{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Загрузка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume',\n",
      "       'SP_open', 'SP_high', 'SP_low', 'SP_close', 'SP_Ajclose', 'SP_volume',\n",
      "       'DJ_open', 'DJ_high', 'DJ_low', 'DJ_close', 'DJ_Ajclose', 'DJ_volume',\n",
      "       'EG_open', 'EG_high', 'EG_low', 'EG_close', 'EG_Ajclose', 'EG_volume',\n",
      "       'EU_Price', 'EU_open', 'EU_high', 'EU_low', 'EU_Trend', 'OF_Price',\n",
      "       'OF_Open', 'OF_High', 'OF_Low', 'OF_Volume', 'OF_Trend', 'OS_Price',\n",
      "       'OS_Open', 'OS_High', 'OS_Low', 'OS_Trend', 'SF_Price', 'SF_Open',\n",
      "       'SF_High', 'SF_Low', 'SF_Volume', 'SF_Trend', 'USB_Price', 'USB_Open',\n",
      "       'USB_High', 'USB_Low', 'USB_Trend', 'PLT_Price', 'PLT_Open', 'PLT_High',\n",
      "       'PLT_Low', 'PLT_Trend', 'PLD_Price', 'PLD_Open', 'PLD_High', 'PLD_Low',\n",
      "       'PLD_Trend', 'RHO_PRICE', 'USDI_Price', 'USDI_Open', 'USDI_High',\n",
      "       'USDI_Low', 'USDI_Volume', 'USDI_Trend', 'GDX_Open', 'GDX_High',\n",
      "       'GDX_Low', 'GDX_Close', 'GDX_Adj Close', 'GDX_Volume', 'USO_Open',\n",
      "       'USO_High', 'USO_Low', 'USO_Close', 'USO_Adj Close', 'USO_Volume'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>SP_open</th>\n",
       "      <th>SP_high</th>\n",
       "      <th>SP_low</th>\n",
       "      <th>...</th>\n",
       "      <th>GDX_Low</th>\n",
       "      <th>GDX_Close</th>\n",
       "      <th>GDX_Adj Close</th>\n",
       "      <th>GDX_Volume</th>\n",
       "      <th>USO_Open</th>\n",
       "      <th>USO_High</th>\n",
       "      <th>USO_Low</th>\n",
       "      <th>USO_Close</th>\n",
       "      <th>USO_Adj Close</th>\n",
       "      <th>USO_Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-12-15</td>\n",
       "      <td>154.740005</td>\n",
       "      <td>154.949997</td>\n",
       "      <td>151.710007</td>\n",
       "      <td>152.330002</td>\n",
       "      <td>152.330002</td>\n",
       "      <td>21521900</td>\n",
       "      <td>123.029999</td>\n",
       "      <td>123.199997</td>\n",
       "      <td>121.989998</td>\n",
       "      <td>...</td>\n",
       "      <td>51.570000</td>\n",
       "      <td>51.680000</td>\n",
       "      <td>48.973877</td>\n",
       "      <td>20605600</td>\n",
       "      <td>36.900002</td>\n",
       "      <td>36.939999</td>\n",
       "      <td>36.049999</td>\n",
       "      <td>36.130001</td>\n",
       "      <td>36.130001</td>\n",
       "      <td>12616700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-12-16</td>\n",
       "      <td>154.309998</td>\n",
       "      <td>155.369995</td>\n",
       "      <td>153.899994</td>\n",
       "      <td>155.229996</td>\n",
       "      <td>155.229996</td>\n",
       "      <td>18124300</td>\n",
       "      <td>122.230003</td>\n",
       "      <td>122.949997</td>\n",
       "      <td>121.300003</td>\n",
       "      <td>...</td>\n",
       "      <td>52.040001</td>\n",
       "      <td>52.680000</td>\n",
       "      <td>49.921513</td>\n",
       "      <td>16285400</td>\n",
       "      <td>36.180000</td>\n",
       "      <td>36.500000</td>\n",
       "      <td>35.730000</td>\n",
       "      <td>36.270000</td>\n",
       "      <td>36.270000</td>\n",
       "      <td>12578800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-12-19</td>\n",
       "      <td>155.479996</td>\n",
       "      <td>155.860001</td>\n",
       "      <td>154.360001</td>\n",
       "      <td>154.869995</td>\n",
       "      <td>154.869995</td>\n",
       "      <td>12547200</td>\n",
       "      <td>122.059998</td>\n",
       "      <td>122.320000</td>\n",
       "      <td>120.029999</td>\n",
       "      <td>...</td>\n",
       "      <td>51.029999</td>\n",
       "      <td>51.169998</td>\n",
       "      <td>48.490578</td>\n",
       "      <td>15120200</td>\n",
       "      <td>36.389999</td>\n",
       "      <td>36.450001</td>\n",
       "      <td>35.930000</td>\n",
       "      <td>36.200001</td>\n",
       "      <td>36.200001</td>\n",
       "      <td>7418200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-12-20</td>\n",
       "      <td>156.820007</td>\n",
       "      <td>157.429993</td>\n",
       "      <td>156.580002</td>\n",
       "      <td>156.979996</td>\n",
       "      <td>156.979996</td>\n",
       "      <td>9136300</td>\n",
       "      <td>122.180000</td>\n",
       "      <td>124.139999</td>\n",
       "      <td>120.370003</td>\n",
       "      <td>...</td>\n",
       "      <td>52.369999</td>\n",
       "      <td>52.990002</td>\n",
       "      <td>50.215282</td>\n",
       "      <td>11644900</td>\n",
       "      <td>37.299999</td>\n",
       "      <td>37.610001</td>\n",
       "      <td>37.220001</td>\n",
       "      <td>37.560001</td>\n",
       "      <td>37.560001</td>\n",
       "      <td>10041600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-12-21</td>\n",
       "      <td>156.979996</td>\n",
       "      <td>157.529999</td>\n",
       "      <td>156.130005</td>\n",
       "      <td>157.160004</td>\n",
       "      <td>157.160004</td>\n",
       "      <td>11996100</td>\n",
       "      <td>123.930000</td>\n",
       "      <td>124.360001</td>\n",
       "      <td>122.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>52.419998</td>\n",
       "      <td>52.959999</td>\n",
       "      <td>50.186852</td>\n",
       "      <td>8724300</td>\n",
       "      <td>37.669998</td>\n",
       "      <td>38.240002</td>\n",
       "      <td>37.520000</td>\n",
       "      <td>38.110001</td>\n",
       "      <td>38.110001</td>\n",
       "      <td>10728000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1713</th>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>119.570000</td>\n",
       "      <td>120.139999</td>\n",
       "      <td>119.570000</td>\n",
       "      <td>120.019997</td>\n",
       "      <td>120.019997</td>\n",
       "      <td>9736400</td>\n",
       "      <td>239.039993</td>\n",
       "      <td>240.839996</td>\n",
       "      <td>234.270004</td>\n",
       "      <td>...</td>\n",
       "      <td>20.650000</td>\n",
       "      <td>21.090000</td>\n",
       "      <td>21.090000</td>\n",
       "      <td>60507000</td>\n",
       "      <td>9.490000</td>\n",
       "      <td>9.520000</td>\n",
       "      <td>9.280000</td>\n",
       "      <td>9.290000</td>\n",
       "      <td>9.290000</td>\n",
       "      <td>21598200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1714</th>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>120.620003</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>119.570000</td>\n",
       "      <td>119.660004</td>\n",
       "      <td>119.660004</td>\n",
       "      <td>14293500</td>\n",
       "      <td>235.970001</td>\n",
       "      <td>246.179993</td>\n",
       "      <td>233.759995</td>\n",
       "      <td>...</td>\n",
       "      <td>20.530001</td>\n",
       "      <td>20.620001</td>\n",
       "      <td>20.620001</td>\n",
       "      <td>76365200</td>\n",
       "      <td>9.250000</td>\n",
       "      <td>9.920000</td>\n",
       "      <td>9.230000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>40978800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1715</th>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>120.570000</td>\n",
       "      <td>120.900002</td>\n",
       "      <td>120.139999</td>\n",
       "      <td>120.570000</td>\n",
       "      <td>120.570000</td>\n",
       "      <td>11874400</td>\n",
       "      <td>242.570007</td>\n",
       "      <td>248.289993</td>\n",
       "      <td>238.960007</td>\n",
       "      <td>...</td>\n",
       "      <td>20.700001</td>\n",
       "      <td>20.969999</td>\n",
       "      <td>20.969999</td>\n",
       "      <td>52393000</td>\n",
       "      <td>9.590000</td>\n",
       "      <td>9.650000</td>\n",
       "      <td>9.370000</td>\n",
       "      <td>9.620000</td>\n",
       "      <td>9.620000</td>\n",
       "      <td>36578700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1716</th>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>120.800003</td>\n",
       "      <td>121.080002</td>\n",
       "      <td>120.720001</td>\n",
       "      <td>121.059998</td>\n",
       "      <td>121.059998</td>\n",
       "      <td>6864700</td>\n",
       "      <td>249.580002</td>\n",
       "      <td>251.399994</td>\n",
       "      <td>246.449997</td>\n",
       "      <td>...</td>\n",
       "      <td>20.570000</td>\n",
       "      <td>20.600000</td>\n",
       "      <td>20.600000</td>\n",
       "      <td>49835000</td>\n",
       "      <td>9.540000</td>\n",
       "      <td>9.650000</td>\n",
       "      <td>9.380000</td>\n",
       "      <td>9.530000</td>\n",
       "      <td>9.530000</td>\n",
       "      <td>22803400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1717</th>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>120.980003</td>\n",
       "      <td>121.260002</td>\n",
       "      <td>120.830002</td>\n",
       "      <td>121.250000</td>\n",
       "      <td>121.250000</td>\n",
       "      <td>8449400</td>\n",
       "      <td>249.559998</td>\n",
       "      <td>250.190002</td>\n",
       "      <td>247.470001</td>\n",
       "      <td>...</td>\n",
       "      <td>20.559999</td>\n",
       "      <td>21.090000</td>\n",
       "      <td>21.090000</td>\n",
       "      <td>53866600</td>\n",
       "      <td>9.630000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>9.440000</td>\n",
       "      <td>9.660000</td>\n",
       "      <td>9.660000</td>\n",
       "      <td>28417400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1718 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date        Open        High         Low       Close   Adj Close  \\\n",
       "0     2011-12-15  154.740005  154.949997  151.710007  152.330002  152.330002   \n",
       "1     2011-12-16  154.309998  155.369995  153.899994  155.229996  155.229996   \n",
       "2     2011-12-19  155.479996  155.860001  154.360001  154.869995  154.869995   \n",
       "3     2011-12-20  156.820007  157.429993  156.580002  156.979996  156.979996   \n",
       "4     2011-12-21  156.979996  157.529999  156.130005  157.160004  157.160004   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
       "1713  2018-12-24  119.570000  120.139999  119.570000  120.019997  120.019997   \n",
       "1714  2018-12-26  120.620003  121.000000  119.570000  119.660004  119.660004   \n",
       "1715  2018-12-27  120.570000  120.900002  120.139999  120.570000  120.570000   \n",
       "1716  2018-12-28  120.800003  121.080002  120.720001  121.059998  121.059998   \n",
       "1717  2018-12-31  120.980003  121.260002  120.830002  121.250000  121.250000   \n",
       "\n",
       "        Volume     SP_open     SP_high      SP_low  ...    GDX_Low  GDX_Close  \\\n",
       "0     21521900  123.029999  123.199997  121.989998  ...  51.570000  51.680000   \n",
       "1     18124300  122.230003  122.949997  121.300003  ...  52.040001  52.680000   \n",
       "2     12547200  122.059998  122.320000  120.029999  ...  51.029999  51.169998   \n",
       "3      9136300  122.180000  124.139999  120.370003  ...  52.369999  52.990002   \n",
       "4     11996100  123.930000  124.360001  122.750000  ...  52.419998  52.959999   \n",
       "...        ...         ...         ...         ...  ...        ...        ...   \n",
       "1713   9736400  239.039993  240.839996  234.270004  ...  20.650000  21.090000   \n",
       "1714  14293500  235.970001  246.179993  233.759995  ...  20.530001  20.620001   \n",
       "1715  11874400  242.570007  248.289993  238.960007  ...  20.700001  20.969999   \n",
       "1716   6864700  249.580002  251.399994  246.449997  ...  20.570000  20.600000   \n",
       "1717   8449400  249.559998  250.190002  247.470001  ...  20.559999  21.090000   \n",
       "\n",
       "      GDX_Adj Close  GDX_Volume   USO_Open   USO_High    USO_Low  USO_Close  \\\n",
       "0         48.973877    20605600  36.900002  36.939999  36.049999  36.130001   \n",
       "1         49.921513    16285400  36.180000  36.500000  35.730000  36.270000   \n",
       "2         48.490578    15120200  36.389999  36.450001  35.930000  36.200001   \n",
       "3         50.215282    11644900  37.299999  37.610001  37.220001  37.560001   \n",
       "4         50.186852     8724300  37.669998  38.240002  37.520000  38.110001   \n",
       "...             ...         ...        ...        ...        ...        ...   \n",
       "1713      21.090000    60507000   9.490000   9.520000   9.280000   9.290000   \n",
       "1714      20.620001    76365200   9.250000   9.920000   9.230000   9.900000   \n",
       "1715      20.969999    52393000   9.590000   9.650000   9.370000   9.620000   \n",
       "1716      20.600000    49835000   9.540000   9.650000   9.380000   9.530000   \n",
       "1717      21.090000    53866600   9.630000   9.710000   9.440000   9.660000   \n",
       "\n",
       "      USO_Adj Close  USO_Volume  \n",
       "0         36.130001    12616700  \n",
       "1         36.270000    12578800  \n",
       "2         36.200001     7418200  \n",
       "3         37.560001    10041600  \n",
       "4         38.110001    10728000  \n",
       "...             ...         ...  \n",
       "1713       9.290000    21598200  \n",
       "1714       9.900000    40978800  \n",
       "1715       9.620000    36578700  \n",
       "1716       9.530000    22803400  \n",
       "1717       9.660000    28417400  \n",
       "\n",
       "[1718 rows x 81 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"../static/csv/FINAL_USO.csv\")\n",
    "print(df.columns)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **1-я бизнес-цель (регрессия)**: \n",
    "\n",
    "Предсказание цены на золото с целью принятия инвесторами решения о покупке товаров."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Целевой признак: цена закрытия Adj Close.\n",
    "\n",
    "Вход: Volume, Hight, Low, Close, Open.\\\n",
    "Достижимый уровень качества: предсказания должны иметь погрешность в среднем не более 5$. Для проверки будет использоваться метрика MAE (средняя абсолютная ошибка)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.discriminant_analysis import StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "from sklearn.metrics import roc_auc_score, confusion_matrix, accuracy_score\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "import sklearn.preprocessing as preproc\n",
    "from sklearn.linear_model import LinearRegression, Ridge\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from mlxtend.evaluate import bias_variance_decomp\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\"..//static//csv//FINAL_USO.csv\")\n",
    "data = df['Volume', 'High', 'Open', 'Close', 'Low','Adj Close']\n",
    "\n",
    "X = data.drop('Adj Close', axis=1)\n",
    "y = data['Adj Close']\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Преобразование числовых данных\n",
    "#заполнение пустых значений медианой\n",
    "num_imputer = SimpleImputer(strategy=\"median\")\n",
    "\n",
    "preprocessing_num = Pipeline(\n",
    "    [\n",
    "        (\"imputer\", num_imputer)\n",
    "    ]\n",
    ")\n",
    "\n",
    "#Категориальных данных нет, поэтому преобразовывать их не надо\n",
    "\n",
    "\n",
    "# Общая предобработка (только числовые данные)\n",
    "preprocessing = ColumnTransformer(\n",
    "    [\n",
    "        (\"nums\", preprocessing_num, X.columns)\n",
    "    ]\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Лнейная регрессия:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Лучшие гиперпараметры:  {'preprocessing': MinMaxScaler()}\n",
      "Cредняя абсолютная ошибка (MAE) = 1.8424538380756087e-14\n",
      "Смещение: -5.1553225998619436e-11\n",
      "Дисперсия: 3.270386026049708e-11\n",
      "R^2 = 1.0\n"
     ]
    }
   ],
   "source": [
    "pipeline_lin_reg = Pipeline([\n",
    "    ('preprocessing', preprocessing),\n",
    "    ('model', LinearRegression())]\n",
    ")\n",
    "\n",
    "# Определение сетки гиперпараметров (возможных знач-ий гиперпараметров) для перебора\n",
    "param_grid = {\n",
    "    #как будут масштабироваться признаки\n",
    "    'preprocessing': [StandardScaler(), preproc.MinMaxScaler(), preproc.MaxAbsScaler(), None]\n",
    "}\n",
    "\n",
    "# Создание объекта GridSearchCV для поиска лучших гиперпараметров по сетке с максимальным знач-ием \n",
    "# отрицательного корня из среднеквадратичной ошибки (отриц., чтобы искался не минимум, а максимум)\n",
    "grid_search = GridSearchCV(pipeline_lin_reg, param_grid, cv=5, scoring='neg_root_mean_squared_error', n_jobs=-1)\n",
    "\n",
    "# Обучение модели с перебором гиперпараметров\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(\"Лучшие гиперпараметры: \", grid_search.best_params_)\n",
    "\n",
    "# Лучшая модель лин. регрессии\n",
    "best_model = grid_search.best_estimator_\n",
    "\n",
    "y_pred = best_model.predict(X_test)\n",
    "\n",
    "print(f'Cредняя абсолютная ошибка (MAE) = {mean_absolute_error(y_test, y_pred)}')\n",
    "\n",
    "\n",
    "# Оценка дисперсии и смещения\n",
    "cv_results = grid_search.cv_results_\n",
    "mean_test_score = cv_results['mean_test_score']\n",
    "std_test_score = cv_results['std_test_score']\n",
    "\n",
    "print(f\"Смещение: {mean_test_score.mean()}\")\n",
    "print(f\"Дисперсия: {std_test_score.mean()}\")\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "print(f'R^2 = {r2_score(y_test, y_pred)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Гребневая регрессия"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Лучшие гиперпараметры:  {'model__alpha': 0, 'preprocessing': StandardScaler()}\n",
      "Cредняя абсолютная ошибка (MAE) = 5.494726121130867e-13\n",
      "Смещение: -0.4263701358095246\n",
      "Дисперсия: 0.02072744817291101\n",
      "R^2 = 1.0\n"
     ]
    }
   ],
   "source": [
    "pipeline_ridge = Pipeline([\n",
    "    ('preprocessing', preprocessing),\n",
    "    ('model', Ridge())]\n",
    ")\n",
    "\n",
    "# Определение сетки гиперпараметров (возможных знач-ий гиперпараметров) для перебора\n",
    "param_grid = {\n",
    "    #как будут масштабироваться признаки\n",
    "    'preprocessing': [StandardScaler(), preproc.MinMaxScaler(), preproc.MaxAbsScaler(), None],\n",
    "    #сила регуляризации\n",
    "    'model__alpha': [0, 0.5, 1.0, 1.5, 2.0, 5.0, 10.0] \n",
    "}\n",
    "\n",
    "# Создание объекта GridSearchCV для поиска лучших гиперпараметров по сетке с максимальным знач-ием \n",
    "# отрицательного корня из среднеквадратичной ошибки (отриц., чтобы искался не минимум, а максимум)\n",
    "grid_search = GridSearchCV(pipeline_ridge, param_grid, cv=5, scoring='neg_root_mean_squared_error', n_jobs=-1, verbose=0)\n",
    "\n",
    "# Обучение модели с перебором гиперпараметров\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(\"Лучшие гиперпараметры: \", grid_search.best_params_)\n",
    "\n",
    "# Лучшая модель регрессии\n",
    "best_model = grid_search.best_estimator_\n",
    "\n",
    "y_pred = best_model.predict(X_test)\n",
    "\n",
    "print(f'Cредняя абсолютная ошибка (MAE) = {mean_absolute_error(y_test, y_pred)}')\n",
    "\n",
    "\n",
    "cv_results = grid_search.cv_results_\n",
    "mean_test_score = cv_results['mean_test_score']\n",
    "std_test_score = cv_results['std_test_score']\n",
    "\n",
    "print(f\"Смещение: {mean_test_score.mean()}\")\n",
    "print(f\"Дисперсия: {std_test_score.mean()}\")\n",
    "\n",
    "print(f'R^2 = {r2_score(y_test, y_pred)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Гребнавая регрессия дала более точные результаты, чем линейная."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Метод градиентного бустинга (набор деревьев решений)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Лучшие гиперпараметры:  {'model__learning_rate': 0.1, 'model__max_depth': 5, 'model__n_estimators': 300, 'preprocessing': None}\n",
      "Cредняя абсолютная ошибка (MAE) = 0.040833243038698064\n",
      "Смещение: -0.2177327926836486\n",
      "Дисперсия: 0.021373424060567556\n",
      "R^2 = 0.9999842165416633\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score, confusion_matrix, accuracy_score\n",
    "from sklearn.ensemble import GradientBoostingRegressor, RandomForestClassifier, GradientBoostingClassifier\n",
    "# Конвейер\n",
    "pipeline_grad = Pipeline([\n",
    "    ('preprocessing', preprocessing),\n",
    "    ('model', GradientBoostingRegressor())\n",
    "])\n",
    "\n",
    "# Определение сетки гиперпараметров\n",
    "param_grid = {\n",
    "    'preprocessing': [StandardScaler(), preproc.MinMaxScaler(), preproc.MaxAbsScaler(), None],\n",
    "    'model__n_estimators': [100, 200, 300],\n",
    "    #Скорость обучения\n",
    "    'model__learning_rate': [0.1, 0.2],\n",
    "    #Максимальная глубина дерева\n",
    "    'model__max_depth': [3, 5, 7]\n",
    "}\n",
    "\n",
    "# Создание объекта GridSearchCV\n",
    "grid_search = GridSearchCV(pipeline_grad, param_grid, cv=2, scoring='neg_root_mean_squared_error', n_jobs=-1)\n",
    "\n",
    "# Обучение модели с перебором гиперпараметров\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(\"Лучшие гиперпараметры: \", grid_search.best_params_)\n",
    "\n",
    "# Лучшая модель случайного леса\n",
    "best_model = grid_search.best_estimator_\n",
    "\n",
    "\n",
    "y_pred = best_model.predict(X_test)\n",
    "\n",
    "\n",
    "print(f'Cредняя абсолютная ошибка (MAE) = {mean_absolute_error(y_test, y_pred)}')\n",
    "\n",
    "\n",
    "# Получение предсказаний на кросс-валидации\n",
    "y_cv_pred = cross_val_predict(best_model, X_train, y_train, cv=3)\n",
    "\n",
    "cv_results = grid_search.cv_results_\n",
    "mean_test_score = cv_results['mean_test_score']\n",
    "std_test_score = cv_results['std_test_score']\n",
    "\n",
    "print(f\"Смещение: {mean_test_score.mean()}\")\n",
    "print(f\"Дисперсия: {std_test_score.mean()}\")\n",
    "\n",
    "print(f'R^2 = {r2_score(y_test, y_pred)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Вывод**: \n",
    "\n",
    "Все 3 модели регрессии показали допустимый уровень \"погрешности\". \n",
    "\n",
    "R² (коэффициент детерминации): 0.99 — это очень высокий уровень, указывающий на то, что модель объясняет 99% вариации целевой переменной. Это свидетельствует о высокой предсказательной способности модели.\n",
    "\n",
    "Из всех моделей градиентный бустинг показал самую низкую \"погрешность\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **2-я бизнес-цель (классификация):** \n",
    "\n",
    "Определить оптимальные коэффициенты для различных факторов, влияющих на цену золота. \n",
    "\n",
    "Целевой признак: Adj Close.\n",
    "\n",
    "Вход: Volume, Hight, Low, Close, Open. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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: 0.9825581395348837\n",
      "Precision: 1.0\n",
      "Recall: 0.9469026548672567\n",
      "F1-score: 0.9727272727272728\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: Random Forest Classification\n",
      "Best Parameters: {'model__max_depth': None, 'model__n_estimators': 100}\n",
      "Accuracy: 1.0\n",
      "Precision: 1.0\n",
      "Recall: 1.0\n",
      "F1-score: 1.0\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': 0.01, 'model__max_depth': 3, 'model__n_estimators': 100}\n",
      "Accuracy: 1.0\n",
      "Precision: 1.0\n",
      "Recall: 1.0\n",
      "F1-score: 1.0\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 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.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//FINAL_USO.csv\")\n",
    "\n",
    "numerical_cols = ['Volume', 'High', 'Open', 'Close', '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['Adj Close']\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",
    "# 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['Adj Close'] > df['Adj Close'].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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Вывод**:\n",
    "\n",
    "Градиентный бустинг и случайный лес выдали наилучшие результаты. "
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Оценка смещения и дисперсии для задачи регрессии:\n",
      "Model: Linear Regression\n",
      "MAE (Cross-Validation): Mean = 3.475420657900542e-14, Std = 2.3108544967235046e-14\n",
      "R² (Cross-Validation): Mean = 1.0, Std = 0.0\n",
      "\n",
      "Model: Random Forest Regression\n",
      "MAE (Cross-Validation): Mean = 4.770713368258853, Std = 9.027907502951276\n",
      "R² (Cross-Validation): Mean = -0.8676362010013315, Std = 3.6735082182967664\n",
      "\n",
      "Model: Gradient Boosting Regression\n",
      "MAE (Cross-Validation): Mean = 4.790726208613611, Std = 8.978223486805094\n",
      "R² (Cross-Validation): Mean = -0.8531326799804774, Std = 3.6480201756306525\n",
      "\n",
      "Оценка смещения и дисперсии для задачи классификации:\n",
      "Model: Logistic Regression\n",
      "Accuracy (Cross-Validation): Mean = 0.9469472506610617, Std = 0.09607008028935687\n",
      "Precision (Cross-Validation): Mean = 0.9903846153846153, Std = 0.019230769230769253\n",
      "Recall (Cross-Validation): Mean = 0.8244897959183675, Std = 0.34090796763789555\n",
      "F1-score (Cross-Validation): Mean = 0.8430120359555126, Std = 0.29664350339720796\n",
      "\n",
      "Model: Random Forest Classification\n",
      "Accuracy (Cross-Validation): Mean = 0.99533527696793, Std = 0.009329446064139945\n",
      "Precision (Cross-Validation): Mean = 1.0, Std = 0.0\n",
      "Recall (Cross-Validation): Mean = 0.9795918367346939, Std = 0.04081632653061225\n",
      "F1-score (Cross-Validation): Mean = 0.9904843365764995, Std = 0.016633019819396317\n",
      "\n",
      "Model: Gradient Boosting Classification\n",
      "Accuracy (Cross-Validation): Mean = 0.9988338192419824, Std = 0.0023323615160349754\n",
      "Precision (Cross-Validation): Mean = 1.0, Std = 0.0\n",
      "Recall (Cross-Validation): Mean = 0.9959183673469388, Std = 0.008163265306122458\n",
      "F1-score (Cross-Validation): Mean = 0.9979381443298969, Std = 0.004123711340206171\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//FINAL_USO.csv\")\n",
    "\n",
    "# Определяем категориальные и числовые столбцы\n",
    "numerical_cols = ['Volume', 'High', 'Open', 'Close', '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['Adj Close']\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['Adj Close'] > df['Adj Close'].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()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1700x1200 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//FINAL_USO.csv\")\n",
    "\n",
    "# Определяем категориальные и числовые столбцы\n",
    "numerical_cols = ['Volume', 'High', 'Open', 'Close', '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['Adj Close']\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=(15, 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['Adj Close'] > df['Adj Close'].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=(17, 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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