demo-sberbank-russian-housing-market.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4038a496",
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir -p data/sberbank-russian-housing-market"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c03a0ef0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  data/sberbank-russian-housing-market.zip\n",
      "  inflating: data/sberbank-russian-housing-market/data_dictionary.txt  \n",
      "  inflating: data/sberbank-russian-housing-market/macro.csv.zip  \n",
      "  inflating: data/sberbank-russian-housing-market/sample_submission.csv.zip  \n",
      "  inflating: data/sberbank-russian-housing-market/test.csv.zip  \n",
      "  inflating: data/sberbank-russian-housing-market/train.csv.zip  \n"
     ]
    }
   ],
   "source": [
    "!unzip -o data/sberbank-russian-housing-market.zip -d data/sberbank-russian-housing-market"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bc09efca",
   "metadata": {},
   "outputs": [],
   "source": [
    "import zipfile\n",
    "from statistics import mean\n",
    "\n",
    "import advertion\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import RocCurveDisplay, auc\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "seed = np.random.RandomState(31337)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "426ea934",
   "metadata": {},
   "outputs": [],
   "source": [
    "z = zipfile.ZipFile(\"data/sberbank-russian-housing-market/train.csv.zip\")\n",
    "z.extractall(\"data/sberbank-russian-housing-market/\")\n",
    "\n",
    "t = zipfile.ZipFile(\"data/sberbank-russian-housing-market/test.csv.zip\")\n",
    "t.extractall(\"data/sberbank-russian-housing-market/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "522e9523",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30471, 292)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainset = pd.read_csv(\"data/sberbank-russian-housing-market/train.csv\")\n",
    "\n",
    "trainset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bebed19d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7662, 291)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testset = pd.read_csv(\"data/sberbank-russian-housing-market/test.csv\")\n",
    "\n",
    "testset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0b56a640",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'timestamp', 'full_sq', 'life_sq', 'floor', 'max_floor',\n",
       "       'material', 'build_year', 'num_room', 'kitch_sq',\n",
       "       ...\n",
       "       'cafe_count_5000_price_2500', 'cafe_count_5000_price_4000',\n",
       "       'cafe_count_5000_price_high', 'big_church_count_5000',\n",
       "       'church_count_5000', 'mosque_count_5000', 'leisure_count_5000',\n",
       "       'sport_count_5000', 'market_count_5000', 'price_doc'],\n",
       "      dtype='object', length=292)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a25664a2",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "          id   timestamp  full_sq  life_sq  floor  max_floor  material  \\\n",
       "0          1  2011-08-20       43     27.0    4.0        NaN       NaN   \n",
       "1          2  2011-08-23       34     19.0    3.0        NaN       NaN   \n",
       "2          3  2011-08-27       43     29.0    2.0        NaN       NaN   \n",
       "3          4  2011-09-01       89     50.0    9.0        NaN       NaN   \n",
       "4          5  2011-09-05       77     77.0    4.0        NaN       NaN   \n",
       "...      ...         ...      ...      ...    ...        ...       ...   \n",
       "30466  30469  2015-06-30       44     27.0    7.0        9.0       1.0   \n",
       "30467  30470  2015-06-30       86     59.0    3.0        9.0       2.0   \n",
       "30468  30471  2015-06-30       45      NaN   10.0       20.0       1.0   \n",
       "30469  30472  2015-06-30       64     32.0    5.0       15.0       1.0   \n",
       "30470  30473  2015-06-30       43     28.0    1.0        9.0       1.0   \n",
       "\n",
       "       build_year  num_room  kitch_sq  ...  cafe_count_5000_price_2500  \\\n",
       "0             NaN       NaN       NaN  ...                           9   \n",
       "1             NaN       NaN       NaN  ...                          15   \n",
       "2             NaN       NaN       NaN  ...                          10   \n",
       "3             NaN       NaN       NaN  ...                          11   \n",
       "4             NaN       NaN       NaN  ...                         319   \n",
       "...           ...       ...       ...  ...                         ...   \n",
       "30466      1975.0       2.0       6.0  ...                          15   \n",
       "30467      1935.0       4.0      10.0  ...                         313   \n",
       "30468         NaN       1.0       1.0  ...                           1   \n",
       "30469      2003.0       2.0      11.0  ...                          22   \n",
       "30470      1968.0       2.0       6.0  ...                           5   \n",
       "\n",
       "      cafe_count_5000_price_4000 cafe_count_5000_price_high  \\\n",
       "0                              4                          0   \n",
       "1                              3                          0   \n",
       "2                              3                          0   \n",
       "3                              2                          1   \n",
       "4                            108                         17   \n",
       "...                          ...                        ...   \n",
       "30466                          5                          0   \n",
       "30467                        128                         24   \n",
       "30468                          1                          0   \n",
       "30469                          1                          1   \n",
       "30470                          2                          0   \n",
       "\n",
       "       big_church_count_5000  church_count_5000  mosque_count_5000  \\\n",
       "0                         13                 22                  1   \n",
       "1                         15                 29                  1   \n",
       "2                         11                 27                  0   \n",
       "3                          4                  4                  0   \n",
       "4                        135                236                  2   \n",
       "...                      ...                ...                ...   \n",
       "30466                     15                 26                  1   \n",
       "30467                     98                182                  1   \n",
       "30468                      2                 12                  0   \n",
       "30469                      6                 31                  1   \n",
       "30470                      7                 16                  0   \n",
       "\n",
       "       leisure_count_5000  sport_count_5000  market_count_5000  price_doc  \n",
       "0                       0                52                  4    5850000  \n",
       "1                      10                66                 14    6000000  \n",
       "2                       4                67                 10    5700000  \n",
       "3                       0                26                  3   13100000  \n",
       "4                      91               195                 14   16331452  \n",
       "...                   ...               ...                ...        ...  \n",
       "30466                   2                84                  6    7400000  \n",
       "30467                  82               171                 15   25000000  \n",
       "30468                   1                11                  1    6970959  \n",
       "30469                   4                65                  7   13500000  \n",
       "30470                   9                54                 10    5600000  \n",
       "\n",
       "[30471 rows x 292 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0e116ff7",
   "metadata": {},
   "outputs": [
    {
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       "      <th>leisure_count_5000</th>\n",
       "      <th>sport_count_5000</th>\n",
       "      <th>market_count_5000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30474</td>\n",
       "      <td>2015-07-01</td>\n",
       "      <td>39.00</td>\n",
       "      <td>20.7</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.9</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30475</td>\n",
       "      <td>2015-07-01</td>\n",
       "      <td>79.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30476</td>\n",
       "      <td>2015-07-01</td>\n",
       "      <td>40.50</td>\n",
       "      <td>25.1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>...</td>\n",
       "      <td>42</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>71</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30477</td>\n",
       "      <td>2015-07-01</td>\n",
       "      <td>62.80</td>\n",
       "      <td>36.0</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2</td>\n",
       "      <td>62.8</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30478</td>\n",
       "      <td>2015-07-01</td>\n",
       "      <td>40.00</td>\n",
       "      <td>40.0</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</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>7657</th>\n",
       "      <td>38131</td>\n",
       "      <td>2016-05-26</td>\n",
       "      <td>52.20</td>\n",
       "      <td>31.8</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>2</td>\n",
       "      <td>9.1</td>\n",
       "      <td>...</td>\n",
       "      <td>81</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>75</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7658</th>\n",
       "      <td>38132</td>\n",
       "      <td>2016-05-28</td>\n",
       "      <td>54.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7659</th>\n",
       "      <td>38133</td>\n",
       "      <td>2016-05-30</td>\n",
       "      <td>41.08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>636</td>\n",
       "      <td>371</td>\n",
       "      <td>141</td>\n",
       "      <td>26</td>\n",
       "      <td>150</td>\n",
       "      <td>249</td>\n",
       "      <td>2</td>\n",
       "      <td>105</td>\n",
       "      <td>203</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7660</th>\n",
       "      <td>38134</td>\n",
       "      <td>2016-05-30</td>\n",
       "      <td>34.80</td>\n",
       "      <td>19.8</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.4</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7661</th>\n",
       "      <td>38135</td>\n",
       "      <td>2016-05-30</td>\n",
       "      <td>63.00</td>\n",
       "      <td>43.8</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7.1</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7662 rows × 291 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id   timestamp  full_sq  life_sq  floor  max_floor  material  \\\n",
       "0     30474  2015-07-01    39.00     20.7      2          9         1   \n",
       "1     30475  2015-07-01    79.20      NaN      8         17         1   \n",
       "2     30476  2015-07-01    40.50     25.1      3          5         2   \n",
       "3     30477  2015-07-01    62.80     36.0     17         17         1   \n",
       "4     30478  2015-07-01    40.00     40.0     17         17         1   \n",
       "...     ...         ...      ...      ...    ...        ...       ...   \n",
       "7657  38131  2016-05-26    52.20     31.8     10         12         5   \n",
       "7658  38132  2016-05-28    54.09      NaN     14          0         1   \n",
       "7659  38133  2016-05-30    41.08      1.0     12          1         1   \n",
       "7660  38134  2016-05-30    34.80     19.8      8          9         5   \n",
       "7661  38135  2016-05-30    63.00     43.8      5          5         1   \n",
       "\n",
       "      build_year  num_room  kitch_sq  ...  cafe_count_5000_price_1500  \\\n",
       "0         1998.0         1       8.9  ...                           8   \n",
       "1            0.0         3       1.0  ...                           4   \n",
       "2         1960.0         2       4.8  ...                          42   \n",
       "3         2016.0         2      62.8  ...                           1   \n",
       "4            0.0         1       1.0  ...                           5   \n",
       "...          ...       ...       ...  ...                         ...   \n",
       "7657      1973.0         2       9.1  ...                          81   \n",
       "7658         NaN         2       0.0  ...                           1   \n",
       "7659         1.0         1       1.0  ...                         636   \n",
       "7660      1977.0         1       6.4  ...                          16   \n",
       "7661      1973.0         3       7.1  ...                          31   \n",
       "\n",
       "     cafe_count_5000_price_2500 cafe_count_5000_price_4000  \\\n",
       "0                             0                          0   \n",
       "1                             1                          1   \n",
       "2                            11                          4   \n",
       "3                             1                          2   \n",
       "4                             1                          1   \n",
       "...                         ...                        ...   \n",
       "7657                         28                          1   \n",
       "7658                          2                          1   \n",
       "7659                        371                        141   \n",
       "7660                          7                          2   \n",
       "7661                         11                          1   \n",
       "\n",
       "      cafe_count_5000_price_high  big_church_count_5000  church_count_5000  \\\n",
       "0                              0                      1                 10   \n",
       "1                              0                      2                 11   \n",
       "2                              0                     10                 21   \n",
       "3                              0                      0                 10   \n",
       "4                              0                      2                 12   \n",
       "...                          ...                    ...                ...   \n",
       "7657                           1                      8                 39   \n",
       "7658                           0                      0                 10   \n",
       "7659                          26                    150                249   \n",
       "7660                           0                      5                 11   \n",
       "7661                           0                      6                 26   \n",
       "\n",
       "      mosque_count_5000  leisure_count_5000  sport_count_5000  \\\n",
       "0                     1                   0                14   \n",
       "1                     0                   1                12   \n",
       "2                     0                  10                71   \n",
       "3                     0                   0                 2   \n",
       "4                     0                   1                11   \n",
       "...                 ...                 ...               ...   \n",
       "7657                  2                   3                75   \n",
       "7658                  0                   0                 0   \n",
       "7659                  2                 105               203   \n",
       "7660                  0                   2                43   \n",
       "7661                  1                   4                42   \n",
       "\n",
       "      market_count_5000  \n",
       "0                     1  \n",
       "1                     1  \n",
       "2                    11  \n",
       "3                     0  \n",
       "4                     1  \n",
       "...                 ...  \n",
       "7657                 12  \n",
       "7658                  0  \n",
       "7659                 13  \n",
       "7660                 10  \n",
       "7661                 11  \n",
       "\n",
       "[7662 rows x 291 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8c4df49c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop target variable from our training dataset\n",
    "X_train = trainset.drop([\"price_doc\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f888632f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = testset.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "edcdf55f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train[\"_adval_label_\"] = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0c67fd6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test[\"_adval_label_\"] = 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6ff63ea1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38133, 292)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine training & test datasets\n",
    "combined_dataset = pd.concat([X_train, X_test], axis=0, ignore_index=True)\n",
    "\n",
    "combined_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c270e417",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'timestamp', 'full_sq', 'life_sq', 'floor', 'max_floor',\n",
       "       'material', 'build_year', 'num_room', 'kitch_sq',\n",
       "       ...\n",
       "       'cafe_count_5000_price_2500', 'cafe_count_5000_price_4000',\n",
       "       'cafe_count_5000_price_high', 'big_church_count_5000',\n",
       "       'church_count_5000', 'mosque_count_5000', 'leisure_count_5000',\n",
       "       'sport_count_5000', 'market_count_5000', '_adval_label_'],\n",
       "      dtype='object', length=292)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "671c6e78",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert non-numerical columns to integers\n",
    "combined_dataset_numeric = combined_dataset.select_dtypes(exclude=[\"object\"])\n",
    "combined_dataset_obj = combined_dataset.select_dtypes(include=[\"object\"]).copy()\n",
    "\n",
    "for c in combined_dataset_obj:\n",
    "    combined_dataset_obj[c] = pd.factorize(combined_dataset_obj[c])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "062d98d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_dataset = pd.concat([combined_dataset_numeric, combined_dataset_obj], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "be49a3b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# shuffle it\n",
    "combined_dataset_shuffled = combined_dataset.sample(frac=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7890c1f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = combined_dataset_shuffled.drop([\"_adval_label_\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4aa308bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.drop([\"id\", \"timestamp\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a37ffd50",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = combined_dataset_shuffled[\"_adval_label_\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "acfaf59b",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_splits = 5\n",
    "cv = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=seed)\n",
    "classifier = xgb.XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6feeb1aa",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "45918b0cab524fb899a6a99c10a4dcad",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tprs = []\n",
    "aucs = []\n",
    "mean_fpr = np.linspace(0, 1, 100)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "\n",
    "for fold, (train, test) in tqdm([*enumerate(cv.split(X, y))]):\n",
    "    classifier.fit(X.iloc[train], y.iloc[train])\n",
    "    viz = RocCurveDisplay.from_estimator(\n",
    "        classifier,\n",
    "        X.iloc[test],\n",
    "        y.iloc[test],\n",
    "        name=f\"ROC fold {fold}\",\n",
    "        alpha=0.3,\n",
    "        lw=1,\n",
    "        ax=ax,\n",
    "        plot_chance_level=(fold == n_splits - 1),\n",
    "    )\n",
    "    interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr)\n",
    "    interp_tpr[0] = 0.0\n",
    "    tprs.append(interp_tpr)\n",
    "    aucs.append(viz.roc_auc)\n",
    "\n",
    "mean_tpr = np.mean(tprs, axis=0)\n",
    "mean_tpr[-1] = 1.0\n",
    "mean_auc = auc(mean_fpr, mean_tpr)\n",
    "std_auc = np.std(aucs)\n",
    "ax.plot(\n",
    "    mean_fpr,\n",
    "    mean_tpr,\n",
    "    color=\"b\",\n",
    "    label=r\"Mean ROC (AUC = %0.2f $\\pm$ %0.2f)\" % (mean_auc, std_auc),\n",
    "    lw=2,\n",
    "    alpha=0.8,\n",
    ")\n",
    "\n",
    "std_tpr = np.std(tprs, axis=0)\n",
    "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n",
    "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n",
    "ax.fill_between(\n",
    "    mean_fpr,\n",
    "    tprs_lower,\n",
    "    tprs_upper,\n",
    "    color=\"grey\",\n",
    "    alpha=0.2,\n",
    "    label=r\"$\\pm$ 1 std. dev.\",\n",
    ")\n",
    "\n",
    "ax.set(\n",
    "    xlim=[-0.05, 1.05],\n",
    "    ylim=[-0.05, 1.05],\n",
    "    xlabel=\"False Positive Rate\",\n",
    "    ylabel=\"True Positive Rate\",\n",
    ")\n",
    "ax.axis(\"square\")\n",
    "ax.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "57c497ba",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9930616257454692,\n",
       " 0.9933295525996183,\n",
       " 0.993228891121216,\n",
       " 0.9927296013456716,\n",
       " 0.993396535221478]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aucs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "525f9337",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9931492412066906"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean(aucs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74aa4c8e",
   "metadata": {},
   "source": [
    "### using the advertion package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5f12c5b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Working only with available numerical features, categorical features are not yet supported.\n",
      "INFO: Will try to identify adversarial features (see: https://advertion.readthedocs.io/en/0.1.0-alpha/adversarial-features)\n",
      "INFO: Identified adversarial feature: [name: id, statistic: 1.0, p-value: 0.0].\n",
      "INFO: Identified adversarial feature: [name: max_floor, statistic: 0.3142988415214466, p-value: 0.0].\n",
      "INFO: Identified adversarial feature: [name: material, statistic: 0.3141347510747924, p-value: 0.0].\n",
      "INFO: Identified adversarial feature: [name: build_year, statistic: 0.3096463183976076, p-value: 0.0].\n",
      "INFO: Identified adversarial feature: [name: num_room, statistic: 0.3142667815633885, p-value: 0.0].\n",
      "INFO: Identified adversarial feature: [name: kitch_sq, statistic: 0.3144682388870098, p-value: 0.0].\n",
      "INFO: Identified adversarial feature: [name: state, statistic: 0.3544364184470351, p-value: 0.0].\n",
      "INFO: training and test datasets do not follow the same underlying distribution [mean ROC AUC: 0.987].\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'adversarial_features': ['id',\n",
       "  'max_floor',\n",
       "  'material',\n",
       "  'build_year',\n",
       "  'num_room',\n",
       "  'kitch_sq',\n",
       "  'state'],\n",
       " 'datasets_follow_same_distribution': False,\n",
       " 'mean_roc_auc': 0.9870733237580953}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertion.validate(\n",
    "    trainset=trainset,\n",
    "    testset=testset,\n",
    "    target=\"price_doc\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d515b380",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_splits = 5\n",
    "cv = StratifiedKFold(n_splits=n_splits, shuffle=False)\n",
    "classifier = xgb.XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7a2a7b4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>full_sq</th>\n",
       "      <th>life_sq</th>\n",
       "      <th>floor</th>\n",
       "      <th>max_floor</th>\n",
       "      <th>material</th>\n",
       "      <th>build_year</th>\n",
       "      <th>num_room</th>\n",
       "      <th>kitch_sq</th>\n",
       "      <th>state</th>\n",
       "      <th>...</th>\n",
       "      <th>oil_chemistry_raion</th>\n",
       "      <th>radiation_raion</th>\n",
       "      <th>railroad_terminal_raion</th>\n",
       "      <th>big_market_raion</th>\n",
       "      <th>nuclear_reactor_raion</th>\n",
       "      <th>detention_facility_raion</th>\n",
       "      <th>water_1line</th>\n",
       "      <th>big_road1_1line</th>\n",
       "      <th>railroad_1line</th>\n",
       "      <th>ecology</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36438</th>\n",
       "      <td>36441</td>\n",
       "      <td>53.5</td>\n",
       "      <td>27.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>27762</th>\n",
       "      <td>27765</td>\n",
       "      <td>57.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>32112</th>\n",
       "      <td>32115</td>\n",
       "      <td>34.1</td>\n",
       "      <td>16.7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37146</th>\n",
       "      <td>37149</td>\n",
       "      <td>46.1</td>\n",
       "      <td>28.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1971.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>13355</th>\n",
       "      <td>13358</td>\n",
       "      <td>39.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 292 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  full_sq  life_sq  floor  max_floor  material  build_year  \\\n",
       "36438  36441     53.5     27.8    1.0        4.0       1.0      2000.0   \n",
       "27762  27765     57.0      NaN   20.0        1.0       1.0         NaN   \n",
       "32112  32115     34.1     16.7    8.0        9.0       2.0      1970.0   \n",
       "37146  37149     46.1     28.5    3.0        9.0       5.0      1971.0   \n",
       "13355  13358     39.0      NaN    3.0       17.0       1.0         NaN   \n",
       "\n",
       "       num_room  kitch_sq  state  ...  oil_chemistry_raion  radiation_raion  \\\n",
       "36438       2.0       7.6    2.0  ...                    0                0   \n",
       "27762       2.0       1.0    NaN  ...                    0                0   \n",
       "32112       1.0       7.4    2.0  ...                    0                1   \n",
       "37146       2.0       6.0    4.0  ...                    1                1   \n",
       "13355       1.0       1.0    NaN  ...                    0                0   \n",
       "\n",
       "       railroad_terminal_raion  big_market_raion  nuclear_reactor_raion  \\\n",
       "36438                        0                 0                      0   \n",
       "27762                        0                 0                      0   \n",
       "32112                        0                 0                      0   \n",
       "37146                        0                 0                      0   \n",
       "13355                        0                 0                      0   \n",
       "\n",
       "       detention_facility_raion  water_1line  big_road1_1line  railroad_1line  \\\n",
       "36438                         0            0                0               0   \n",
       "27762                         0            0                0               0   \n",
       "32112                         0            0                0               0   \n",
       "37146                         0            0                0               0   \n",
       "13355                         0            0                0               0   \n",
       "\n",
       "       ecology  \n",
       "36438        3  \n",
       "27762        0  \n",
       "32112        2  \n",
       "37146        2  \n",
       "13355        4  \n",
       "\n",
       "[5 rows x 292 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_dataset_shuffled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1b61e933",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = combined_dataset_shuffled.drop([\"_adval_label_\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e7b3c500",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.drop([\"id\", \"timestamp\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7e4f0215",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = combined_dataset_shuffled[\"_adval_label_\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e385c620",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ab6cc378e48a4f22a1797b813ca653eb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = []\n",
    "\n",
    "for fold, (train, test) in tqdm([*enumerate(cv.split(X, y))]):\n",
    "    classifier.fit(X.iloc[train], y.iloc[train])\n",
    "\n",
    "    y_pred = classifier.predict_proba(X.iloc[test])[:, 1]\n",
    "\n",
    "    predictions.extend(y_pred.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a1366f3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_dataset_shuffled[\"preds\"] = predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "477edc92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30471, 293)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainset = combined_dataset_shuffled[combined_dataset_shuffled[\"_adval_label_\"] == 0.0]\n",
    "\n",
    "trainset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c918c7de",
   "metadata": {},
   "outputs": [],
   "source": [
    "i = trainset[\"preds\"].argsort()\n",
    "\n",
    "sorted_train = trainset.iloc[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "dcc850a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23837</th>\n",
       "      <td>23840</td>\n",
       "      <td>102.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>9.991301e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9375</th>\n",
       "      <td>9378</td>\n",
       "      <td>35.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9.991896e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14583</th>\n",
       "      <td>14586</td>\n",
       "      <td>31.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1964.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.994344e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15790</th>\n",
       "      <td>15793</td>\n",
       "      <td>66.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1935.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9.998305e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21768</th>\n",
       "      <td>21771</td>\n",
       "      <td>38.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1972.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9.999312e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30471 rows × 293 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  full_sq  life_sq  floor  max_floor  material  build_year  \\\n",
       "5592    5595     37.0     19.0    5.0        NaN       NaN         NaN   \n",
       "2546    2549     38.0     18.0    7.0        NaN       NaN         NaN   \n",
       "1340    1343     38.0     19.0   16.0        NaN       NaN         NaN   \n",
       "5072    5075     34.0      NaN   12.0        NaN       NaN         NaN   \n",
       "5866    5869     38.0      NaN   11.0        NaN       NaN         NaN   \n",
       "...      ...      ...      ...    ...        ...       ...         ...   \n",
       "23837  23840    102.0      NaN   12.0       25.0       1.0         NaN   \n",
       "9375    9378     35.0     20.0   11.0        NaN       NaN         NaN   \n",
       "14583  14586     31.0     21.0    1.0        9.0       5.0      1964.0   \n",
       "15790  15793     66.0     48.0    5.0        5.0       2.0      1935.0   \n",
       "21768  21771     38.0     23.0    3.0       14.0       1.0      1972.0   \n",
       "\n",
       "       num_room  kitch_sq  state  ...  radiation_raion  \\\n",
       "5592        NaN       NaN    NaN  ...                1   \n",
       "2546        NaN       NaN    NaN  ...                0   \n",
       "1340        NaN       NaN    NaN  ...                1   \n",
       "5072        NaN       NaN    NaN  ...                0   \n",
       "5866        NaN       NaN    NaN  ...                0   \n",
       "...         ...       ...    ...  ...              ...   \n",
       "23837       3.0       1.0    NaN  ...                0   \n",
       "9375        NaN       NaN    NaN  ...                1   \n",
       "14583       1.0       5.0    2.0  ...                1   \n",
       "15790       3.0       6.0    3.0  ...                0   \n",
       "21768       2.0       6.0    3.0  ...                1   \n",
       "\n",
       "       railroad_terminal_raion  big_market_raion  nuclear_reactor_raion  \\\n",
       "5592                         0                 0                      0   \n",
       "2546                         1                 0                      0   \n",
       "1340                         0                 0                      0   \n",
       "5072                         0                 1                      0   \n",
       "5866                         0                 0                      0   \n",
       "...                        ...               ...                    ...   \n",
       "23837                        0                 0                      0   \n",
       "9375                         0                 0                      0   \n",
       "14583                        0                 0                      0   \n",
       "15790                        0                 0                      0   \n",
       "21768                        0                 0                      0   \n",
       "\n",
       "       detention_facility_raion  water_1line  big_road1_1line  railroad_1line  \\\n",
       "5592                          0            0                0               0   \n",
       "2546                          0            0                0               0   \n",
       "1340                          0            0                0               0   \n",
       "5072                          0            0                0               0   \n",
       "5866                          0            0                0               0   \n",
       "...                         ...          ...              ...             ...   \n",
       "23837                         0            0                0               0   \n",
       "9375                          0            0                0               0   \n",
       "14583                         0            0                0               0   \n",
       "15790                         1            0                0               0   \n",
       "21768                         0            0                0               0   \n",
       "\n",
       "       ecology         preds  \n",
       "5592         2  4.897290e-07  \n",
       "2546         2  5.077677e-07  \n",
       "1340         0  5.474540e-07  \n",
       "5072         4  5.633994e-07  \n",
       "5866         0  5.665751e-07  \n",
       "...        ...           ...  \n",
       "23837        4  9.991301e-01  \n",
       "9375         2  9.991896e-01  \n",
       "14583        1  9.994344e-01  \n",
       "15790        2  9.998305e-01  \n",
       "21768        2  9.999312e-01  \n",
       "\n",
       "[30471 rows x 293 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4d74c90f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>full_sq</th>\n",
       "      <th>life_sq</th>\n",
       "      <th>max_floor</th>\n",
       "      <th>preds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30359</th>\n",
       "      <td>81.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.853813</td>\n",
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       "      <td>64.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.540079</td>\n",
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       "    <tr>\n",
       "      <th>21634</th>\n",
       "      <td>115.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.514691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19519</th>\n",
       "      <td>66.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.628838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30201</th>\n",
       "      <td>64.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.714351</td>\n",
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       "    <tr>\n",
       "      <th>22552</th>\n",
       "      <td>55.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.546772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>237 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       full_sq  life_sq  max_floor     preds\n",
       "30359     81.0     81.0       22.0  0.853813\n",
       "29068     50.0     50.0        4.0  0.608003\n",
       "28132     90.0      1.0        1.0  0.556087\n",
       "29003     85.0     49.0       25.0  0.506222\n",
       "30080     69.0      NaN       15.0  0.618344\n",
       "...        ...      ...        ...       ...\n",
       "28891     64.0     64.0       17.0  0.540079\n",
       "21634    115.0     62.0       23.0  0.514691\n",
       "19519     66.0     66.0       16.0  0.628838\n",
       "30201     64.0      NaN        1.0  0.714351\n",
       "22552     55.0      0.0        0.0  0.546772\n",
       "\n",
       "[237 rows x 4 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainset[trainset[\"preds\"] >= 0.5][[\"full_sq\", \"life_sq\", \"max_floor\", \"preds\"]]"
   ]
  }
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