notebooks/region-economics-prediction-pure.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.metrics import f1_score, r2_score\n",
    "\n",
    "sns.set(rc={'figure.figsize':(20,20)})\n",
    "\n",
    "current_dir = os.path.abspath('')\n",
    "parent_dir = os.path.dirname(current_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x38\n",
      "x59\n",
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       "\n",
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       "66  ...   619.9  1245619.0  264.48  15.32  17109.5  100.0    30067.0   \n",
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       "\n",
       "         x64  Y1       Y2  \n",
       "0    1002.69   0  1252.25  \n",
       "1     -97.22   0   418.14  \n",
       "2     260.31   0   480.15  \n",
       "3     875.31   0   544.36  \n",
       "4      30.65   0   448.20  \n",
       "..       ...  ..      ...  \n",
       "63    381.14   0   771.30  \n",
       "64  17100.30   1  1151.50  \n",
       "65   2375.05   0   627.63  \n",
       "66    406.31   0   536.38  \n",
       "67    406.42   0   478.24  \n",
       "\n",
       "[68 rows x 66 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regions_data = pd.read_csv(os.path.join(parent_dir, 'datasets', 'region-economics-prediction', 'train.csv'))\n",
    "regions_data.loc[32, 'x59'] = 0.0\n",
    "regions_data.loc[20, 'x4'] = 4379\n",
    "regions_data['x4'] = regions_data['x4'].astype('float64')\n",
    "regions_data['x59'] = regions_data['x59'].astype('float64')\n",
    "regions_data['x58'] = regions_data['x58'].astype('float64')\n",
    "\n",
    "#regions_data.info()\n",
    "\n",
    "# проверка на NaN-ы\n",
    "for i in regions_data.columns:\n",
    "    if regions_data[i].isnull().values.any():\n",
    "        print(i)\n",
    "        regions_data[i].fillna(regions_data[i].mean(), axis=0, inplace=True)\n",
    "\n",
    "regions_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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       "      <td>935400.0</td>\n",
       "      <td>12640.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>236.0</td>\n",
       "      <td>672.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>17540</td>\n",
       "      <td>671.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>42361.80</td>\n",
       "      <td>1650.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11896.0</td>\n",
       "      <td>9.96</td>\n",
       "      <td>1410.6</td>\n",
       "      <td>2855899.0</td>\n",
       "      <td>201.41</td>\n",
       "      <td>15.28</td>\n",
       "      <td>18681.1</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1123004.0</td>\n",
       "      <td>15175.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>73.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6931</td>\n",
       "      <td>262.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>41227.90</td>\n",
       "      <td>721.8</td>\n",
       "      <td>...</td>\n",
       "      <td>-885.0</td>\n",
       "      <td>8.83</td>\n",
       "      <td>530.5</td>\n",
       "      <td>1151042.0</td>\n",
       "      <td>402.10</td>\n",
       "      <td>12.29</td>\n",
       "      <td>18625.2</td>\n",
       "      <td>98.7</td>\n",
       "      <td>712351.0</td>\n",
       "      <td>9626.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4851</td>\n",
       "      <td>350.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>37321.88</td>\n",
       "      <td>580.4</td>\n",
       "      <td>...</td>\n",
       "      <td>1410.0</td>\n",
       "      <td>6.56</td>\n",
       "      <td>456.2</td>\n",
       "      <td>921127.0</td>\n",
       "      <td>185.09</td>\n",
       "      <td>9.70</td>\n",
       "      <td>17259.5</td>\n",
       "      <td>82.0</td>\n",
       "      <td>44377.0</td>\n",
       "      <td>599.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>39.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2604</td>\n",
       "      <td>94.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>52172.70</td>\n",
       "      <td>318.2</td>\n",
       "      <td>...</td>\n",
       "      <td>705.0</td>\n",
       "      <td>11.22</td>\n",
       "      <td>260.7</td>\n",
       "      <td>485621.0</td>\n",
       "      <td>143.96</td>\n",
       "      <td>15.82</td>\n",
       "      <td>21532.6</td>\n",
       "      <td>89.4</td>\n",
       "      <td>403023.0</td>\n",
       "      <td>5446.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>29.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2577</td>\n",
       "      <td>153.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>38307.22</td>\n",
       "      <td>519.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-969.0</td>\n",
       "      <td>7.28</td>\n",
       "      <td>333.7</td>\n",
       "      <td>724686.0</td>\n",
       "      <td>311.61</td>\n",
       "      <td>5.70</td>\n",
       "      <td>15755.2</td>\n",
       "      <td>107.1</td>\n",
       "      <td>48430.0</td>\n",
       "      <td>654.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>58.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5852</td>\n",
       "      <td>421.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>48604.38</td>\n",
       "      <td>456.2</td>\n",
       "      <td>...</td>\n",
       "      <td>1323.0</td>\n",
       "      <td>8.28</td>\n",
       "      <td>275.5</td>\n",
       "      <td>609071.0</td>\n",
       "      <td>145.57</td>\n",
       "      <td>6.43</td>\n",
       "      <td>19293.9</td>\n",
       "      <td>108.0</td>\n",
       "      <td>142046.0</td>\n",
       "      <td>1919.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>49.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4498</td>\n",
       "      <td>263.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>37751.68</td>\n",
       "      <td>615.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2680.0</td>\n",
       "      <td>7.33</td>\n",
       "      <td>502.3</td>\n",
       "      <td>987032.0</td>\n",
       "      <td>240.02</td>\n",
       "      <td>9.49</td>\n",
       "      <td>17277.4</td>\n",
       "      <td>100.3</td>\n",
       "      <td>13287.0</td>\n",
       "      <td>179.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>40.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3736</td>\n",
       "      <td>182.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>39021.40</td>\n",
       "      <td>300.4</td>\n",
       "      <td>...</td>\n",
       "      <td>-432.0</td>\n",
       "      <td>9.85</td>\n",
       "      <td>238.3</td>\n",
       "      <td>532036.0</td>\n",
       "      <td>207.97</td>\n",
       "      <td>1.77</td>\n",
       "      <td>18009.7</td>\n",
       "      <td>105.2</td>\n",
       "      <td>46958.0</td>\n",
       "      <td>634.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>21.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1004</td>\n",
       "      <td>55.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32947.07</td>\n",
       "      <td>244.6</td>\n",
       "      <td>...</td>\n",
       "      <td>40.0</td>\n",
       "      <td>9.58</td>\n",
       "      <td>186.2</td>\n",
       "      <td>465357.0</td>\n",
       "      <td>268.21</td>\n",
       "      <td>1.85</td>\n",
       "      <td>16914.7</td>\n",
       "      <td>115.3</td>\n",
       "      <td>4520.0</td>\n",
       "      <td>61.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>209.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>19385</td>\n",
       "      <td>618.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>38809.20</td>\n",
       "      <td>2231.2</td>\n",
       "      <td>...</td>\n",
       "      <td>11448.0</td>\n",
       "      <td>10.18</td>\n",
       "      <td>1977.6</td>\n",
       "      <td>3894120.0</td>\n",
       "      <td>522.63</td>\n",
       "      <td>36.85</td>\n",
       "      <td>16132.3</td>\n",
       "      <td>115.7</td>\n",
       "      <td>629776.0</td>\n",
       "      <td>8510.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>134.0</td>\n",
       "      <td>335.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>26804</td>\n",
       "      <td>1025.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>36163.45</td>\n",
       "      <td>2470.8</td>\n",
       "      <td>...</td>\n",
       "      <td>15022.0</td>\n",
       "      <td>8.35</td>\n",
       "      <td>2046.8</td>\n",
       "      <td>4181486.0</td>\n",
       "      <td>247.16</td>\n",
       "      <td>52.19</td>\n",
       "      <td>18113.8</td>\n",
       "      <td>96.2</td>\n",
       "      <td>127942.0</td>\n",
       "      <td>1728.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>75.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>8807</td>\n",
       "      <td>283.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>37247.70</td>\n",
       "      <td>1150.2</td>\n",
       "      <td>...</td>\n",
       "      <td>-7641.0</td>\n",
       "      <td>9.05</td>\n",
       "      <td>937.9</td>\n",
       "      <td>3870860.0</td>\n",
       "      <td>343.98</td>\n",
       "      <td>25.73</td>\n",
       "      <td>16373.3</td>\n",
       "      <td>108.6</td>\n",
       "      <td>57754.0</td>\n",
       "      <td>780.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>105.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>15971</td>\n",
       "      <td>729.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>39169.38</td>\n",
       "      <td>1889.4</td>\n",
       "      <td>...</td>\n",
       "      <td>7733.0</td>\n",
       "      <td>8.52</td>\n",
       "      <td>1625.1</td>\n",
       "      <td>3154164.0</td>\n",
       "      <td>353.75</td>\n",
       "      <td>26.57</td>\n",
       "      <td>17307.0</td>\n",
       "      <td>105.6</td>\n",
       "      <td>346161.0</td>\n",
       "      <td>4677.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>48.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>4577</td>\n",
       "      <td>118.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36465.38</td>\n",
       "      <td>847.2</td>\n",
       "      <td>...</td>\n",
       "      <td>-634.0</td>\n",
       "      <td>7.02</td>\n",
       "      <td>635.7</td>\n",
       "      <td>1290898.0</td>\n",
       "      <td>408.74</td>\n",
       "      <td>11.09</td>\n",
       "      <td>16212.8</td>\n",
       "      <td>88.7</td>\n",
       "      <td>38340.0</td>\n",
       "      <td>518.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>27.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>3813</td>\n",
       "      <td>165.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>35046.90</td>\n",
       "      <td>507.4</td>\n",
       "      <td>...</td>\n",
       "      <td>-2758.0</td>\n",
       "      <td>10.38</td>\n",
       "      <td>465.7</td>\n",
       "      <td>997778.0</td>\n",
       "      <td>145.18</td>\n",
       "      <td>7.00</td>\n",
       "      <td>16025.0</td>\n",
       "      <td>93.6</td>\n",
       "      <td>168027.0</td>\n",
       "      <td>2270.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       x1     x2     x3     x4      x5     x6     x7    x8        x9     x10  \\\n",
       "0   132.0  473.0   57.0  12201   461.0   78.0   85.0  17.0  40450.05  1032.4   \n",
       "1    51.0  125.0   26.0   4734   166.0   22.0   77.0   1.0  39120.38   998.2   \n",
       "2   236.0  672.0  164.0  17540   671.0   89.0  180.0  26.0  42361.80  1650.0   \n",
       "3    73.0  186.0   20.0   6931   262.0   31.0   74.0   6.0  41227.90   721.8   \n",
       "4    58.0  104.0   12.0   4851   350.0   27.0   67.0  13.0  37321.88   580.4   \n",
       "5    39.0  110.0   12.0   2604    94.0   21.0   21.0  12.0  52172.70   318.2   \n",
       "6    29.0   63.0    7.0   2577   153.0   16.0   16.0  10.0  38307.22   519.0   \n",
       "7    58.0  101.0   21.0   5852   421.0   25.0   66.0  17.0  48604.38   456.2   \n",
       "8    49.0  111.0   26.0   4498   263.0   38.0   60.0  16.0  37751.68   615.0   \n",
       "9    40.0  125.0   22.0   3736   182.0   17.0   64.0   5.0  39021.40   300.4   \n",
       "10   21.0   28.0    8.0   1004    55.0    9.0   41.0   2.0  32947.07   244.6   \n",
       "11  209.0  348.0  162.0  19385   618.0  111.0  260.0  31.0  38809.20  2231.2   \n",
       "12  134.0  335.0   51.0  26804  1025.0  106.0  136.0  23.0  36163.45  2470.8   \n",
       "13   75.0  264.0   34.0   8807   283.0   72.0   28.0   3.0  37247.70  1150.2   \n",
       "14  105.0  274.0   26.0  15971   729.0   95.0  121.0  17.0  39169.38  1889.4   \n",
       "15   48.0  138.0   32.0   4577   118.0   19.0   54.0   1.0  36465.38   847.2   \n",
       "16   27.0   87.0   57.0   3813   165.0   23.0   35.0  23.0  35046.90   507.4   \n",
       "\n",
       "    ...      x55    x56     x57        x58     x59    x60      x61    x62  \\\n",
       "0   ...   -310.0   9.15   960.6  1877844.0  120.69  12.55  20830.5   75.0   \n",
       "1   ...   6436.0   7.72   794.5  1541259.0  175.28  13.76  16022.7  108.6   \n",
       "2   ...  11896.0   9.96  1410.6  2855899.0  201.41  15.28  18681.1  106.0   \n",
       "3   ...   -885.0   8.83   530.5  1151042.0  402.10  12.29  18625.2   98.7   \n",
       "4   ...   1410.0   6.56   456.2   921127.0  185.09   9.70  17259.5   82.0   \n",
       "5   ...    705.0  11.22   260.7   485621.0  143.96  15.82  21532.6   89.4   \n",
       "6   ...   -969.0   7.28   333.7   724686.0  311.61   5.70  15755.2  107.1   \n",
       "7   ...   1323.0   8.28   275.5   609071.0  145.57   6.43  19293.9  108.0   \n",
       "8   ...   2680.0   7.33   502.3   987032.0  240.02   9.49  17277.4  100.3   \n",
       "9   ...   -432.0   9.85   238.3   532036.0  207.97   1.77  18009.7  105.2   \n",
       "10  ...     40.0   9.58   186.2   465357.0  268.21   1.85  16914.7  115.3   \n",
       "11  ...  11448.0  10.18  1977.6  3894120.0  522.63  36.85  16132.3  115.7   \n",
       "12  ...  15022.0   8.35  2046.8  4181486.0  247.16  52.19  18113.8   96.2   \n",
       "13  ...  -7641.0   9.05   937.9  3870860.0  343.98  25.73  16373.3  108.6   \n",
       "14  ...   7733.0   8.52  1625.1  3154164.0  353.75  26.57  17307.0  105.6   \n",
       "15  ...   -634.0   7.02   635.7  1290898.0  408.74  11.09  16212.8   88.7   \n",
       "16  ...  -2758.0  10.38   465.7   997778.0  145.18   7.00  16025.0   93.6   \n",
       "\n",
       "          x63       x64  \n",
       "0    225557.0   3048.07  \n",
       "1    935400.0  12640.54  \n",
       "2   1123004.0  15175.73  \n",
       "3    712351.0   9626.36  \n",
       "4     44377.0    599.69  \n",
       "5    403023.0   5446.26  \n",
       "6     48430.0    654.46  \n",
       "7    142046.0   1919.54  \n",
       "8     13287.0    179.55  \n",
       "9     46958.0    634.57  \n",
       "10     4520.0     61.08  \n",
       "11   629776.0   8510.49  \n",
       "12   127942.0   1728.95  \n",
       "13    57754.0    780.46  \n",
       "14   346161.0   4677.85  \n",
       "15    38340.0    518.11  \n",
       "16   168027.0   2270.64  \n",
       "\n",
       "[17 rows x 64 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regions_final_test_data = pd.read_csv(os.path.join(parent_dir, 'datasets', 'region-economics-prediction', 'test.csv'))\n",
    "regions_final_test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train, data_test = train_test_split(regions_data, test_size=0.1, random_state=42)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Классификация (но использую регрессоры)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, Y_train = data_train.drop(['Y1','Y2'], axis=1), data_train['Y1']\n",
    "X_test, Y_test = data_test.drop(['Y1','Y2'], axis=1), data_test['Y1']\n",
    "\n",
    "model_0 = KNeighborsRegressor()\n",
    "model_0.fit(X_train, Y_train)\n",
    "\n",
    "model_1 = DecisionTreeRegressor()\n",
    "model_1.fit(X_train, Y_train)\n",
    "\n",
    "model_2 = RandomForestRegressor(n_estimators=1000)\n",
    "model_2.fit(X_train, Y_train)\n",
    "\n",
    "model_3 = ExtraTreesRegressor(n_estimators=1000)\n",
    "model_3.fit(X_train, Y_train)\n",
    "\n",
    "model_0_test_predictions = [0 if i < 0.05 else 1 for i in model_0.predict(X_test)]\n",
    "model_1_test_predictions = [0 if i < 0.05 else 1 for i in model_1.predict(X_test)]\n",
    "model_2_test_predictions = [0 if i < 0.05 else 1 for i in model_2.predict(X_test)]\n",
    "model_3_test_predictions = [0 if i < 0.05 else 1 for i in model_3.predict(X_test)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score модели KNeighborsRegressor() составляет 0.0\n",
      "f1_score модели DecisionTreeRegressor() составляет 0.0\n",
      "f1_score модели RandomForestRegressor(n_estimators=1000) составляет 0.0\n",
      "f1_score модели ExtraTreesRegressor(n_estimators=1000) составляет 0.5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    f'f1_score модели {model_0} составляет {f1_score(Y_test, model_0_test_predictions)}\\n'\n",
    "    f'f1_score модели {model_1} составляет {f1_score(Y_test, model_1_test_predictions)}\\n'\n",
    "    f'f1_score модели {model_2} составляет {f1_score(Y_test, model_2_test_predictions)}\\n'\n",
    "    f'f1_score модели {model_3} составляет {f1_score(Y_test, model_3_test_predictions)}\\n'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.006, 0.401, 0.804, 0.255, 0.007, 0.016, 0.   , 0.02 , 0.   ,\n",
       "       0.   , 0.   , 0.441, 0.11 , 0.   , 0.03 , 0.   , 0.   ])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_3.predict(regions_final_test_data)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Регрессия"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ExtraTreesRegressor(n_estimators=10000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExtraTreesRegressor</label><div class=\"sk-toggleable__content\"><pre>ExtraTreesRegressor(n_estimators=10000)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "ExtraTreesRegressor(n_estimators=10000)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, Y_train = data_train.drop(['Y1','Y2'], axis=1), data_train['Y2']\n",
    "X_test, Y_test = data_test.drop(['Y1','Y2'], axis=1), data_test['Y2']\n",
    "\n",
    "model_0 = KNeighborsRegressor()\n",
    "model_0.fit(X_train, Y_train)\n",
    "\n",
    "model_1 = DecisionTreeRegressor()\n",
    "model_1.fit(X_train, Y_train)\n",
    "\n",
    "model_2 = RandomForestRegressor(n_estimators=1000)\n",
    "model_2.fit(X_train, Y_train)\n",
    "\n",
    "model_3 = ExtraTreesRegressor(n_estimators=10000)\n",
    "model_3.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score модели KNeighborsRegressor() составляет 0.40723667520346096\n",
      "r2_score модели DecisionTreeRegressor() составляет 0.5549960406551494\n",
      "r2_score модели RandomForestRegressor(n_estimators=1000) составляет 0.9209462098875588\n",
      "r2_score модели ExtraTreesRegressor(n_estimators=10000) составляет 0.9556497648159903\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    f'r2_score модели {model_0} составляет {r2_score(Y_test, model_0.predict(X_test))}\\n'\n",
    "    f'r2_score модели {model_1} составляет {r2_score(Y_test, model_1.predict(X_test))}\\n'\n",
    "    f'r2_score модели {model_2} составляет {r2_score(Y_test, model_2.predict(X_test))}\\n'\n",
    "    f'r2_score модели {model_3} составляет {r2_score(Y_test, model_3.predict(X_test))}\\n'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 737.916791  569.932148  772.418173  661.357268  519.830626 1011.007923\n",
      "  493.667939  776.567275  505.199338  522.542907  470.284632  585.031355\n",
      "  546.202526  522.783882  563.399397  496.86415   497.560869]\n"
     ]
    }
   ],
   "source": [
    "print(model_3.predict(regions_final_test_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  },
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 "nbformat": 4,
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