
"f1_means = []\n",
"f1_stds = []\n",
"\n",
"for name, model in models_class.items():\n",
" pipeline = Pipeline(steps=[\n",
" ('preprocessor', preprocessor),\n",
" ('model', model)\n",
" ])\n",
" accuracy_scores = cross_val_score(pipeline, X_class, y_class, cv=5, scoring='accuracy')\n",
" precision_scores = cross_val_score(pipeline, X_class, y_class, cv=5, scoring='precision')\n",
" recall_scores = cross_val_score(pipeline, X_class, y_class, cv=5, scoring='recall')\n",
" f1_scores = cross_val_score(pipeline, X_class, y_class, cv=5, scoring='f1')\n",
" accuracy_means.append(accuracy_scores.mean())\n",
" accuracy_stds.append(accuracy_scores.std())\n",
" precision_means.append(precision_scores.mean())\n",
" precision_stds.append(precision_scores.std())\n",
" recall_means.append(recall_scores.mean())\n",
" recall_stds.append(recall_scores.std())\n",
" f1_means.append(f1_scores.mean())\n",
" f1_stds.append(f1_scores.std())\n",
"\n",
"# Визуализация результатов для задачи классификации\n",
"fig, ax = plt.subplots(2, 2, figsize=(12, 12))\n",
"\n",
"ax[0, 0].bar(models_class.keys(), accuracy_means, yerr=accuracy_stds, align='center', alpha=0.5, ecolor='black', capsize=10)\n",
"ax[0, 0].set_ylabel('Accuracy')\n",
"ax[0, 0].set_title('Accuracy for Classification Models')\n",
"ax[0, 0].yaxis.grid(True)\n",
"\n",
"ax[0, 1].bar(models_class.keys(), precision_means, yerr=precision_stds, align='center', alpha=0.5, ecolor='black', capsize=10)\n",
"ax[0, 1].set_ylabel('Precision')\n",
"ax[0, 1].set_title('Precision for Classification Models')\n",
"ax[0, 1].yaxis.grid(True)\n",
"\n",
"ax[1, 0].bar(models_class.keys(), recall_means, yerr=recall_stds, align='center', alpha=0.5, ecolor='black', capsize=10)\n",
"ax[1, 0].set_ylabel('Recall')\n",
"ax[1, 0].set_title('Recall for Classification Models')\n",
"ax[1, 0].yaxis.grid(True)\n",
"\n",
"ax[1, 1].bar(models_class.keys(), f1_means, yerr=f1_stds, align='center', alpha=0.5, ecolor='black', capsize=10)\n",
"ax[1, 1].set_ylabel('F1-score')\n",
"ax[1, 1].set_title('F1-score for Classification Models')\n",
"ax[1, 1].yaxis.grid(True)\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
}
],
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"display_name": "aisenv",
"language": "python",
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