diff --git a/lab3/lab3 copy.ipynb b/lab3/lab3 copy.ipynb new file mode 100644 index 0000000..0652cf7 --- /dev/null +++ b/lab3/lab3 copy.ipynb @@ -0,0 +1,841 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')\n", + "\n", + "RangeIndex: 5251 entries, 0 to 5250\n", + "Data columns (total 8 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Date 5251 non-null object \n", + " 1 Open 5251 non-null float64 \n", + " 2 High 5251 non-null float64 \n", + " 3 Low 5251 non-null float64 \n", + " 4 Close 5251 non-null float64 \n", + " 5 Adj Close 5251 non-null float64 \n", + " 6 Volume 5251 non-null int64 \n", + " 7 date 5251 non-null datetime64[ns]\n", + "dtypes: datetime64[ns](1), float64(5), int64(1), object(1)\n", + "memory usage: 328.3+ KB\n" + ] + } + ], + "source": [ + "import pandas as pn\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "import matplotlib.ticker as ticker\n", + "from datetime import datetime\n", + "import matplotlib.dates as md\n", + "\n", + "df = pn.read_csv(\".//static//csv//Yamana_Gold_Inc._AUY.csv\")\n", + "print(df.columns)\n", + "\n", + "df[\"date\"] = df.apply(lambda row: datetime.strptime(row[\"Date\"], \"%m/%d/%Y\"), axis=1)\n", + "df.info()\n", + "#print(df['date'].head)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Разделим на 3 выборки\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Размер обучающей выборки: 3360\n", + "Размер контрольной выборки: 840\n", + "Размер тестовой выборки: 1051\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Разделение данных на обучающую и тестовую выборки (80% - обучение, 20% - тест)\n", + "train_data, test_data = train_test_split(df, test_size=0.2, random_state=42)\n", + "\n", + "# Разделение обучающей выборки на обучающую и контрольную (80% - обучение, 20% - контроль)\n", + "train_data, val_data = train_test_split(train_data, test_size=0.2, random_state=42)\n", + "\n", + "print(\"Размер обучающей выборки:\", len(train_data))\n", + "print(\"Размер контрольной выборки:\", len(val_data))\n", + "print(\"Размер тестовой выборки:\", len(test_data))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Гистограмма распределения объема в обучающей выборке\n", + "sns.histplot(train_data[\"Volume\"], kde=True)\n", + "plt.title('Распределение цены в обучающей выборке')\n", + "plt.show()\n", + "\n", + "# Гистограмма распределения объема в контрольной выборке\n", + "sns.histplot(val_data[\"Volume\"], kde=True)\n", + "plt.title('Распределение цены в контрольной выборке')\n", + "plt.show()\n", + "\n", + "# Гистограмма распределения объема в тестовой выборке\n", + "sns.histplot(test_data[\"Volume\"], kde=True)\n", + "plt.title('Распределение цены в тестовой выборке')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Процесс конструирования признаков\n", + "\n", + "\n", + "\n", + "### Унитарное кодирование категориальных признаков (one-hot encoding)\n", + "\n", + "One-hot encoding: Преобразование категориальных признаков в бинарные векторы." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "# Пример категориальных признаков\n", + "categorical_features = [\n", + " \"Date\",\n", + " \"date\"\n", + "]\n", + "\n", + "# Применение one-hot encoding\n", + "train_data_encoded = pd.get_dummies(train_data, columns=categorical_features)\n", + "val_data_encoded = pd.get_dummies(val_data, columns=categorical_features)\n", + "test_data_encoded = pd.get_dummies(test_data, columns=categorical_features)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Дискретизация числовых признаков " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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193.142857low hight price
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" + ], + "text/plain": [ + " High High\n", + "0 3.428571 low hight price\n", + "1 3.428571 low hight price\n", + "2 3.714286 low hight price\n", + "3 3.714286 low hight price\n", + "4 3.714286 low hight price\n", + "5 3.714286 low hight price\n", + "6 3.714286 low hight price\n", + "7 3.714286 low hight price\n", + "8 3.428571 low hight price\n", + "9 3.428571 low hight price\n", + "10 3.428571 low hight price\n", + "11 3.428571 low hight price\n", + "12 3.428571 low hight price\n", + "13 2.857143 low hight price\n", + "14 3.142857 low hight price\n", + "15 3.142857 low hight price\n", + "16 3.428571 low hight price\n", + "17 3.714286 low hight price\n", + "18 3.142857 low hight price\n", + "19 3.142857 low hight price" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.concat(\n", + " [df[\"High\"], pd.cut(df[\"High\"], list(bins1), labels=labels)], axis=1\n", + ").head(20)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ручной синтез" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Пример синтеза признака среднего значения в максимальной и минимальной цене\n", + "train_data_encoded[\"medium\"] = train_data_encoded[\"High\"] / train_data_encoded[\"Low\"]\n", + "val_data_encoded[\"medium\"] = val_data_encoded[\"High\"] / val_data_encoded[\"Low\"]\n", + "test_data_encoded[\"medium\"] = test_data_encoded[\"High\"] / test_data_encoded[\"Low\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n", + "\n", + "# Пример масштабирования числовых признаков\n", + "numerical_features = [\"Open\", \"Close\"]\n", + "\n", + "scaler = StandardScaler()\n", + "train_data_encoded[numerical_features] = scaler.fit_transform(train_data_encoded[numerical_features])\n", + "val_data_encoded[numerical_features] = scaler.transform(val_data_encoded[numerical_features])\n", + "test_data_encoded[numerical_features] = scaler.transform(test_data_encoded[numerical_features])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Конструирование признаков с применением фреймворка Featuretools" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\featuretools\\entityset\\entityset.py:1733: UserWarning: index id not found in dataframe, creating new integer column\n", + " warnings.warn(\n", + "d:\\3_КУРС_ПИ\\МИИ\\aisenv\\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", + "d:\\3_КУРС_ПИ\\МИИ\\aisenv\\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", + "d:\\3_КУРС_ПИ\\МИИ\\aisenv\\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", + "d:\\3_КУРС_ПИ\\МИИ\\aisenv\\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", + "d:\\3_КУРС_ПИ\\МИИ\\aisenv\\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" + ] + } + ], + "source": [ + "import featuretools as ft\n", + "\n", + "# Определение сущностей\n", + "es = ft.EntitySet(id='gold_data')\n", + "\n", + "es = es.add_dataframe(dataframe_name='yamana', dataframe=train_data_encoded, index='id')\n", + "\n", + "\n", + "# Генерация признаков\n", + "feature_matrix, feature_defs = ft.dfs(\n", + " entityset=es, target_dataframe_name=\"yamana\", max_depth=2\n", + ")\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_data_encoded.index)\n", + "test_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=test_data_encoded.index)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Оценка качества каждого набора признаков\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": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\featuretools\\entityset\\entityset.py:724: UserWarning: A Woodwork-initialized DataFrame was provided, so the following parameters were ignored: index\n", + " warnings.warn(\n", + "d:\\3_КУРС_ПИ\\МИИ\\aisenv\\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", + "d:\\3_КУРС_ПИ\\МИИ\\aisenv\\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", + "d:\\3_КУРС_ПИ\\МИИ\\aisenv\\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", + "d:\\3_КУРС_ПИ\\МИИ\\aisenv\\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", + "d:\\3_КУРС_ПИ\\МИИ\\aisenv\\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" + ] + } + ], + "source": [ + "import featuretools as ft\n", + "\n", + "# Определение сущностей\n", + "es = ft.EntitySet(id='gold_data')\n", + "es = es.add_dataframe(\n", + " dataframe_name=\"yamana\", dataframe=train_data_encoded, index=\"id\"\n", + ")\n", + "\n", + "# Генерация признаков\n", + "feature_matrix, feature_defs = ft.dfs(\n", + " entityset=es, target_dataframe_name=\"yamana\", max_depth=2\n", + ")\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_data_encoded.index)\n", + "test_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=test_data_encoded.index)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "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": [ + "RMSE: 1764152.3991770656\n", + "R²: 0.942082609353535\n", + "MAE: 1161195.586464497\n", + "Cross-validated RMSE: 4835663.342127571\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": [ + "Train RMSE: 1789275.1500045008\n", + "Train R²: 0.944270691246794\n", + "Train MAE: 1134044.475345238\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n", + "from sklearn.model_selection import cross_val_score\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\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(\"Volume\", axis=1)\n", + "y_train = feature_matrix[\"Volume\"]\n", + "X_val = val_feature_matrix.drop(\"Volume\", axis=1)\n", + "y_val = val_feature_matrix[\"Volume\"]\n", + "X_test = test_feature_matrix.drop(\"Volume\", axis=1)\n", + "y_test = test_feature_matrix[\"Volume\"]\n", + "\n", + "# Выбор модели\n", + "model = RandomForestRegressor(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", + "rmse = mean_squared_error(y_test, y_pred, squared=False)\n", + "r2 = r2_score(y_test, y_pred)\n", + "mae = mean_absolute_error(y_test, y_pred)\n", + "\n", + "print(f\"RMSE: {rmse}\")\n", + "print(f\"R²: {r2}\")\n", + "print(f\"MAE: {mae}\")\n", + "\n", + "# Кросс-валидация\n", + "scores = cross_val_score(model, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n", + "rmse_cv = (-scores.mean())**0.5\n", + "print(f\"Cross-validated RMSE: {rmse_cv}\")\n", + "\n", + "# Анализ важности признаков\n", + "feature_importances = model.feature_importances_\n", + "feature_names = X_train.columns\n", + "\n", + "\n", + "# Проверка на переобучение\n", + "y_train_pred = model.predict(X_train)\n", + "\n", + "rmse_train = mean_squared_error(y_train, y_train_pred, squared=False)\n", + "r2_train = r2_score(y_train, y_train_pred)\n", + "mae_train = mean_absolute_error(y_train, y_train_pred)\n", + "\n", + "print(f\"Train RMSE: {rmse_train}\")\n", + "print(f\"Train R²: {r2_train}\")\n", + "print(f\"Train MAE: {mae_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 Volume\")\n", + "plt.ylabel(\"Predicted Volume\")\n", + "plt.title(\"Actual vs Predicted Volume\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Точность предсказаний: Модель показывает довольно высокий R² (0.944), что указывает на хорошее объяснение вариации распродаж. Значения RMSE и MAE довольно низки, что говорит о том, что модель достаточно точно предсказывает цены.\n", + "\n", + "Переобучение: Разница между RMSE на обучающей и тестовой выборках не очень большая, что указывает на то, что переобучение не является критическим. Однако, стоит быть осторожным и продолжать мониторинг этого показателя.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "aisenv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}