IIS/assets/mlflow/research.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "import numpy\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler, OrdinalEncoder, OneHotEncoder\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from catboost import CatBoostRegressor\n",
    "\n",
    "from sklearn.metrics import mean_absolute_percentage_error, mean_absolute_error, mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 547701 entries, 313199 to 690900\n",
      "Data columns (total 13 columns):\n",
      " #   Column         Non-Null Count   Dtype   \n",
      "---  ------         --------------   -----   \n",
      " 0   price          547701 non-null  int64   \n",
      " 1   date           547701 non-null  object  \n",
      " 2   time           547701 non-null  object  \n",
      " 3   geo_lat        547701 non-null  float32 \n",
      " 4   geo_lon        547701 non-null  float32 \n",
      " 5   region         547701 non-null  category\n",
      " 6   building_type  547701 non-null  category\n",
      " 7   level          547701 non-null  int8    \n",
      " 8   levels         547701 non-null  int8    \n",
      " 9   rooms          547701 non-null  int8    \n",
      " 10  area           547701 non-null  float16 \n",
      " 11  kitchen_area   547701 non-null  float16 \n",
      " 12  object_type    547701 non-null  category\n",
      "dtypes: category(3), float16(2), float32(2), int64(1), int8(3), object(2)\n",
      "memory usage: 26.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_pickle('data/clean_data.pkl').sample(frac=0.1, random_state = 2) # Уменьшаем размер чтобы модель быстрее обучалась на лекции\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={'price': 'target'})\n",
    "df = df.drop(columns=['date', 'time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>target</th>\n",
       "      <th>geo_lat</th>\n",
       "      <th>geo_lon</th>\n",
       "      <th>region</th>\n",
       "      <th>building_type</th>\n",
       "      <th>level</th>\n",
       "      <th>levels</th>\n",
       "      <th>rooms</th>\n",
       "      <th>area</th>\n",
       "      <th>kitchen_area</th>\n",
       "      <th>object_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr>\n",
       "      <th>5188085</th>\n",
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       "      <td>4189</td>\n",
       "      <td>3</td>\n",
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       "      <td>38.00000</td>\n",
       "      <td>11.000000</td>\n",
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       "      <td>5</td>\n",
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       "      <td>66.37500</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>39.273037</td>\n",
       "      <td>2072</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>89.50000</td>\n",
       "      <td>14.203125</td>\n",
       "      <td>1</td>\n",
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       "<p>547701 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          target    geo_lat    geo_lon region building_type  level  levels  \\\n",
       "313199   4999999  59.958458  30.215530   2661             3      8      13   \n",
       "2437764  2150000  45.072674  41.936996   2900             3      5       5   \n",
       "4949072  8600000  59.939358  30.437069   2661             2     11      22   \n",
       "4109465  5100000  59.740479  30.569540   2661             1      2       9   \n",
       "2187702  3470000  56.324062  44.005390   2871             2     11      26   \n",
       "...          ...        ...        ...    ...           ...    ...     ...   \n",
       "5188085  2300000  57.750603  40.866467   4189             3      2       3   \n",
       "4542014  6700000  55.911720  37.737419     81             3      2       5   \n",
       "3306731  3850000  51.704510  39.273037   2072             2     10      18   \n",
       "520293   1878885  54.943577  82.958862   9654             1      1      10   \n",
       "690900   4097350  59.882702  30.451246   2661             2      6      23   \n",
       "\n",
       "         rooms      area  kitchen_area object_type  \n",
       "313199       1  36.00000      7.199219           1  \n",
       "2437764      1  52.00000     15.000000           1  \n",
       "4949072      1  37.09375      9.796875           1  \n",
       "4109465      3  74.50000      9.500000           1  \n",
       "2187702      2  54.00000      8.000000          11  \n",
       "...        ...       ...           ...         ...  \n",
       "5188085      1  38.00000     11.000000           1  \n",
       "4542014      2  66.37500      8.000000           1  \n",
       "3306731      3  89.50000     14.203125           1  \n",
       "520293       3  87.75000     12.921875          11  \n",
       "690900       1  36.09375     16.203125          11  \n",
       "\n",
       "[547701 rows x 11 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df.drop('target', axis=1), df['target'], test_size=0.25, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geo_lat            56.327686\n",
       "geo_lon            43.928062\n",
       "region           2871.000000\n",
       "building_type       1.000000\n",
       "level               8.000000\n",
       "levels             10.000000\n",
       "rooms               2.000000\n",
       "area               56.000000\n",
       "kitchen_area        8.500000\n",
       "object_type         1.000000\n",
       "Name: 879487, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['region', 'building_type', 'object_type']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_features = X_train.select_dtypes(include=['category','object']).columns.to_list()\n",
    "cat_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['geo_lat', 'geo_lon', 'level', 'levels', 'rooms', 'area', 'kitchen_area']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_features = X_train.select_dtypes(include=['number']).columns.to_list()\n",
    "num_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://scikit-learn.org/stable/api/sklearn.preprocessing.html - разные способы кодирования и скалирования"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_scaler = StandardScaler()\n",
    "l_encoder = OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=99999999) # unknown_value нужно выбирать с умом\n",
    "regressor = CatBoostRegressor()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Column transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Для удобной работы со столбцами\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', s_scaler, num_features),  # преобразования для числовых признаков\n",
    "        ('cat', l_encoder, cat_features), # преобразования для категориальных признаков\n",
    "    ],\n",
    "    remainder='drop' ) # Удаляем столбцы, которые не затронуты преобразования"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#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>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
       "                                                  [&#x27;geo_lat&#x27;, &#x27;geo_lon&#x27;,\n",
       "                                                   &#x27;level&#x27;, &#x27;levels&#x27;, &#x27;rooms&#x27;,\n",
       "                                                   &#x27;area&#x27;, &#x27;kitchen_area&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                                 unknown_value=99999999),\n",
       "                                                  [&#x27;region&#x27;, &#x27;building_type&#x27;,\n",
       "                                                   &#x27;object_type&#x27;])])),\n",
       "                (&#x27;model&#x27;,\n",
       "                 &lt;catboost.core.CatBoostRegressor object at 0x7be8319969b0&gt;)])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
       "                                                  [&#x27;geo_lat&#x27;, &#x27;geo_lon&#x27;,\n",
       "                                                   &#x27;level&#x27;, &#x27;levels&#x27;, &#x27;rooms&#x27;,\n",
       "                                                   &#x27;area&#x27;, &#x27;kitchen_area&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                                 unknown_value=99999999),\n",
       "                                                  [&#x27;region&#x27;, &#x27;building_type&#x27;,\n",
       "                                                   &#x27;object_type&#x27;])])),\n",
       "                (&#x27;model&#x27;,\n",
       "                 &lt;catboost.core.CatBoostRegressor object at 0x7be8319969b0&gt;)])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
       "                                 [&#x27;geo_lat&#x27;, &#x27;geo_lon&#x27;, &#x27;level&#x27;, &#x27;levels&#x27;,\n",
       "                                  &#x27;rooms&#x27;, &#x27;area&#x27;, &#x27;kitchen_area&#x27;]),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                unknown_value=99999999),\n",
       "                                 [&#x27;region&#x27;, &#x27;building_type&#x27;, &#x27;object_type&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;geo_lat&#x27;, &#x27;geo_lon&#x27;, &#x27;level&#x27;, &#x27;levels&#x27;, &#x27;rooms&#x27;, &#x27;area&#x27;, &#x27;kitchen_area&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;region&#x27;, &#x27;building_type&#x27;, &#x27;object_type&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;, unknown_value=99999999)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CatBoostRegressor</label><div class=\"sk-toggleable__content\"><pre>&lt;catboost.core.CatBoostRegressor object at 0x7be8319969b0&gt;</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num', StandardScaler(),\n",
       "                                                  ['geo_lat', 'geo_lon',\n",
       "                                                   'level', 'levels', 'rooms',\n",
       "                                                   'area', 'kitchen_area']),\n",
       "                                                 ('cat',\n",
       "                                                  OrdinalEncoder(handle_unknown='use_encoded_value',\n",
       "                                                                 unknown_value=99999999),\n",
       "                                                  ['region', 'building_type',\n",
       "                                                   'object_type'])])),\n",
       "                ('model',\n",
       "                 <catboost.core.CatBoostRegressor object at 0x7be8319969b0>)])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "pipeline = Pipeline(steps=[('preprocessor', preprocessor), \n",
    "                           ('model', regressor)])\n",
    "\n",
    "pipeline.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "        model_params = {\n",
    "            \"geo_lat\":56.327686,\n",
    "            \"geo_lon\":43.928062,\n",
    "            \"region\":2871.000000,\n",
    "            \"building_type\":1.000000,\n",
    "            \"level\":8.000000,\n",
    "            \"levels\":10.000000,\n",
    "            \"rooms\":2.000000,\n",
    "            \"area\":56.000000,\n",
    "            \"kitchen_area\":8.500000,\n",
    "            \"object_type\":1.000000,\n",
    "            \"floor_level\":0,\n",
    "            }\n",
    "        df_pred = pd.DataFrame(model_params, index=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3414175.89042869])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "pipeline.predict(df_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mae': 1447931.3425270966,\n",
       " 'mape': 1.6294525363466488e+18,\n",
       " 'mse': 281898017343454.56}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = pipeline.predict(X_test) \n",
    "\n",
    "metrics = {}\n",
    "metrics[\"mae\"] = mean_absolute_error(y_test, predictions)   \n",
    "metrics[\"mape\"] = mean_absolute_percentage_error(y_test, predictions)\n",
    "metrics[\"mse\"] = mean_squared_error(y_test, predictions)\n",
    "\n",
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Работаем с MLflow локально\n",
    "TRACKING_SERVER_HOST = \"127.0.0.1\"\n",
    "TRACKING_SERVER_PORT = 5000\n",
    "\n",
    "registry_uri = f\"http://{TRACKING_SERVER_HOST}:{TRACKING_SERVER_PORT}\"\n",
    "tracking_uri = f\"http://{TRACKING_SERVER_HOST}:{TRACKING_SERVER_PORT}\"\n",
    "\n",
    "mlflow.set_tracking_uri(tracking_uri)   \n",
    "mlflow.set_registry_uri(registry_uri)   \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# название тестового эксперимента, запуска (run) внутри него, имени, под которым модель будет регистрироваться\n",
    "EXPERIMENT_NAME = \"estate_project\"\n",
    "RUN_NAME = \"baseline model\"\n",
    "REGISTRY_MODEL_NAME = \"estate_model_rf\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Логируем вручную"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Обязательно логируем сигнатуру модели и пример входных данных. Подготовим их\n",
    "from mlflow.models import infer_signature\n",
    "\n",
    "signature =  infer_signature(model_input = X_train.head(5))\n",
    "input_example = X_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Будем логировать requirements и артефакт - текстовый файл\n",
    "req_file = 'requirements.txt'\n",
    "art = 'comment.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Параметры, котороые будут залогированы, можем задавать вручную или полностью взять из модели\n",
    "#params_dict = {'n_estimators': 10, 'max_depth': 10}\n",
    "params_dict = pipeline.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Когда создаем новый эксперимент, то: \n",
    "experiment_id = mlflow.create_experiment(EXPERIMENT_NAME)\n",
    "\n",
    "# Впоследствии. чтобы добавлять запуски в этот же эксепримент мы должны получить его id:\n",
    "#experiment_id = mlflow.get_experiment_by_name(EXPERIMENT_NAME).experiment_id\n",
    "\n",
    "with mlflow.start_run(run_name=RUN_NAME, experiment_id=experiment_id) as run:\n",
    "    # получаем уникальный идентификатор запуска эксперимента\n",
    "    run_id = run.info.run_id \n",
    "    mlflow.sklearn.log_model(pipeline, \n",
    "                             artifact_path=\"models\",\n",
    "                             signature=signature,\n",
    "                             input_example=input_example,\n",
    "                             pip_requirements=req_file\n",
    "                             )\n",
    "    mlflow.log_metrics(metrics)\n",
    "    mlflow.log_artifact(art)\n",
    "    mlflow.log_params(params_dict)\n",
    "\n",
    "run = mlflow.get_run(run_id) \n",
    "assert (run.info.status =='FINISHED')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Удаление runs, experiments\n",
    "\n",
    "Использовать осторожно"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_id = mlflow.get_experiment_by_name(EXPERIMENT_NAME).experiment_id\n",
    "#mlflow.delete_experiment(experiment_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlflow.search_runs(\n",
    "    #experiment_ids=[experiment_id],\n",
    "    experiment_names=[EXPERIMENT_NAME],\n",
    "    # filter_string='status = \"FAILED\"'\n",
    "    #filter_string='metrics.mae > 1'\n",
    "    \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#mlflow.delete_run('74d2a7a40c07413c9cf65df841164356')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Автологирование\n",
    "После включения будет срабатывать на каждом обучении модели (на методе fit()).\n",
    "\n",
    "Есть плюсы, есть и минусы. Предлагается сделать прогон и сравнить с результатами вручную "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlflow.sklearn.autolog()\n",
    "\n",
    "with mlflow.start_run(run_name='auto', experiment_id=experiment_id) as run:\n",
    "    pipeline.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Отключаем автологирование\n",
    "mlflow.sklearn.autolog(disable=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model #2\n",
    "Обучим вторую \"маленькую\" модель\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor2 = RandomForestRegressor(n_estimators=10, max_depth=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline(steps=[('preprocessor', preprocessor), \n",
    "                           ('model', regressor2)])\n",
    "\n",
    "pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = pipeline.predict(X_test) \n",
    "metrics = {}\n",
    "metrics[\"mae\"] = mean_absolute_error(y_test, predictions)   \n",
    "metrics[\"mape\"] = mean_absolute_percentage_error(y_test, predictions)\n",
    "metrics[\"mse\"] = mean_squared_error(y_test, predictions)\n",
    "\n",
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !!! Проверить название прогона а также все логируемые параметры и артефакты, что они соответствуют второй \"маленькой\" модели. \n",
    "\n",
    "\n",
    "RUN_NAME = 'smaller_model'\n",
    "\n",
    "experiment_id = mlflow.get_experiment_by_name(EXPERIMENT_NAME).experiment_id\n",
    "\n",
    "with mlflow.start_run(run_name=RUN_NAME, experiment_id=experiment_id) as run:\n",
    "    # получаем уникальный идентификатор запуска эксперимента\n",
    "    run_id = run.info.run_id \n",
    "    mlflow.sklearn.log_model(pipeline, \n",
    "                             artifact_path=\"models\",\n",
    "                             signature=signature,\n",
    "                             input_example=input_example,\n",
    "                             pip_requirements=req_file\n",
    "                             )\n",
    "    mlflow.log_metrics(metrics)\n",
    "    mlflow.log_artifact(art)\n",
    "    mlflow.log_params(pipeline.get_params())\n",
    "\n",
    "run = mlflow.get_run(run_id) \n",
    "assert (run.info.status =='FINISHED')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# No model\n",
    "# Логировать можно только артефакты, без модели. Например, залогироавть графики после этапа EDA\n",
    "\n",
    "RUN_NAME = 'no_model'\n",
    "experiment_id = mlflow.get_experiment_by_name(EXPERIMENT_NAME).experiment_id\n",
    "\n",
    "with mlflow.start_run(run_name=RUN_NAME, experiment_id=experiment_id) as run:\n",
    "    run_id = run.info.run_id \n",
    "    mlflow.log_artifact(art)\n",
    "\n",
    "\n",
    "run = mlflow.get_run(run_id) \n",
    "assert (run.info.status =='FINISHED')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_id = '06fa7ec1f1b74aedb3509c88dc4ee1c0' # Указываем run id\n",
    "mlflow.register_model(f\"runs:/{run_id}/models\", REGISTRY_MODEL_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Можно регистрировать сразу при создании прогона\n",
    "\n",
    "experiment_id = mlflow.get_experiment_by_name(EXPERIMENT_NAME).experiment_id\n",
    "\n",
    "with mlflow.start_run(run_name='register_at_run', experiment_id=experiment_id) as run:\n",
    "    # получаем уникальный идентификатор запуска эксперимента\n",
    "    run_id = run.info.run_id \n",
    "    mlflow.sklearn.log_model(pipeline, \n",
    "                             artifact_path=\"models\",\n",
    "                             signature=signature,\n",
    "                             input_example=input_example,\n",
    "                             pip_requirements=req_file,\n",
    "                             registered_model_name = REGISTRY_MODEL_NAME # Указываем для какой модели регистрируем\n",
    "                             )\n",
    "    mlflow.log_metrics(metrics)\n",
    "    mlflow.log_artifact(art)\n",
    "    mlflow.log_params(pipeline.get_params())\n",
    "\n",
    "run = mlflow.get_run(run_id) \n",
    "assert (run.info.status =='FINISHED')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Можно найти зарегистрированные модели\n",
    "model_reg = mlflow.search_registered_models()\n",
    "model_reg[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model_name = REGISTRY_MODEL_NAME\n",
    "model_version = 1\n",
    "\n",
    "model_loaded = mlflow.sklearn.load_model(model_uri=f\"models:/{model_name}/{model_version}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_loaded.predict(X_test.iloc[0:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import QuantileTransformer, SplineTransformer, PolynomialFeatures, MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_sklearn = X_train.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PolynomialFeatures\n",
    "Создает полином степени `degree` из указанных признаков\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "pf = PolynomialFeatures(degree=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pf.fit_transform(X_train_sklearn[['area','kitchen_area']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SplineTransformer\n",
    "Cоздаёт новую матрицу признаков, состоящую из сплайнов порядка degree. Количество сгенерированных сплайнов равно `n_splines=n_knots + degree - 1` для каждого признака, где\n",
    "\n",
    "`n_knots` определяет количество узлов (точек, в которых сопрягаются сплайны) для каждого признака. \n",
    "\n",
    "`degree` определяет порядок полинома, используемого для построения сплайнов. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp = SplineTransformer(n_knots=3, degree=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp.fit_transform(X_train_sklearn[['area']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### QuantileTransformer\n",
    "Этот метод преобразует признаки, чтобы они распределялись равномерно или нормально — так данные меньше подвергаются влиянию выбросов. Преобразование применяется к каждому признаку независимо. Идея метода такова: оценить функцию распределения признака, чтобы преобразовать исходные значения в равномерное или нормальное распределение. \n",
    "\n",
    "`output_distribution='uniform'` или\n",
    "`output_distribution='normal'` соответственно\n",
    "\n",
    "\n",
    "Пример использования: если у вас есть данные о доходах с широким диапазоном значений, квантильное преобразование сделает их более сопоставимыми и устойчивыми к выбросам."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "qt = QuantileTransformer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "qt.fit_transform(X_train_sklearn[['area']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Объединяем в ColumnTransformer и создаем Pipeline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "pf = PolynomialFeatures(degree=2)\n",
    "qt = QuantileTransformer()\n",
    "sp = SplineTransformer(n_knots=3, degree=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Значения преобразованных признаков нужно отскейлить, поэтому создаем pipeline из двух шагов - преобразование и скейлинг\n",
    "pf_pipeline = Pipeline(steps=[\n",
    "    ('poly', pf),\n",
    "    ('scale', StandardScaler())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessor_sklearn = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', s_scaler, num_features),  # преобразования для числовых признаков\n",
    "        ('cat', l_encoder, cat_features), # преобразования для категориальных признаков\n",
    "        ('quantile', qt,num_features),\n",
    "        ('poly', pf_pipeline, ['area', 'kitchen_area']), # В преобразования добавляем созданный ранее pipeline\n",
    "        ('spline', sp, ['area'])\n",
    "    ],\n",
    "    remainder='drop',\n",
    "    ) # Удаляем столбцы, которые не затронуты преобразования"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Посмотрим что из себя теперь представляет датафрейм"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "## не влезаем в float64 в полиномальном преобразовании. Использовать его нужно с умом!\n",
    "X_train_sklearn[['area', 'kitchen_area']] = X_train_sklearn[['area', 'kitchen_area']].astype('float128')\n",
    "X_train_sklearn[['area', 'kitchen_area']] = X_train_sklearn[['area', 'kitchen_area']].astype('float128')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_sklearn_raw = preprocessor_sklearn.fit_transform(X_train_sklearn)\n",
    "X_train_sklearn = pd.DataFrame(X_train_sklearn_raw, columns=preprocessor_sklearn.get_feature_names_out())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num__geo_lat</th>\n",
       "      <th>num__geo_lon</th>\n",
       "      <th>num__level</th>\n",
       "      <th>num__levels</th>\n",
       "      <th>num__rooms</th>\n",
       "      <th>num__area</th>\n",
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       "      <th>cat__region</th>\n",
       "      <th>cat__building_type</th>\n",
       "      <th>cat__object_type</th>\n",
       "      <th>quantile__geo_lat</th>\n",
       "      <th>quantile__geo_lon</th>\n",
       "      <th>quantile__level</th>\n",
       "      <th>quantile__levels</th>\n",
       "      <th>quantile__rooms</th>\n",
       "      <th>quantile__area</th>\n",
       "      <th>quantile__kitchen_area</th>\n",
       "      <th>poly__1</th>\n",
       "      <th>poly__area</th>\n",
       "      <th>poly__kitchen_area</th>\n",
       "      <th>poly__area^2</th>\n",
       "      <th>poly__area kitchen_area</th>\n",
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       "      <td>0.359235</td>\n",
       "      <td>-0.214789</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>0.063735</td>\n",
       "      <td>-0.186285</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.766257</td>\n",
       "      <td>0.511028</td>\n",
       "      <td>0.717217</td>\n",
       "      <td>0.536537</td>\n",
       "      <td>0.600601</td>\n",
       "      <td>0.623624</td>\n",
       "      <td>0.374875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.063735</td>\n",
       "      <td>-0.186285</td>\n",
       "      <td>-0.010002</td>\n",
       "      <td>-0.132188</td>\n",
       "      <td>-0.002792</td>\n",
       "      <td>0.155806</td>\n",
       "      <td>0.666179</td>\n",
       "      <td>0.178013</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.433673</td>\n",
       "      <td>-0.246529</td>\n",
       "      <td>-0.367718</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-0.114293</td>\n",
       "      <td>-0.186285</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.297142</td>\n",
       "      <td>0.867999</td>\n",
       "      <td>0.522022</td>\n",
       "      <td>0.386887</td>\n",
       "      <td>0.600601</td>\n",
       "      <td>0.541542</td>\n",
       "      <td>0.374875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.114293</td>\n",
       "      <td>-0.186285</td>\n",
       "      <td>-0.017375</td>\n",
       "      <td>-0.169370</td>\n",
       "      <td>-0.002792</td>\n",
       "      <td>0.156921</td>\n",
       "      <td>0.666275</td>\n",
       "      <td>0.176803</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>-0.804077</td>\n",
       "      <td>-0.650371</td>\n",
       "      <td>0.702788</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>1.365441</td>\n",
       "      <td>1.501833</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.193143</td>\n",
       "      <td>0.114753</td>\n",
       "      <td>0.309810</td>\n",
       "      <td>0.741742</td>\n",
       "      <td>0.600601</td>\n",
       "      <td>0.961367</td>\n",
       "      <td>0.984535</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.365441</td>\n",
       "      <td>1.501833</td>\n",
       "      <td>0.068438</td>\n",
       "      <td>1.570163</td>\n",
       "      <td>0.008616</td>\n",
       "      <td>0.147820</td>\n",
       "      <td>0.665159</td>\n",
       "      <td>0.187011</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>-1.101815</td>\n",
       "      <td>-0.044608</td>\n",
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       "      <td>1.175911</td>\n",
       "      <td>0.553789</td>\n",
       "      <td>-0.142544</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.908036</td>\n",
       "      <td>0.075725</td>\n",
       "      <td>0.604605</td>\n",
       "      <td>0.645646</td>\n",
       "      <td>0.867367</td>\n",
       "      <td>0.841842</td>\n",
       "      <td>0.436436</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.553789</td>\n",
       "      <td>-0.142544</td>\n",
       "      <td>0.014463</td>\n",
       "      <td>-0.002742</td>\n",
       "      <td>-0.002649</td>\n",
       "      <td>0.152767</td>\n",
       "      <td>0.665860</td>\n",
       "      <td>0.181370</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>410775 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        num__geo_lat  num__geo_lon  num__level  num__levels  num__rooms  \\\n",
       "0           0.495902     -0.449742    0.359235    -0.214789    0.253413   \n",
       "1           0.177806      1.433673   -0.246529    -0.367718    0.253413   \n",
       "...              ...           ...         ...          ...         ...   \n",
       "410773     -0.748366     -0.804077   -0.650371     0.702788    0.253413   \n",
       "410774      1.257769     -1.101815   -0.044608     0.091070    1.175911   \n",
       "\n",
       "        num__area  num__kitchen_area  cat__region  cat__building_type  \\\n",
       "0        0.063735          -0.186285         20.0                 1.0   \n",
       "1       -0.114293          -0.186285         70.0                 1.0   \n",
       "...           ...                ...          ...                 ...   \n",
       "410773   1.365441           1.501833         52.0                 3.0   \n",
       "410774   0.553789          -0.142544         14.0                 1.0   \n",
       "\n",
       "        cat__object_type  quantile__geo_lat  quantile__geo_lon  \\\n",
       "0                    0.0           0.766257           0.511028   \n",
       "1                    0.0           0.297142           0.867999   \n",
       "...                  ...                ...                ...   \n",
       "410773               0.0           0.193143           0.114753   \n",
       "410774               0.0           0.908036           0.075725   \n",
       "\n",
       "        quantile__level  quantile__levels  quantile__rooms  quantile__area  \\\n",
       "0              0.717217          0.536537         0.600601        0.623624   \n",
       "1              0.522022          0.386887         0.600601        0.541542   \n",
       "...                 ...               ...              ...             ...   \n",
       "410773         0.309810          0.741742         0.600601        0.961367   \n",
       "410774         0.604605          0.645646         0.867367        0.841842   \n",
       "\n",
       "        quantile__kitchen_area  poly__1  poly__area  poly__kitchen_area  \\\n",
       "0                     0.374875      0.0    0.063735           -0.186285   \n",
       "1                     0.374875      0.0   -0.114293           -0.186285   \n",
       "...                        ...      ...         ...                 ...   \n",
       "410773                0.984535      0.0    1.365441            1.501833   \n",
       "410774                0.436436      0.0    0.553789           -0.142544   \n",
       "\n",
       "        poly__area^2  poly__area kitchen_area  poly__kitchen_area^2  \\\n",
       "0          -0.010002                -0.132188             -0.002792   \n",
       "1          -0.017375                -0.169370             -0.002792   \n",
       "...              ...                      ...                   ...   \n",
       "410773      0.068438                 1.570163              0.008616   \n",
       "410774      0.014463                -0.002742             -0.002649   \n",
       "\n",
       "        spline__area_sp_0  spline__area_sp_1  spline__area_sp_2  \\\n",
       "0                0.155806           0.666179           0.178013   \n",
       "1                0.156921           0.666275           0.176803   \n",
       "...                   ...                ...                ...   \n",
       "410773           0.147820           0.665159           0.187011   \n",
       "410774           0.152767           0.665860           0.181370   \n",
       "\n",
       "        spline__area_sp_3  spline__area_sp_4  \n",
       "0                0.000002                0.0  \n",
       "1                0.000001                0.0  \n",
       "...                   ...                ...  \n",
       "410773           0.000010                0.0  \n",
       "410774           0.000004                0.0  \n",
       "\n",
       "[410775 rows x 28 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Удобно использовать для отображения всех строк\\столбцов в DataFrame\n",
    "with pd.option_context('display.max_rows', 5, 'display.max_columns', None):\n",
    "    display (X_train_sklearn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Создаем пайплайн с препроцессингом и моделью"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline_sklearn = Pipeline(steps=[\n",
    "    ('transform', preprocessor_sklearn),\n",
    "    ('model', regressor)\n",
    "])\n",
    "\n",
    "model_sklearn = pipeline_sklearn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model_sklearn.predict(X_test) \n",
    "metrics = {}\n",
    "metrics[\"mae\"] = mean_absolute_error(y_test, predictions)   \n",
    "metrics[\"mape\"] = mean_absolute_percentage_error(y_test, predictions)\n",
    "metrics[\"mse\"] = mean_squared_error(y_test, predictions)\n",
    "\n",
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_id = mlflow.get_experiment_by_name(EXPERIMENT_NAME).experiment_id\n",
    "RUN_NAME = 'fe_sklearn'\n",
    "\n",
    "with mlflow.start_run(run_name=RUN_NAME, experiment_id=experiment_id) as run:\n",
    "    # получаем уникальный идентификатор запуска эксперимента\n",
    "    run_id = run.info.run_id \n",
    "    mlflow.sklearn.log_model(model_sklearn, \n",
    "                             artifact_path=\"models\",\n",
    "                             signature=signature,\n",
    "                             input_example=input_example,\n",
    "                             pip_requirements=req_file\n",
    "                             )\n",
    "    mlflow.log_metrics(metrics)\n",
    "    mlflow.log_artifact(art)\n",
    "    mlflow.log_params(model_sklearn.get_params())\n",
    "\n",
    "run = mlflow.get_run(run_id) \n",
    "assert (run.info.status =='FINISHED')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Autofeat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from autofeat import AutoFeatRegressor\n",
    "transformations = [\"1/\", \"exp\", \"log\", \"abs\", \"sqrt\", \"^2\", \"^3\", \"1+\", \"1-\", \"sin\", \"cos\", \"exp-\", \"2^\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "afreg = AutoFeatRegressor(verbose=1, feateng_steps=2, max_gb=8, transformations=[\"log\", \"sqrt\"],feateng_cols=num_features)\n",
    "X_train_arf = afreg.fit_transform(X_train,y_train)\n",
    "X_train_arf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Создаем обертку, в которой добавляем метод get_feature_names_out() для получения названий признаков\n",
    "import numpy as np\n",
    "\n",
    "class AutoFeatWrapper():\n",
    "    def __init__(self, feateng_cols, feateng_steps=1, max_gb=16, transformations=[\"1/\", \"exp\", \"log\"], n_jobs=-1, verbose=1):\n",
    "        self.feateng_cols = feateng_cols\n",
    "        self.feateng_steps = feateng_steps\n",
    "        self.max_gb = max_gb\n",
    "        self.transformations = transformations\n",
    "        self.n_jobs = n_jobs\n",
    "        self.afreg = AutoFeatRegressor(feateng_cols=self.feateng_cols,\n",
    "                                     feateng_steps=self.feateng_steps,\n",
    "                                     max_gb=self.max_gb,\n",
    "                                     transformations=self.transformations,\n",
    "                                     n_jobs=self.n_jobs)\n",
    "        \n",
    "    def fit(self, X, y=None):\n",
    "        self.afreg.fit(X, y)\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X):\n",
    "        return self.afreg.transform(X)\n",
    "    \n",
    "    def get_feature_names_out(self, input_features=None):\n",
    "        # Преобразуем данные и возвращаем имена фичей из DataFrame\n",
    "        transformed_X = self.afreg.transform(pd.DataFrame(np.zeros((1, len(self.feateng_cols))), columns=self.feateng_cols))\n",
    "        return transformed_X.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "afreg_pipeline = Pipeline(steps=[\n",
    "    ('autofeat', AutoFeatWrapper( feateng_steps=2, max_gb=16, transformations=[\"log\", \"sqrt\"],feateng_cols=num_features)),\n",
    "    ('scaler', StandardScaler()),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessor_afr = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', s_scaler, num_features),  # преобразования для числовых признаков\n",
    "        ('cat', l_encoder, cat_features), # преобразования для категориальных признаков\n",
    "        ('afr', afreg_pipeline, num_features), # преобразования autofeat\n",
    "    ],\n",
    "    remainder='drop', # Удаляем столбцы, которые не затронуты преобразованиями\n",
    "    ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_afr_raw =  preprocessor_afr.fit_transform(X_train,y_train)\n",
    "X_train_afr = pd.DataFrame(X_train_afr_raw, columns=preprocessor_afr.get_feature_names_out())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "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>num__geo_lat</th>\n",
       "      <th>num__geo_lon</th>\n",
       "      <th>num__level</th>\n",
       "      <th>num__levels</th>\n",
       "      <th>num__rooms</th>\n",
       "      <th>num__area</th>\n",
       "      <th>num__kitchen_area</th>\n",
       "      <th>cat__region</th>\n",
       "      <th>cat__building_type</th>\n",
       "      <th>cat__object_type</th>\n",
       "      <th>afr__geo_lat</th>\n",
       "      <th>afr__geo_lon</th>\n",
       "      <th>afr__level</th>\n",
       "      <th>afr__levels</th>\n",
       "      <th>afr__rooms</th>\n",
       "      <th>afr__area</th>\n",
       "      <th>afr__kitchen_area</th>\n",
       "      <th>afr__area*rooms</th>\n",
       "      <th>afr__area*geo_lon</th>\n",
       "      <th>afr__levels*rooms</th>\n",
       "      <th>afr__area*kitchen_area</th>\n",
       "      <th>afr__sqrt(area)*geo_lat</th>\n",
       "      <th>afr__sqrt(area)*log(level)</th>\n",
       "      <th>afr__kitchen_area*log(level)</th>\n",
       "      <th>afr__sqrt(area)*kitchen_area</th>\n",
       "      <th>afr__geo_lon*log(kitchen_area)</th>\n",
       "      <th>afr__sqrt(area)*sqrt(kitchen_area)</th>\n",
       "      <th>afr__sqrt(geo_lon)*sqrt(kitchen_area)</th>\n",
       "      <th>afr__log(area)</th>\n",
       "      <th>afr__rooms*log(level)</th>\n",
       "      <th>afr__kitchen_area*rooms</th>\n",
       "      <th>afr__kitchen_area*levels</th>\n",
       "      <th>afr__sqrt(geo_lon)*sqrt(level)</th>\n",
       "      <th>afr__area**(3/2)</th>\n",
       "      <th>afr__geo_lat*log(kitchen_area)</th>\n",
       "      <th>afr__geo_lat*log(geo_lon)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.495902</td>\n",
       "      <td>-0.449742</td>\n",
       "      <td>0.359235</td>\n",
       "      <td>-0.214789</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>0.063735</td>\n",
       "      <td>-0.186285</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.495902</td>\n",
       "      <td>-0.449742</td>\n",
       "      <td>0.359235</td>\n",
       "      <td>-0.214789</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>0.063735</td>\n",
       "      <td>-0.186285</td>\n",
       "      <td>0.006208</td>\n",
       "      <td>-0.195129</td>\n",
       "      <td>0.060916</td>\n",
       "      <td>-0.132188</td>\n",
       "      <td>0.373151</td>\n",
       "      <td>0.688076</td>\n",
       "      <td>0.044178</td>\n",
       "      <td>-0.211335</td>\n",
       "      <td>-0.481294</td>\n",
       "      <td>-0.153548</td>\n",
       "      <td>-0.490805</td>\n",
       "      <td>0.307835</td>\n",
       "      <td>0.690329</td>\n",
       "      <td>-0.132529</td>\n",
       "      <td>-0.352834</td>\n",
       "      <td>0.323880</td>\n",
       "      <td>-0.008748</td>\n",
       "      <td>-0.031529</td>\n",
       "      <td>0.068167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.177806</td>\n",
       "      <td>1.433673</td>\n",
       "      <td>-0.246529</td>\n",
       "      <td>-0.367718</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-0.114293</td>\n",
       "      <td>-0.186285</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.177806</td>\n",
       "      <td>1.433673</td>\n",
       "      <td>-0.246529</td>\n",
       "      <td>-0.367718</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-0.114293</td>\n",
       "      <td>-0.186285</td>\n",
       "      <td>-0.083402</td>\n",
       "      <td>0.655053</td>\n",
       "      <td>-0.054279</td>\n",
       "      <td>-0.169370</td>\n",
       "      <td>0.005114</td>\n",
       "      <td>0.071369</td>\n",
       "      <td>-0.173647</td>\n",
       "      <td>-0.252775</td>\n",
       "      <td>1.191304</td>\n",
       "      <td>-0.267268</td>\n",
       "      <td>0.615798</td>\n",
       "      <td>0.031907</td>\n",
       "      <td>0.282625</td>\n",
       "      <td>-0.132529</td>\n",
       "      <td>-0.418643</td>\n",
       "      <td>0.552794</td>\n",
       "      <td>-0.056540</td>\n",
       "      <td>-0.143829</td>\n",
       "      <td>1.129118</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",
       "      <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>410773</th>\n",
       "      <td>-0.748366</td>\n",
       "      <td>-0.804077</td>\n",
       "      <td>-0.650371</td>\n",
       "      <td>0.702788</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>1.365441</td>\n",
       "      <td>1.501833</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.748366</td>\n",
       "      <td>-0.804077</td>\n",
       "      <td>-0.650371</td>\n",
       "      <td>0.702788</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>1.365441</td>\n",
       "      <td>1.501833</td>\n",
       "      <td>0.661427</td>\n",
       "      <td>0.375199</td>\n",
       "      <td>0.752088</td>\n",
       "      <td>1.570163</td>\n",
       "      <td>1.274445</td>\n",
       "      <td>-0.002521</td>\n",
       "      <td>0.745507</td>\n",
       "      <td>2.382258</td>\n",
       "      <td>0.071599</td>\n",
       "      <td>2.828890</td>\n",
       "      <td>1.431272</td>\n",
       "      <td>1.729715</td>\n",
       "      <td>-0.160491</td>\n",
       "      <td>1.581436</td>\n",
       "      <td>2.432437</td>\n",
       "      <td>-0.843150</td>\n",
       "      <td>0.411475</td>\n",
       "      <td>1.671069</td>\n",
       "      <td>-1.052343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410774</th>\n",
       "      <td>1.257769</td>\n",
       "      <td>-1.101815</td>\n",
       "      <td>-0.044608</td>\n",
       "      <td>0.091070</td>\n",
       "      <td>1.175911</td>\n",
       "      <td>0.553789</td>\n",
       "      <td>-0.142544</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.257769</td>\n",
       "      <td>-1.101815</td>\n",
       "      <td>-0.044608</td>\n",
       "      <td>0.091070</td>\n",
       "      <td>1.175911</td>\n",
       "      <td>0.553789</td>\n",
       "      <td>-0.142544</td>\n",
       "      <td>0.807887</td>\n",
       "      <td>-0.330070</td>\n",
       "      <td>0.982478</td>\n",
       "      <td>-0.002742</td>\n",
       "      <td>1.338996</td>\n",
       "      <td>0.635065</td>\n",
       "      <td>-0.040302</td>\n",
       "      <td>-0.055435</td>\n",
       "      <td>-1.025588</td>\n",
       "      <td>0.202136</td>\n",
       "      <td>-0.916054</td>\n",
       "      <td>0.940624</td>\n",
       "      <td>1.217910</td>\n",
       "      <td>0.311575</td>\n",
       "      <td>-0.174762</td>\n",
       "      <td>-0.415359</td>\n",
       "      <td>0.135617</td>\n",
       "      <td>0.359680</td>\n",
       "      <td>-0.246790</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>410775 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        num__geo_lat  num__geo_lon  num__level  num__levels  num__rooms  \\\n",
       "0           0.495902     -0.449742    0.359235    -0.214789    0.253413   \n",
       "1           0.177806      1.433673   -0.246529    -0.367718    0.253413   \n",
       "...              ...           ...         ...          ...         ...   \n",
       "410773     -0.748366     -0.804077   -0.650371     0.702788    0.253413   \n",
       "410774      1.257769     -1.101815   -0.044608     0.091070    1.175911   \n",
       "\n",
       "        num__area  num__kitchen_area  cat__region  cat__building_type  \\\n",
       "0        0.063735          -0.186285         20.0                 1.0   \n",
       "1       -0.114293          -0.186285         70.0                 1.0   \n",
       "...           ...                ...          ...                 ...   \n",
       "410773   1.365441           1.501833         52.0                 3.0   \n",
       "410774   0.553789          -0.142544         14.0                 1.0   \n",
       "\n",
       "        cat__object_type  afr__geo_lat  afr__geo_lon  afr__level  afr__levels  \\\n",
       "0                    0.0      0.495902     -0.449742    0.359235    -0.214789   \n",
       "1                    0.0      0.177806      1.433673   -0.246529    -0.367718   \n",
       "...                  ...           ...           ...         ...          ...   \n",
       "410773               0.0     -0.748366     -0.804077   -0.650371     0.702788   \n",
       "410774               0.0      1.257769     -1.101815   -0.044608     0.091070   \n",
       "\n",
       "        afr__rooms  afr__area  afr__kitchen_area  afr__area*rooms  \\\n",
       "0         0.253413   0.063735          -0.186285         0.006208   \n",
       "1         0.253413  -0.114293          -0.186285        -0.083402   \n",
       "...            ...        ...                ...              ...   \n",
       "410773    0.253413   1.365441           1.501833         0.661427   \n",
       "410774    1.175911   0.553789          -0.142544         0.807887   \n",
       "\n",
       "        afr__area*geo_lon  afr__levels*rooms  afr__area*kitchen_area  \\\n",
       "0               -0.195129           0.060916               -0.132188   \n",
       "1                0.655053          -0.054279               -0.169370   \n",
       "...                   ...                ...                     ...   \n",
       "410773           0.375199           0.752088                1.570163   \n",
       "410774          -0.330070           0.982478               -0.002742   \n",
       "\n",
       "        afr__sqrt(area)*geo_lat  afr__sqrt(area)*log(level)  \\\n",
       "0                      0.373151                    0.688076   \n",
       "1                      0.005114                    0.071369   \n",
       "...                         ...                         ...   \n",
       "410773                 1.274445                   -0.002521   \n",
       "410774                 1.338996                    0.635065   \n",
       "\n",
       "        afr__kitchen_area*log(level)  afr__sqrt(area)*kitchen_area  \\\n",
       "0                           0.044178                     -0.211335   \n",
       "1                          -0.173647                     -0.252775   \n",
       "...                              ...                           ...   \n",
       "410773                      0.745507                      2.382258   \n",
       "410774                     -0.040302                     -0.055435   \n",
       "\n",
       "        afr__geo_lon*log(kitchen_area)  afr__sqrt(area)*sqrt(kitchen_area)  \\\n",
       "0                            -0.481294                           -0.153548   \n",
       "1                             1.191304                           -0.267268   \n",
       "...                                ...                                 ...   \n",
       "410773                        0.071599                            2.828890   \n",
       "410774                       -1.025588                            0.202136   \n",
       "\n",
       "        afr__sqrt(geo_lon)*sqrt(kitchen_area)  afr__log(area)  \\\n",
       "0                                   -0.490805        0.307835   \n",
       "1                                    0.615798        0.031907   \n",
       "...                                       ...             ...   \n",
       "410773                               1.431272        1.729715   \n",
       "410774                              -0.916054        0.940624   \n",
       "\n",
       "        afr__rooms*log(level)  afr__kitchen_area*rooms  \\\n",
       "0                    0.690329                -0.132529   \n",
       "1                    0.282625                -0.132529   \n",
       "...                       ...                      ...   \n",
       "410773              -0.160491                 1.581436   \n",
       "410774               1.217910                 0.311575   \n",
       "\n",
       "        afr__kitchen_area*levels  afr__sqrt(geo_lon)*sqrt(level)  \\\n",
       "0                      -0.352834                        0.323880   \n",
       "1                      -0.418643                        0.552794   \n",
       "...                          ...                             ...   \n",
       "410773                  2.432437                       -0.843150   \n",
       "410774                 -0.174762                       -0.415359   \n",
       "\n",
       "        afr__area**(3/2)  afr__geo_lat*log(kitchen_area)  \\\n",
       "0              -0.008748                       -0.031529   \n",
       "1              -0.056540                       -0.143829   \n",
       "...                  ...                             ...   \n",
       "410773          0.411475                        1.671069   \n",
       "410774          0.135617                        0.359680   \n",
       "\n",
       "        afr__geo_lat*log(geo_lon)  \n",
       "0                        0.068167  \n",
       "1                        1.129118  \n",
       "...                           ...  \n",
       "410773                  -1.052343  \n",
       "410774                  -0.246790  \n",
       "\n",
       "[410775 rows x 36 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with pd.option_context('display.max_rows', 5, 'display.max_columns', None):\n",
    "    display (X_train_afr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline_afr = Pipeline(steps=[('preprocessor', preprocessor_afr), \n",
    "                               ('model', regressor)])\n",
    "\n",
    "pipeline_afr.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = pipeline_afr.predict(X_test) \n",
    "\n",
    "metrics = {}\n",
    "metrics[\"mae\"] = mean_absolute_error(y_test, predictions)   \n",
    "metrics[\"mape\"] = mean_absolute_percentage_error(y_test, predictions)\n",
    "metrics[\"mse\"] = mean_squared_error(y_test, predictions)\n",
    "\n",
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "experiment_id = mlflow.get_experiment_by_name(EXPERIMENT_NAME).experiment_id\n",
    "\n",
    "with mlflow.start_run(run_name='autofeat', experiment_id=experiment_id) as run:\n",
    "    # получаем уникальный идентификатор запуска эксперимента\n",
    "    run_id = run.info.run_id \n",
    "    mlflow.sklearn.log_model(pipeline_afr, \n",
    "                             artifact_path=\"models\",\n",
    "                             signature=signature,\n",
    "                             input_example=input_example,\n",
    "                             pip_requirements=req_file\n",
    "                             )\n",
    "    mlflow.log_metrics(metrics)\n",
    "    mlflow.log_artifact(art)\n",
    "    mlflow.log_params(pipeline_afr.get_params())\n",
    "\n",
    "run = mlflow.get_run(run_id) \n",
    "assert (run.info.status =='FINISHED')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FEATURE SELECTION\n",
    "## RFE\n",
    "### Используем autofeat признаки\n",
    "Поскольку autofeat дает разные совокупности сгенерированных признаков, мы можем добавить выбор информативных только как шаг пайплайна "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "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>num__geo_lat</th>\n",
       "      <th>num__geo_lon</th>\n",
       "      <th>num__level</th>\n",
       "      <th>num__levels</th>\n",
       "      <th>num__rooms</th>\n",
       "      <th>num__area</th>\n",
       "      <th>num__kitchen_area</th>\n",
       "      <th>cat__region</th>\n",
       "      <th>cat__building_type</th>\n",
       "      <th>cat__object_type</th>\n",
       "      <th>...</th>\n",
       "      <th>afr__sqrt(area)*sqrt(kitchen_area)</th>\n",
       "      <th>afr__sqrt(geo_lon)*sqrt(kitchen_area)</th>\n",
       "      <th>afr__log(area)</th>\n",
       "      <th>afr__rooms*log(level)</th>\n",
       "      <th>afr__kitchen_area*rooms</th>\n",
       "      <th>afr__kitchen_area*levels</th>\n",
       "      <th>afr__sqrt(geo_lon)*sqrt(level)</th>\n",
       "      <th>afr__area**(3/2)</th>\n",
       "      <th>afr__geo_lat*log(kitchen_area)</th>\n",
       "      <th>afr__geo_lat*log(geo_lon)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.495902</td>\n",
       "      <td>-0.449742</td>\n",
       "      <td>0.359235</td>\n",
       "      <td>-0.214789</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>0.063735</td>\n",
       "      <td>-0.186285</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.153548</td>\n",
       "      <td>-0.490805</td>\n",
       "      <td>0.307835</td>\n",
       "      <td>0.690329</td>\n",
       "      <td>-0.132529</td>\n",
       "      <td>-0.352834</td>\n",
       "      <td>0.323880</td>\n",
       "      <td>-0.008748</td>\n",
       "      <td>-0.031529</td>\n",
       "      <td>0.068167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.177806</td>\n",
       "      <td>1.433673</td>\n",
       "      <td>-0.246529</td>\n",
       "      <td>-0.367718</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-0.114293</td>\n",
       "      <td>-0.186285</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.267268</td>\n",
       "      <td>0.615798</td>\n",
       "      <td>0.031907</td>\n",
       "      <td>0.282625</td>\n",
       "      <td>-0.132529</td>\n",
       "      <td>-0.418643</td>\n",
       "      <td>0.552794</td>\n",
       "      <td>-0.056540</td>\n",
       "      <td>-0.143829</td>\n",
       "      <td>1.129118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.440548</td>\n",
       "      <td>0.047222</td>\n",
       "      <td>-0.448450</td>\n",
       "      <td>-0.367718</td>\n",
       "      <td>-0.669085</td>\n",
       "      <td>-0.456947</td>\n",
       "      <td>-0.142544</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.454880</td>\n",
       "      <td>-0.067183</td>\n",
       "      <td>-0.603122</td>\n",
       "      <td>-0.512211</td>\n",
       "      <td>-0.487813</td>\n",
       "      <td>-0.383803</td>\n",
       "      <td>-0.243092</td>\n",
       "      <td>-0.140800</td>\n",
       "      <td>0.063464</td>\n",
       "      <td>0.460495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.588818</td>\n",
       "      <td>-0.722477</td>\n",
       "      <td>-0.246529</td>\n",
       "      <td>-0.979436</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-0.181292</td>\n",
       "      <td>-0.142544</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.254514</td>\n",
       "      <td>-0.607607</td>\n",
       "      <td>-0.080304</td>\n",
       "      <td>0.282625</td>\n",
       "      <td>-0.088119</td>\n",
       "      <td>-0.662523</td>\n",
       "      <td>-0.369355</td>\n",
       "      <td>-0.073838</td>\n",
       "      <td>-0.672113</td>\n",
       "      <td>-1.481033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.493662</td>\n",
       "      <td>1.125819</td>\n",
       "      <td>0.157313</td>\n",
       "      <td>0.549858</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>0.615045</td>\n",
       "      <td>-0.011322</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.438600</td>\n",
       "      <td>0.891383</td>\n",
       "      <td>1.009612</td>\n",
       "      <td>0.574497</td>\n",
       "      <td>0.045112</td>\n",
       "      <td>0.208478</td>\n",
       "      <td>0.945981</td>\n",
       "      <td>0.154902</td>\n",
       "      <td>0.780855</td>\n",
       "      <td>1.923382</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>410770</th>\n",
       "      <td>0.592011</td>\n",
       "      <td>0.355014</td>\n",
       "      <td>0.561156</td>\n",
       "      <td>1.008646</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-0.079836</td>\n",
       "      <td>-0.092653</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.120035</td>\n",
       "      <td>0.237580</td>\n",
       "      <td>0.087725</td>\n",
       "      <td>0.792500</td>\n",
       "      <td>-0.037463</td>\n",
       "      <td>0.322797</td>\n",
       "      <td>0.974381</td>\n",
       "      <td>-0.047496</td>\n",
       "      <td>0.243018</td>\n",
       "      <td>0.789871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410771</th>\n",
       "      <td>0.240478</td>\n",
       "      <td>0.392697</td>\n",
       "      <td>-0.650371</td>\n",
       "      <td>-0.979436</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-0.334434</td>\n",
       "      <td>-0.404989</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.716150</td>\n",
       "      <td>-0.510766</td>\n",
       "      <td>-0.357277</td>\n",
       "      <td>-0.160491</td>\n",
       "      <td>-0.354582</td>\n",
       "      <td>-0.778657</td>\n",
       "      <td>-0.406361</td>\n",
       "      <td>-0.111897</td>\n",
       "      <td>-0.808157</td>\n",
       "      <td>0.574534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410772</th>\n",
       "      <td>-1.936771</td>\n",
       "      <td>-0.688830</td>\n",
       "      <td>0.359235</td>\n",
       "      <td>0.855717</td>\n",
       "      <td>-0.669085</td>\n",
       "      <td>-0.456947</td>\n",
       "      <td>-0.142544</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.454880</td>\n",
       "      <td>-0.581851</td>\n",
       "      <td>-0.603122</td>\n",
       "      <td>-0.211576</td>\n",
       "      <td>-0.487813</td>\n",
       "      <td>0.173638</td>\n",
       "      <td>0.170166</td>\n",
       "      <td>-0.140800</td>\n",
       "      <td>-0.798234</td>\n",
       "      <td>-1.663294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410773</th>\n",
       "      <td>-0.748366</td>\n",
       "      <td>-0.804077</td>\n",
       "      <td>-0.650371</td>\n",
       "      <td>0.702788</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>1.365441</td>\n",
       "      <td>1.501833</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.828890</td>\n",
       "      <td>1.431272</td>\n",
       "      <td>1.729715</td>\n",
       "      <td>-0.160491</td>\n",
       "      <td>1.581436</td>\n",
       "      <td>2.432437</td>\n",
       "      <td>-0.843150</td>\n",
       "      <td>0.411475</td>\n",
       "      <td>1.671069</td>\n",
       "      <td>-1.052343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410774</th>\n",
       "      <td>1.257769</td>\n",
       "      <td>-1.101815</td>\n",
       "      <td>-0.044608</td>\n",
       "      <td>0.091070</td>\n",
       "      <td>1.175911</td>\n",
       "      <td>0.553789</td>\n",
       "      <td>-0.142544</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.202136</td>\n",
       "      <td>-0.916054</td>\n",
       "      <td>0.940624</td>\n",
       "      <td>1.217910</td>\n",
       "      <td>0.311575</td>\n",
       "      <td>-0.174762</td>\n",
       "      <td>-0.415359</td>\n",
       "      <td>0.135617</td>\n",
       "      <td>0.359680</td>\n",
       "      <td>-0.246790</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>410775 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        num__geo_lat  num__geo_lon  num__level  num__levels  num__rooms  \\\n",
       "0           0.495902     -0.449742    0.359235    -0.214789    0.253413   \n",
       "1           0.177806      1.433673   -0.246529    -0.367718    0.253413   \n",
       "2           0.440548      0.047222   -0.448450    -0.367718   -0.669085   \n",
       "3          -1.588818     -0.722477   -0.246529    -0.979436    0.253413   \n",
       "4           1.493662      1.125819    0.157313     0.549858    0.253413   \n",
       "...              ...           ...         ...          ...         ...   \n",
       "410770      0.592011      0.355014    0.561156     1.008646    0.253413   \n",
       "410771      0.240478      0.392697   -0.650371    -0.979436    0.253413   \n",
       "410772     -1.936771     -0.688830    0.359235     0.855717   -0.669085   \n",
       "410773     -0.748366     -0.804077   -0.650371     0.702788    0.253413   \n",
       "410774      1.257769     -1.101815   -0.044608     0.091070    1.175911   \n",
       "\n",
       "        num__area  num__kitchen_area  cat__region  cat__building_type  \\\n",
       "0        0.063735          -0.186285         20.0                 1.0   \n",
       "1       -0.114293          -0.186285         70.0                 1.0   \n",
       "2       -0.456947          -0.142544         15.0                 3.0   \n",
       "3       -0.181292          -0.142544         18.0                 1.0   \n",
       "4        0.615045          -0.011322         10.0                 2.0   \n",
       "...           ...                ...          ...                 ...   \n",
       "410770  -0.079836          -0.092653         54.0                 2.0   \n",
       "410771  -0.334434          -0.404989         45.0                 3.0   \n",
       "410772  -0.456947          -0.142544         18.0                 0.0   \n",
       "410773   1.365441           1.501833         52.0                 3.0   \n",
       "410774   0.553789          -0.142544         14.0                 1.0   \n",
       "\n",
       "        cat__object_type  ...  afr__sqrt(area)*sqrt(kitchen_area)  \\\n",
       "0                    0.0  ...                           -0.153548   \n",
       "1                    0.0  ...                           -0.267268   \n",
       "2                    1.0  ...                           -0.454880   \n",
       "3                    0.0  ...                           -0.254514   \n",
       "4                    0.0  ...                            0.438600   \n",
       "...                  ...  ...                                 ...   \n",
       "410770               0.0  ...                           -0.120035   \n",
       "410771               0.0  ...                           -0.716150   \n",
       "410772               1.0  ...                           -0.454880   \n",
       "410773               0.0  ...                            2.828890   \n",
       "410774               0.0  ...                            0.202136   \n",
       "\n",
       "        afr__sqrt(geo_lon)*sqrt(kitchen_area)  afr__log(area)  \\\n",
       "0                                   -0.490805        0.307835   \n",
       "1                                    0.615798        0.031907   \n",
       "2                                   -0.067183       -0.603122   \n",
       "3                                   -0.607607       -0.080304   \n",
       "4                                    0.891383        1.009612   \n",
       "...                                       ...             ...   \n",
       "410770                               0.237580        0.087725   \n",
       "410771                              -0.510766       -0.357277   \n",
       "410772                              -0.581851       -0.603122   \n",
       "410773                               1.431272        1.729715   \n",
       "410774                              -0.916054        0.940624   \n",
       "\n",
       "        afr__rooms*log(level)  afr__kitchen_area*rooms  \\\n",
       "0                    0.690329                -0.132529   \n",
       "1                    0.282625                -0.132529   \n",
       "2                   -0.512211                -0.487813   \n",
       "3                    0.282625                -0.088119   \n",
       "4                    0.574497                 0.045112   \n",
       "...                       ...                      ...   \n",
       "410770               0.792500                -0.037463   \n",
       "410771              -0.160491                -0.354582   \n",
       "410772              -0.211576                -0.487813   \n",
       "410773              -0.160491                 1.581436   \n",
       "410774               1.217910                 0.311575   \n",
       "\n",
       "        afr__kitchen_area*levels  afr__sqrt(geo_lon)*sqrt(level)  \\\n",
       "0                      -0.352834                        0.323880   \n",
       "1                      -0.418643                        0.552794   \n",
       "2                      -0.383803                       -0.243092   \n",
       "3                      -0.662523                       -0.369355   \n",
       "4                       0.208478                        0.945981   \n",
       "...                          ...                             ...   \n",
       "410770                  0.322797                        0.974381   \n",
       "410771                 -0.778657                       -0.406361   \n",
       "410772                  0.173638                        0.170166   \n",
       "410773                  2.432437                       -0.843150   \n",
       "410774                 -0.174762                       -0.415359   \n",
       "\n",
       "        afr__area**(3/2)  afr__geo_lat*log(kitchen_area)  \\\n",
       "0              -0.008748                       -0.031529   \n",
       "1              -0.056540                       -0.143829   \n",
       "2              -0.140800                        0.063464   \n",
       "3              -0.073838                       -0.672113   \n",
       "4               0.154902                        0.780855   \n",
       "...                  ...                             ...   \n",
       "410770         -0.047496                        0.243018   \n",
       "410771         -0.111897                       -0.808157   \n",
       "410772         -0.140800                       -0.798234   \n",
       "410773          0.411475                        1.671069   \n",
       "410774          0.135617                        0.359680   \n",
       "\n",
       "        afr__geo_lat*log(geo_lon)  \n",
       "0                        0.068167  \n",
       "1                        1.129118  \n",
       "2                        0.460495  \n",
       "3                       -1.481033  \n",
       "4                        1.923382  \n",
       "...                           ...  \n",
       "410770                   0.789871  \n",
       "410771                   0.574534  \n",
       "410772                  -1.663294  \n",
       "410773                  -1.052343  \n",
       "410774                  -0.246790  \n",
       "\n",
       "[410775 rows x 36 columns]"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "X_train_afr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "rfe_selector = RFE(estimator=regressor, n_features_to_select=12, step = 0.2) #drop 20% of features each iteration\n",
    "X_train_rfe = rfe_selector.fit_transform(X_train_afr,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "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>num__geo_lat</th>\n",
       "      <th>num__geo_lon</th>\n",
       "      <th>afr__geo_lon</th>\n",
       "      <th>afr__area*kitchen_area</th>\n",
       "      <th>afr__sqrt(area)*geo_lat</th>\n",
       "      <th>afr__sqrt(area)*log(level)</th>\n",
       "      <th>afr__kitchen_area*log(level)</th>\n",
       "      <th>afr__sqrt(area)*sqrt(kitchen_area)</th>\n",
       "      <th>afr__rooms*log(level)</th>\n",
       "      <th>afr__kitchen_area*rooms</th>\n",
       "      <th>afr__sqrt(geo_lon)*sqrt(level)</th>\n",
       "      <th>afr__geo_lat*log(geo_lon)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.495902</td>\n",
       "      <td>-0.449742</td>\n",
       "      <td>-0.449742</td>\n",
       "      <td>-0.132188</td>\n",
       "      <td>0.373151</td>\n",
       "      <td>0.688076</td>\n",
       "      <td>0.044178</td>\n",
       "      <td>-0.153548</td>\n",
       "      <td>0.690329</td>\n",
       "      <td>-0.132529</td>\n",
       "      <td>0.323880</td>\n",
       "      <td>0.068167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.177806</td>\n",
       "      <td>1.433673</td>\n",
       "      <td>1.433673</td>\n",
       "      <td>-0.169370</td>\n",
       "      <td>0.005114</td>\n",
       "      <td>0.071369</td>\n",
       "      <td>-0.173647</td>\n",
       "      <td>-0.267268</td>\n",
       "      <td>0.282625</td>\n",
       "      <td>-0.132529</td>\n",
       "      <td>0.552794</td>\n",
       "      <td>1.129118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.440548</td>\n",
       "      <td>0.047222</td>\n",
       "      <td>0.047222</td>\n",
       "      <td>-0.226261</td>\n",
       "      <td>-0.425530</td>\n",
       "      <td>-0.335537</td>\n",
       "      <td>-0.239271</td>\n",
       "      <td>-0.454880</td>\n",
       "      <td>-0.512211</td>\n",
       "      <td>-0.487813</td>\n",
       "      <td>-0.243092</td>\n",
       "      <td>0.460495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.588818</td>\n",
       "      <td>-0.722477</td>\n",
       "      <td>-0.722477</td>\n",
       "      <td>-0.165302</td>\n",
       "      <td>-0.723225</td>\n",
       "      <td>0.034116</td>\n",
       "      <td>-0.129771</td>\n",
       "      <td>-0.254514</td>\n",
       "      <td>0.282625</td>\n",
       "      <td>-0.088119</td>\n",
       "      <td>-0.369355</td>\n",
       "      <td>-1.481033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.493662</td>\n",
       "      <td>1.125819</td>\n",
       "      <td>1.125819</td>\n",
       "      <td>0.094342</td>\n",
       "      <td>1.522265</td>\n",
       "      <td>0.862773</td>\n",
       "      <td>0.194490</td>\n",
       "      <td>0.438600</td>\n",
       "      <td>0.574497</td>\n",
       "      <td>0.045112</td>\n",
       "      <td>0.945981</td>\n",
       "      <td>1.923382</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410770</th>\n",
       "      <td>0.592011</td>\n",
       "      <td>0.355014</td>\n",
       "      <td>0.355014</td>\n",
       "      <td>-0.120841</td>\n",
       "      <td>0.206926</td>\n",
       "      <td>0.714499</td>\n",
       "      <td>0.226990</td>\n",
       "      <td>-0.120035</td>\n",
       "      <td>0.792500</td>\n",
       "      <td>-0.037463</td>\n",
       "      <td>0.974381</td>\n",
       "      <td>0.789871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410771</th>\n",
       "      <td>0.240478</td>\n",
       "      <td>0.392697</td>\n",
       "      <td>0.392697</td>\n",
       "      <td>-0.296252</td>\n",
       "      <td>-0.297209</td>\n",
       "      <td>-0.551021</td>\n",
       "      <td>-0.560144</td>\n",
       "      <td>-0.716150</td>\n",
       "      <td>-0.160491</td>\n",
       "      <td>-0.354582</td>\n",
       "      <td>-0.406361</td>\n",
       "      <td>0.574534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410772</th>\n",
       "      <td>-1.936771</td>\n",
       "      <td>-0.688830</td>\n",
       "      <td>-0.688830</td>\n",
       "      <td>-0.226261</td>\n",
       "      <td>-1.192706</td>\n",
       "      <td>0.306280</td>\n",
       "      <td>0.100868</td>\n",
       "      <td>-0.454880</td>\n",
       "      <td>-0.211576</td>\n",
       "      <td>-0.487813</td>\n",
       "      <td>0.170166</td>\n",
       "      <td>-1.663294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410773</th>\n",
       "      <td>-0.748366</td>\n",
       "      <td>-0.804077</td>\n",
       "      <td>-0.804077</td>\n",
       "      <td>1.570163</td>\n",
       "      <td>1.274445</td>\n",
       "      <td>-0.002521</td>\n",
       "      <td>0.745507</td>\n",
       "      <td>2.828890</td>\n",
       "      <td>-0.160491</td>\n",
       "      <td>1.581436</td>\n",
       "      <td>-0.843150</td>\n",
       "      <td>-1.052343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410774</th>\n",
       "      <td>1.257769</td>\n",
       "      <td>-1.101815</td>\n",
       "      <td>-1.101815</td>\n",
       "      <td>-0.002742</td>\n",
       "      <td>1.338996</td>\n",
       "      <td>0.635065</td>\n",
       "      <td>-0.040302</td>\n",
       "      <td>0.202136</td>\n",
       "      <td>1.217910</td>\n",
       "      <td>0.311575</td>\n",
       "      <td>-0.415359</td>\n",
       "      <td>-0.246790</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>410775 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        num__geo_lat  num__geo_lon  afr__geo_lon  afr__area*kitchen_area  \\\n",
       "0           0.495902     -0.449742     -0.449742               -0.132188   \n",
       "1           0.177806      1.433673      1.433673               -0.169370   \n",
       "2           0.440548      0.047222      0.047222               -0.226261   \n",
       "3          -1.588818     -0.722477     -0.722477               -0.165302   \n",
       "4           1.493662      1.125819      1.125819                0.094342   \n",
       "...              ...           ...           ...                     ...   \n",
       "410770      0.592011      0.355014      0.355014               -0.120841   \n",
       "410771      0.240478      0.392697      0.392697               -0.296252   \n",
       "410772     -1.936771     -0.688830     -0.688830               -0.226261   \n",
       "410773     -0.748366     -0.804077     -0.804077                1.570163   \n",
       "410774      1.257769     -1.101815     -1.101815               -0.002742   \n",
       "\n",
       "        afr__sqrt(area)*geo_lat  afr__sqrt(area)*log(level)  \\\n",
       "0                      0.373151                    0.688076   \n",
       "1                      0.005114                    0.071369   \n",
       "2                     -0.425530                   -0.335537   \n",
       "3                     -0.723225                    0.034116   \n",
       "4                      1.522265                    0.862773   \n",
       "...                         ...                         ...   \n",
       "410770                 0.206926                    0.714499   \n",
       "410771                -0.297209                   -0.551021   \n",
       "410772                -1.192706                    0.306280   \n",
       "410773                 1.274445                   -0.002521   \n",
       "410774                 1.338996                    0.635065   \n",
       "\n",
       "        afr__kitchen_area*log(level)  afr__sqrt(area)*sqrt(kitchen_area)  \\\n",
       "0                           0.044178                           -0.153548   \n",
       "1                          -0.173647                           -0.267268   \n",
       "2                          -0.239271                           -0.454880   \n",
       "3                          -0.129771                           -0.254514   \n",
       "4                           0.194490                            0.438600   \n",
       "...                              ...                                 ...   \n",
       "410770                      0.226990                           -0.120035   \n",
       "410771                     -0.560144                           -0.716150   \n",
       "410772                      0.100868                           -0.454880   \n",
       "410773                      0.745507                            2.828890   \n",
       "410774                     -0.040302                            0.202136   \n",
       "\n",
       "        afr__rooms*log(level)  afr__kitchen_area*rooms  \\\n",
       "0                    0.690329                -0.132529   \n",
       "1                    0.282625                -0.132529   \n",
       "2                   -0.512211                -0.487813   \n",
       "3                    0.282625                -0.088119   \n",
       "4                    0.574497                 0.045112   \n",
       "...                       ...                      ...   \n",
       "410770               0.792500                -0.037463   \n",
       "410771              -0.160491                -0.354582   \n",
       "410772              -0.211576                -0.487813   \n",
       "410773              -0.160491                 1.581436   \n",
       "410774               1.217910                 0.311575   \n",
       "\n",
       "        afr__sqrt(geo_lon)*sqrt(level)  afr__geo_lat*log(geo_lon)  \n",
       "0                             0.323880                   0.068167  \n",
       "1                             0.552794                   1.129118  \n",
       "2                            -0.243092                   0.460495  \n",
       "3                            -0.369355                  -1.481033  \n",
       "4                             0.945981                   1.923382  \n",
       "...                                ...                        ...  \n",
       "410770                        0.974381                   0.789871  \n",
       "410771                       -0.406361                   0.574534  \n",
       "410772                        0.170166                  -1.663294  \n",
       "410773                       -0.843150                  -1.052343  \n",
       "410774                       -0.415359                  -0.246790  \n",
       "\n",
       "[410775 rows x 12 columns]"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_afr_rfe = pd.DataFrame(X_train_rfe, columns=rfe_selector.get_feature_names_out())\n",
    "X_train_afr_rfe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfe_pipeline = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor_afr), \n",
    "    ('rfe_extractor', RFE(estimator=regressor, n_features_to_select=12, step = 0.2)),\n",
    "    ('model', regressor)\n",
    "])\n",
    "\n",
    "rfe_pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mae': 1431925.3203264712,\n",
       " 'mape': 1.239752923791043e+18,\n",
       " 'mse': 261947924998018.2}"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_rfe = rfe_pipeline.predict(X_test)\n",
    "\n",
    "metrics = {}\n",
    "metrics[\"mae\"] = mean_absolute_error(y_test, predictions_rfe)   \n",
    "metrics[\"mape\"] = mean_absolute_percentage_error(y_test, predictions_rfe)\n",
    "metrics[\"mse\"] = mean_squared_error(y_test, predictions_rfe)\n",
    "\n",
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading artifacts: 100%|██████████| 7/7 [00:00<00:00, 40.15it/s]\n",
      "2024/10/17 14:26:50 INFO mlflow.tracking._tracking_service.client: 🏃 View run rfe_feature_selection at: http://127.0.0.1:5000/#/experiments/1/runs/96f0bbcd6d88466abcf38f3b53f06ff1.\n",
      "2024/10/17 14:26:50 INFO mlflow.tracking._tracking_service.client: 🧪 View experiment at: http://127.0.0.1:5000/#/experiments/1.\n"
     ]
    }
   ],
   "source": [
    "experiment_id = mlflow.get_experiment_by_name(EXPERIMENT_NAME).experiment_id\n",
    "RUN_NAME = 'rfe_feature_selection'\n",
    "\n",
    "with mlflow.start_run(run_name=RUN_NAME, experiment_id=experiment_id) as run:\n",
    "    # получаем уникальный идентификатор запуска эксперимента\n",
    "    run_id = run.info.run_id \n",
    "    mlflow.sklearn.log_model(rfe_pipeline, \n",
    "                             artifact_path=\"models\",\n",
    "                             signature=signature,\n",
    "                             input_example=input_example,\n",
    "                             pip_requirements=req_file\n",
    "                             )\n",
    "    mlflow.log_metrics(metrics)\n",
    "    mlflow.log_artifact(art)\n",
    "    mlflow.log_params(model_sklearn.get_params())\n",
    "\n",
    "run = mlflow.get_run(run_id) \n",
    "assert (run.info.status =='FINISHED')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Используем sklearn признаки\n",
    "Тут мы можем отобрать признаки один раз на обучении, а далее в качестве шага пайплайна использовать написанный класс ColumnExtractor для выбора нуных столбцов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "rfe_skl_selector = RFE(estimator=regressor, n_features_to_select=12, step = 0.2) #drop 20% of features each iteration\n",
    "X_train_skl_rfe = rfe_skl_selector.fit_transform(X_train_sklearn,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>num__geo_lat</th>\n",
       "      <th>num__geo_lon</th>\n",
       "      <th>num__level</th>\n",
       "      <th>num__rooms</th>\n",
       "      <th>num__kitchen_area</th>\n",
       "      <th>cat__region</th>\n",
       "      <th>quantile__geo_lat</th>\n",
       "      <th>quantile__geo_lon</th>\n",
       "      <th>quantile__level</th>\n",
       "      <th>poly__area kitchen_area</th>\n",
       "      <th>spline__area_sp_0</th>\n",
       "      <th>spline__area_sp_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.495902</td>\n",
       "      <td>-0.449742</td>\n",
       "      <td>0.359235</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-0.186285</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.766257</td>\n",
       "      <td>0.511028</td>\n",
       "      <td>0.717217</td>\n",
       "      <td>-0.132188</td>\n",
       "      <td>0.155806</td>\n",
       "      <td>0.178013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.177806</td>\n",
       "      <td>1.433673</td>\n",
       "      <td>-0.246529</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-0.186285</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.297142</td>\n",
       "      <td>0.867999</td>\n",
       "      <td>0.522022</td>\n",
       "      <td>-0.169370</td>\n",
       "      <td>0.156921</td>\n",
       "      <td>0.176803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.440548</td>\n",
       "      <td>0.047222</td>\n",
       "      <td>-0.448450</td>\n",
       "      <td>-0.669085</td>\n",
       "      <td>-0.142544</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.732330</td>\n",
       "      <td>0.629984</td>\n",
       "      <td>0.417417</td>\n",
       "      <td>-0.226261</td>\n",
       "      <td>0.159080</td>\n",
       "      <td>0.174488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.588818</td>\n",
       "      <td>-0.722477</td>\n",
       "      <td>-0.246529</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-0.142544</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.148789</td>\n",
       "      <td>0.295262</td>\n",
       "      <td>0.522022</td>\n",
       "      <td>-0.165302</td>\n",
       "      <td>0.157341</td>\n",
       "      <td>0.176349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.493662</td>\n",
       "      <td>1.125819</td>\n",
       "      <td>0.157313</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-0.011322</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.985937</td>\n",
       "      <td>0.758363</td>\n",
       "      <td>0.662663</td>\n",
       "      <td>0.094342</td>\n",
       "      <td>0.152390</td>\n",
       "      <td>0.181792</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410770</th>\n",
       "      <td>0.592011</td>\n",
       "      <td>0.355014</td>\n",
       "      <td>0.561156</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-0.092653</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.788393</td>\n",
       "      <td>0.686728</td>\n",
       "      <td>0.771271</td>\n",
       "      <td>-0.120841</td>\n",
       "      <td>0.156705</td>\n",
       "      <td>0.177037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410771</th>\n",
       "      <td>0.240478</td>\n",
       "      <td>0.392697</td>\n",
       "      <td>-0.650371</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>-0.404989</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.494062</td>\n",
       "      <td>0.717240</td>\n",
       "      <td>0.309810</td>\n",
       "      <td>-0.296252</td>\n",
       "      <td>0.158306</td>\n",
       "      <td>0.175314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410772</th>\n",
       "      <td>-1.936771</td>\n",
       "      <td>-0.688830</td>\n",
       "      <td>0.359235</td>\n",
       "      <td>-0.669085</td>\n",
       "      <td>-0.142544</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.131352</td>\n",
       "      <td>0.327613</td>\n",
       "      <td>0.717217</td>\n",
       "      <td>-0.226261</td>\n",
       "      <td>0.159080</td>\n",
       "      <td>0.174488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410773</th>\n",
       "      <td>-0.748366</td>\n",
       "      <td>-0.804077</td>\n",
       "      <td>-0.650371</td>\n",
       "      <td>0.253413</td>\n",
       "      <td>1.501833</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.193143</td>\n",
       "      <td>0.114753</td>\n",
       "      <td>0.309810</td>\n",
       "      <td>1.570163</td>\n",
       "      <td>0.147820</td>\n",
       "      <td>0.187011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410774</th>\n",
       "      <td>1.257769</td>\n",
       "      <td>-1.101815</td>\n",
       "      <td>-0.044608</td>\n",
       "      <td>1.175911</td>\n",
       "      <td>-0.142544</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.908036</td>\n",
       "      <td>0.075725</td>\n",
       "      <td>0.604605</td>\n",
       "      <td>-0.002742</td>\n",
       "      <td>0.152767</td>\n",
       "      <td>0.181370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>410775 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        num__geo_lat  num__geo_lon  num__level  num__rooms  num__kitchen_area  \\\n",
       "0           0.495902     -0.449742    0.359235    0.253413          -0.186285   \n",
       "1           0.177806      1.433673   -0.246529    0.253413          -0.186285   \n",
       "2           0.440548      0.047222   -0.448450   -0.669085          -0.142544   \n",
       "3          -1.588818     -0.722477   -0.246529    0.253413          -0.142544   \n",
       "4           1.493662      1.125819    0.157313    0.253413          -0.011322   \n",
       "...              ...           ...         ...         ...                ...   \n",
       "410770      0.592011      0.355014    0.561156    0.253413          -0.092653   \n",
       "410771      0.240478      0.392697   -0.650371    0.253413          -0.404989   \n",
       "410772     -1.936771     -0.688830    0.359235   -0.669085          -0.142544   \n",
       "410773     -0.748366     -0.804077   -0.650371    0.253413           1.501833   \n",
       "410774      1.257769     -1.101815   -0.044608    1.175911          -0.142544   \n",
       "\n",
       "        cat__region  quantile__geo_lat  quantile__geo_lon  quantile__level  \\\n",
       "0              20.0           0.766257           0.511028         0.717217   \n",
       "1              70.0           0.297142           0.867999         0.522022   \n",
       "2              15.0           0.732330           0.629984         0.417417   \n",
       "3              18.0           0.148789           0.295262         0.522022   \n",
       "4              10.0           0.985937           0.758363         0.662663   \n",
       "...             ...                ...                ...              ...   \n",
       "410770         54.0           0.788393           0.686728         0.771271   \n",
       "410771         45.0           0.494062           0.717240         0.309810   \n",
       "410772         18.0           0.131352           0.327613         0.717217   \n",
       "410773         52.0           0.193143           0.114753         0.309810   \n",
       "410774         14.0           0.908036           0.075725         0.604605   \n",
       "\n",
       "        poly__area kitchen_area  spline__area_sp_0  spline__area_sp_2  \n",
       "0                     -0.132188           0.155806           0.178013  \n",
       "1                     -0.169370           0.156921           0.176803  \n",
       "2                     -0.226261           0.159080           0.174488  \n",
       "3                     -0.165302           0.157341           0.176349  \n",
       "4                      0.094342           0.152390           0.181792  \n",
       "...                         ...                ...                ...  \n",
       "410770                -0.120841           0.156705           0.177037  \n",
       "410771                -0.296252           0.158306           0.175314  \n",
       "410772                -0.226261           0.159080           0.174488  \n",
       "410773                 1.570163           0.147820           0.187011  \n",
       "410774                -0.002742           0.152767           0.181370  \n",
       "\n",
       "[410775 rows x 12 columns]"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_skl_rfe = pd.DataFrame(X_train_skl_rfe, columns=rfe_skl_selector.get_feature_names_out())\n",
    "X_train_skl_rfe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['num__geo_lat',\n",
       " 'num__geo_lon',\n",
       " 'num__level',\n",
       " 'num__rooms',\n",
       " 'num__kitchen_area',\n",
       " 'cat__region',\n",
       " 'quantile__geo_lat',\n",
       " 'quantile__geo_lon',\n",
       " 'quantile__level',\n",
       " 'poly__area kitchen_area',\n",
       " 'spline__area_sp_0',\n",
       " 'spline__area_sp_2']"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfe_cols = X_train_skl_rfe.columns.tolist()\n",
    "rfe_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True, False,  True, False,  True,  True, False,\n",
       "       False,  True,  True,  True, False, False, False, False, False,\n",
       "       False, False, False,  True, False,  True, False,  True, False,\n",
       "       False])"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfe_idx = rfe_skl_selector.support_\n",
    "rfe_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Отбираемые столбцы нужно залогировать, иначе мы потеряем информацию о том, какие призныки выбраны\n",
    "with open('rfe_skl_idx.txt', 'w+') as f:\n",
    "    f.write(str(rfe_idx))\n",
    "with open('rfe_skl_cols.txt', 'w+') as f:\n",
    "    f.write(str(rfe_cols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ColumnExtractor(object):\n",
    "\n",
    "    def __init__(self, cols):\n",
    "        self.cols = cols\n",
    "\n",
    "    def transform(self, X):\n",
    "        return X[:,self.cols]\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        return self\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfe_skl_pipeline = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor_sklearn), \n",
    "    ('rfe_extractor', ColumnExtractor(rfe_idx)),\n",
    "    ('model', regressor)\n",
    "])\n",
    "\n",
    "rfe_skl_pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading artifacts: 100%|██████████| 7/7 [00:00<00:00, 193.34it/s]\n",
      "2024/10/17 14:32:07 INFO mlflow.tracking._tracking_service.client: 🏃 View run rfe_skl_feature_selection at: http://127.0.0.1:5000/#/experiments/1/runs/e55206caeb1549e4aa0d98343d5c1d4d.\n",
      "2024/10/17 14:32:07 INFO mlflow.tracking._tracking_service.client: 🧪 View experiment at: http://127.0.0.1:5000/#/experiments/1.\n"
     ]
    }
   ],
   "source": [
    "predictions_rfe_skl = rfe_skl_pipeline.predict(X_test)\n",
    "\n",
    "metrics = {}\n",
    "metrics[\"mae\"] = mean_absolute_error(y_test, predictions_rfe_skl)   \n",
    "metrics[\"mape\"] = mean_absolute_percentage_error(y_test, predictions_rfe_skl)\n",
    "metrics[\"mse\"] = mean_squared_error(y_test, predictions_rfe_skl)\n",
    "\n",
    "metrics\n",
    "experiment_id = mlflow.get_experiment_by_name(EXPERIMENT_NAME).experiment_id\n",
    "RUN_NAME = 'rfe_skl_feature_selection'\n",
    "\n",
    "with mlflow.start_run(run_name=RUN_NAME, experiment_id=experiment_id) as run:\n",
    "    # получаем уникальный идентификатор запуска эксперимента\n",
    "    run_id = run.info.run_id \n",
    "    mlflow.sklearn.log_model(rfe_pipeline, \n",
    "                             artifact_path=\"models\",\n",
    "                             signature=signature,\n",
    "                             input_example=input_example,\n",
    "                             pip_requirements=req_file\n",
    "                             )\n",
    "    mlflow.log_metrics(metrics)\n",
    "    mlflow.log_artifact('rfe_skl_cols.txt')\n",
    "    mlflow.log_artifact('rfe_skl_idx.txt')\n",
    "    mlflow.log_params(model_sklearn.get_params())\n",
    "\n",
    "run = mlflow.get_run(run_id) \n",
    "assert (run.info.status =='FINISHED')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## mlextend\n",
    "https://github.com/rasbt/mlxtend/blob/master/docs/sources/user_guide/feature_selection/SequentialFeatureSelector.ipynb "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.feature_selection import SequentialFeatureSelector \n",
    "#from sklearn.feature_selection import SequentialFeatureSelector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sfs = SequentialFeatureSelector(RandomForestRegressor(n_estimators=3), \n",
    "                                k_features=3,\n",
    "                                forward=True,\n",
    "                                floating=False, # True to drop selected features\n",
    "                                scoring='neg_mean_absolute_error',\n",
    "                                cv=2)\n",
    "\n",
    "sfs.fit(X_train_sklearn,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "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>num__geo_lon</th>\n",
       "      <th>quantile__geo_lat</th>\n",
       "      <th>spline__area_sp_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.449742</td>\n",
       "      <td>0.766257</td>\n",
       "      <td>1.826008e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.433673</td>\n",
       "      <td>0.297142</td>\n",
       "      <td>1.310449e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.047222</td>\n",
       "      <td>0.732330</td>\n",
       "      <td>6.098363e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.722477</td>\n",
       "      <td>0.148789</td>\n",
       "      <td>1.144942e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.125819</td>\n",
       "      <td>0.985937</td>\n",
       "      <td>4.240047e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410770</th>\n",
       "      <td>0.355014</td>\n",
       "      <td>0.788393</td>\n",
       "      <td>1.401454e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410771</th>\n",
       "      <td>0.392697</td>\n",
       "      <td>0.494062</td>\n",
       "      <td>8.202272e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410772</th>\n",
       "      <td>-0.688830</td>\n",
       "      <td>0.131352</td>\n",
       "      <td>6.098363e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410773</th>\n",
       "      <td>-0.804077</td>\n",
       "      <td>0.193143</td>\n",
       "      <td>1.004843e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410774</th>\n",
       "      <td>-1.101815</td>\n",
       "      <td>0.908036</td>\n",
       "      <td>3.903343e-06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>410775 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        num__geo_lon  quantile__geo_lat  spline__area_sp_3\n",
       "0          -0.449742           0.766257       1.826008e-06\n",
       "1           1.433673           0.297142       1.310449e-06\n",
       "2           0.047222           0.732330       6.098363e-07\n",
       "3          -0.722477           0.148789       1.144942e-06\n",
       "4           1.125819           0.985937       4.240047e-06\n",
       "...              ...                ...                ...\n",
       "410770      0.355014           0.788393       1.401454e-06\n",
       "410771      0.392697           0.494062       8.202272e-07\n",
       "410772     -0.688830           0.131352       6.098363e-07\n",
       "410773     -0.804077           0.193143       1.004843e-05\n",
       "410774     -1.101815           0.908036       3.903343e-06\n",
       "\n",
       "[410775 rows x 3 columns]"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_features_sfs = X_train_sklearn.loc[:, sfs.k_feature_names_]\n",
    "selected_features_sfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['num__geo_lon', 'quantile__geo_lat', 'spline__area_sp_3']"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfe_sfs_idx = list(sfs.k_feature_idx_)\n",
    "rfe_sfs_idx\n",
    "rfe_sfs_col = list(sfs.k_feature_names_)\n",
    "rfe_sfs_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mlxtend.plotting import plot_sequential_feature_selection as plot_sfs\n",
    "\n",
    "fig = plot_sfs(sfs.get_metric_dict(), kind='std_dev')\n",
    "\n",
    "plt.title('Sequential Forward Selection (w. StdDev)')\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfe_sfs_pipeline = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor_sklearn), \n",
    "    ('rfe_extractor', ColumnExtractor(rfe_sfs_idx)),\n",
    "    ('model', regressor)\n",
    "])\n",
    "\n",
    "rfe_sfs_pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_sfs = rfe_sfs_pipeline.predict(X_test)\n",
    "\n",
    "metrics = {}\n",
    "metrics[\"mae\"] = mean_absolute_error(y_test, predictions_sfs)   \n",
    "metrics[\"mape\"] = mean_absolute_percentage_error(y_test, predictions_sfs)\n",
    "metrics[\"mse\"] = mean_squared_error(y_test, predictions_sfs)\n",
    "\n",
    "metrics\n",
    "experiment_id = mlflow.get_experiment_by_name(EXPERIMENT_NAME).experiment_id\n",
    "RUN_NAME = 'rfe_sfs_feature_selection'\n",
    "\n",
    "with mlflow.start_run(run_name=RUN_NAME, experiment_id=experiment_id) as run:\n",
    "    # получаем уникальный идентификатор запуска эксперимента\n",
    "    run_id = run.info.run_id \n",
    "    mlflow.sklearn.log_model(rfe_sfs_pipeline, \n",
    "                             artifact_path=\"models\",\n",
    "                             signature=signature,\n",
    "                             input_example=input_example,\n",
    "                             pip_requirements=req_file\n",
    "                             )\n",
    "    mlflow.log_metrics(metrics)\n",
    "    mlflow.log_artifact('rfe_skl_cols.txt')\n",
    "    mlflow.log_artifact('rfe_skl_idx.txt')\n",
    "    mlflow.log_params(model_sklearn.get_params())\n",
    "\n",
    "run = mlflow.get_run(run_id) \n",
    "assert (run.info.status =='FINISHED')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Можно совмещать признаки, выбранные по sfs и sbs: брать их объединение или пересечение. Можно комбинировать с признаками, выделенными разными подходами - целое поле для исследований"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HYPERPARAMS\n",
    "## Gridsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import  GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'model__depth': [1,3,5]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs = GridSearchCV(rfe_sfs_pipeline, param_grid, cv=2, scoring='neg_mean_absolute_error')\n",
    "gs.fit(X_train, y_train)\n",
    "print(\"Лучшие гиперпараметры:\", gs.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs_pipeline  = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor_sklearn), \n",
    "    ('rfe_extractor', ColumnExtractor(rfe_sfs_idx)),\n",
    "    ('model', CatBoostRegressor(depth=5))\n",
    "])\n",
    "\n",
    "# Проведем стандартную проверку на тестовом множестве и залогируем run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вместо GridSearch можно использовать RandomSearch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optuna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "import optuna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective(trial):\n",
    "    # предлагаем гиперпараметры\n",
    "    depth = trial.suggest_int('depth', 1, 10)\n",
    "    learning_rate = trial.suggest_float('learning_rate', 0.001, 0.1)\n",
    "\n",
    "    # создаём и обучаем модель\n",
    "    opt_pipeline  = Pipeline(steps=[\n",
    "        ('preprocessor', preprocessor_sklearn), \n",
    "        ('rfe_extractor', ColumnExtractor(rfe_sfs_idx)),\n",
    "        ('model', CatBoostRegressor(depth=depth, learning_rate=learning_rate, verbose=0))\n",
    "    ])\n",
    "\n",
    "    opt_pipeline.fit(X_train, y_train)\n",
    "\n",
    "    # предсказываем и вычисляем RMSE\n",
    "    preds = opt_pipeline.predict(X_test)\n",
    "    mae =  mean_absolute_error(y_test, preds)   \n",
    "\n",
    "    return mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "study = optuna.create_study(direction='minimize')\n",
    "study.optimize(objective, n_trials=10)\n",
    "\n",
    "# выводим результаты\n",
    "print('Number of finished trials:', len(study.trials))\n",
    "print('Best trial:', study.best_trial.params) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt_pipeline  = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor_sklearn), \n",
    "    ('rfe_extractor', ColumnExtractor(rfe_sfs_idx)),\n",
    "    ('model', CatBoostRegressor(depth=3, learning_rate=0.02789))\n",
    "])\n",
    "\n",
    "# Проведем стандартную проверку на тестовом множестве и залогируем run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выбираем лучшую модель.\n",
    "Обучаем ее на всей выборке (а не только на train-части). \n",
    "Далее будем деплоить именно её"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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