From 3e93c504445d752a7be539836b8fdd06aad336c5 Mon Sep 17 00:00:00 2001
From: Andrey <asmokhov@mail.ru>
Date: Tue, 15 Oct 2024 16:00:36 +0300
Subject: [PATCH] lec6 research clear outputs

---
 assets/mlflow/research.ipynb | 6591 +---------------------------------
 1 file changed, 66 insertions(+), 6525 deletions(-)

diff --git a/assets/mlflow/research.ipynb b/assets/mlflow/research.ipynb
index f95e5a1..fa97d46 100644
--- a/assets/mlflow/research.ipynb
+++ b/assets/mlflow/research.ipynb
@@ -26,36 +26,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "Index: 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"
-     ]
-    }
-   ],
+   "outputs": [],
    "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()"
@@ -73,238 +46,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "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>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",
-       "    <tr>\n",
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-       "      <td>59.958458</td>\n",
-       "      <td>30.215530</td>\n",
-       "      <td>2661</td>\n",
-       "      <td>3</td>\n",
-       "      <td>8</td>\n",
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-       "      <td>2900</td>\n",
-       "      <td>3</td>\n",
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-       "    <tr>\n",
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-       "      <td>9.500000</td>\n",
-       "      <td>1</td>\n",
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-       "    <tr>\n",
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-       "      <td>2</td>\n",
-       "      <td>54.00000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
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-       "      <td>...</td>\n",
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-       "      <td>57.750603</td>\n",
-       "      <td>40.866467</td>\n",
-       "      <td>4189</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
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-       "      <td>11.000000</td>\n",
-       "      <td>1</td>\n",
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-       "    <tr>\n",
-       "      <th>4542014</th>\n",
-       "      <td>6700000</td>\n",
-       "      <td>55.911720</td>\n",
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-       "      <td>81</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
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-       "      <td>66.37500</td>\n",
-       "      <td>8.000000</td>\n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3306731</th>\n",
-       "      <td>3850000</td>\n",
-       "      <td>51.704510</td>\n",
-       "      <td>39.273037</td>\n",
-       "      <td>2072</td>\n",
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-       "      <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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-       "    <tr>\n",
-       "      <th>520293</th>\n",
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-       "      <td>12.921875</td>\n",
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-       "  </tbody>\n",
-       "</table>\n",
-       "<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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df"
    ]
@@ -320,20 +64,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['region', 'building_type', 'object_type']"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "cat_features = X_train.select_dtypes(include=['category','object']).columns.to_list()\n",
     "cat_features"
@@ -341,20 +74,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['geo_lat', 'geo_lon', 'level', 'levels', 'rooms', 'area', 'kitchen_area']"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "num_features = X_train.select_dtypes(include=['number']).columns.to_list()\n",
     "num_features"
@@ -402,1473 +124,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
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-      "Learning rate set to 0.105957\n",
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-      "text/html": [
-       "<style>#sk-container-id-1 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</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 0x7448bd575f60&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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><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 0x7448bd575f60&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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><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 fitted 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 fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><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 fitted 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 fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><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 fitted 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 fitted sk-toggleable__label-arrow fitted\">CatBoostRegressor</label><div class=\"sk-toggleable__content fitted\"><pre>&lt;catboost.core.CatBoostRegressor object at 0x7448bd575f60&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 0x7448bd575f60>)])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "\n",
     "pipeline = Pipeline(steps=[('preprocessor', preprocessor), \n",
@@ -1879,22 +137,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'mae': 1447931.3425270966,\n",
-       " 'mape': 1.6294525363466488e+18,\n",
-       " 'mse': 281898017343454.56}"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "predictions = pipeline.predict(X_test) \n",
     "\n",
@@ -1946,18 +191,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/andrey/work/institute/MLE/assets/mlflow/.venv_ml2/lib/python3.10/site-packages/mlflow/types/utils.py:407: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values <https://www.mlflow.org/docs/latest/models.html#handling-integers-with-missing-values>`_ for more details.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Обязательно логируем сигнатуру модели и пример входных данных. Подготовим их\n",
     "from mlflow.models import infer_signature\n",
@@ -1990,18 +226,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024/10/10 13:34:49 INFO mlflow.tracking._tracking_service.client: 🏃 View run baseline model at: http://127.0.0.1:5000/#/experiments/1/runs/06fa7ec1f1b74aedb3509c88dc4ee1c0.\n",
-      "2024/10/10 13:34:49 INFO mlflow.tracking._tracking_service.client: 🧪 View experiment at: http://127.0.0.1:5000/#/experiments/1.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Когда создаем новый эксперимент, то: \n",
     "experiment_id = mlflow.create_experiment(EXPERIMENT_NAME)\n",
@@ -2047,122 +274,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "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>run_id</th>\n",
-       "      <th>experiment_id</th>\n",
-       "      <th>status</th>\n",
-       "      <th>artifact_uri</th>\n",
-       "      <th>start_time</th>\n",
-       "      <th>end_time</th>\n",
-       "      <th>metrics.mape</th>\n",
-       "      <th>metrics.mae</th>\n",
-       "      <th>metrics.mse</th>\n",
-       "      <th>params.preprocessor__verbose_feature_names_out</th>\n",
-       "      <th>...</th>\n",
-       "      <th>params.preprocessor__num</th>\n",
-       "      <th>params.preprocessor__force_int_remainder_cols</th>\n",
-       "      <th>params.preprocessor__cat__unknown_value</th>\n",
-       "      <th>params.memory</th>\n",
-       "      <th>params.preprocessor__cat__categories</th>\n",
-       "      <th>tags.mlflow.source.type</th>\n",
-       "      <th>tags.mlflow.user</th>\n",
-       "      <th>tags.mlflow.log-model.history</th>\n",
-       "      <th>tags.mlflow.source.name</th>\n",
-       "      <th>tags.mlflow.runName</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>06fa7ec1f1b74aedb3509c88dc4ee1c0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>FINISHED</td>\n",
-       "      <td>mlflow-artifacts:/1/06fa7ec1f1b74aedb3509c88dc...</td>\n",
-       "      <td>2024-10-10 10:34:49.202000+00:00</td>\n",
-       "      <td>2024-10-10 10:34:49.765000+00:00</td>\n",
-       "      <td>1.629453e+18</td>\n",
-       "      <td>1.447931e+06</td>\n",
-       "      <td>2.818980e+14</td>\n",
-       "      <td>True</td>\n",
-       "      <td>...</td>\n",
-       "      <td>StandardScaler()</td>\n",
-       "      <td>True</td>\n",
-       "      <td>99999999</td>\n",
-       "      <td>None</td>\n",
-       "      <td>auto</td>\n",
-       "      <td>LOCAL</td>\n",
-       "      <td>andrey</td>\n",
-       "      <td>[{\"run_id\": \"06fa7ec1f1b74aedb3509c88dc4ee1c0\"...</td>\n",
-       "      <td>/home/andrey/work/institute/MLE/assets/mlflow/...</td>\n",
-       "      <td>baseline model</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1 rows × 40 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                             run_id experiment_id    status  \\\n",
-       "0  06fa7ec1f1b74aedb3509c88dc4ee1c0             1  FINISHED   \n",
-       "\n",
-       "                                        artifact_uri  \\\n",
-       "0  mlflow-artifacts:/1/06fa7ec1f1b74aedb3509c88dc...   \n",
-       "\n",
-       "                        start_time                         end_time  \\\n",
-       "0 2024-10-10 10:34:49.202000+00:00 2024-10-10 10:34:49.765000+00:00   \n",
-       "\n",
-       "   metrics.mape   metrics.mae   metrics.mse  \\\n",
-       "0  1.629453e+18  1.447931e+06  2.818980e+14   \n",
-       "\n",
-       "  params.preprocessor__verbose_feature_names_out  ...  \\\n",
-       "0                                           True  ...   \n",
-       "\n",
-       "  params.preprocessor__num params.preprocessor__force_int_remainder_cols  \\\n",
-       "0         StandardScaler()                                          True   \n",
-       "\n",
-       "  params.preprocessor__cat__unknown_value params.memory  \\\n",
-       "0                                99999999          None   \n",
-       "\n",
-       "  params.preprocessor__cat__categories tags.mlflow.source.type  \\\n",
-       "0                                 auto                   LOCAL   \n",
-       "\n",
-       "  tags.mlflow.user                      tags.mlflow.log-model.history  \\\n",
-       "0           andrey  [{\"run_id\": \"06fa7ec1f1b74aedb3509c88dc4ee1c0\"...   \n",
-       "\n",
-       "                             tags.mlflow.source.name tags.mlflow.runName  \n",
-       "0  /home/andrey/work/institute/MLE/assets/mlflow/...      baseline model  \n",
-       "\n",
-       "[1 rows x 40 columns]"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "mlflow.search_runs(\n",
     "    #experiment_ids=[experiment_id],\n",
@@ -2202,1034 +316,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024/10/10 13:34:49 WARNING mlflow.utils.autologging_utils: MLflow sklearn autologging is known to be compatible with 0.24.1 <= scikit-learn <= 1.5.1, but the installed version is 1.5.2. If you encounter errors during autologging, try upgrading / downgrading scikit-learn to a compatible version, or try upgrading MLflow.\n",
-      "2024/10/10 13:36:26 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"/home/andrey/work/institute/MLE/assets/mlflow/.venv_ml2/lib/python3.10/site-packages/mlflow/types/utils.py:407: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values <https://www.mlflow.org/docs/latest/models.html#handling-integers-with-missing-values>`_ for more details.\"\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate set to 0.105957\n",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024/10/10 13:36:41 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"/home/andrey/work/institute/MLE/assets/mlflow/.venv_ml2/lib/python3.10/site-packages/mlflow/types/utils.py:407: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values <https://www.mlflow.org/docs/latest/models.html#handling-integers-with-missing-values>`_ for more details.\"\n",
-      "2024/10/10 13:36:42 INFO mlflow.tracking._tracking_service.client: 🏃 View run auto at: http://127.0.0.1:5000/#/experiments/1/runs/9dd5dd7674d44c44be662e3c9bdbb5a4.\n",
-      "2024/10/10 13:36:42 INFO mlflow.tracking._tracking_service.client: 🧪 View experiment at: http://127.0.0.1:5000/#/experiments/1.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "mlflow.sklearn.autolog()\n",
     "\n",
@@ -3266,463 +355,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-3 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-3 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-3 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-3 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-3 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-3\" 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;, RandomForestRegressor(max_depth=6, n_estimators=10))])</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><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;, RandomForestRegressor(max_depth=6, n_estimators=10))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_depth=6, n_estimators=10)</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', RandomForestRegressor(max_depth=6, n_estimators=10))])"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "pipeline = Pipeline(steps=[('preprocessor', preprocessor), \n",
     "                           ('model', regressor2)])\n",
@@ -3732,22 +367,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'mae': 1714377.2944153566,\n",
-       " 'mape': 2.2781106914490788e+18,\n",
-       " 'mse': 406946525225456.6}"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "predictions = pipeline.predict(X_test) \n",
     "metrics = {}\n",
@@ -3760,18 +382,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024/10/10 13:36:50 INFO mlflow.tracking._tracking_service.client: 🏃 View run smaller_model at: http://127.0.0.1:5000/#/experiments/1/runs/638e0e7ad9f94ceaa01cacd1916771e2.\n",
-      "2024/10/10 13:36:50 INFO mlflow.tracking._tracking_service.client: 🧪 View experiment at: http://127.0.0.1:5000/#/experiments/1.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# !!! Проверить название прогона а также все логируемые параметры и артефакты, что они соответствуют второй \"маленькой\" модели. \n",
     "\n",
@@ -3799,18 +412,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024/10/10 13:36:50 INFO mlflow.tracking._tracking_service.client: 🏃 View run no_model at: http://127.0.0.1:5000/#/experiments/1/runs/3b80343fbdc2434ba18b42e049677a28.\n",
-      "2024/10/10 13:36:50 INFO mlflow.tracking._tracking_service.client: 🧪 View experiment at: http://127.0.0.1:5000/#/experiments/1.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# No model\n",
     "# Логировать можно только артефакты, без модели. Например, залогироавть графики после этапа EDA\n",
@@ -3834,29 +438,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Registered model 'estate_model_rf' already exists. Creating a new version of this model...\n",
-      "2024/10/10 13:37:19 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: estate_model_rf, version 1\n",
-      "Created version '1' of model 'estate_model_rf'.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<ModelVersion: aliases=[], creation_timestamp=1728556639835, current_stage='None', description='', last_updated_timestamp=1728556639835, name='estate_model_rf', run_id='06fa7ec1f1b74aedb3509c88dc4ee1c0', run_link='', source='mlflow-artifacts:/1/06fa7ec1f1b74aedb3509c88dc4ee1c0/artifacts/models', status='READY', status_message='', tags={}, user_id='', version='1'>"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "run_id = '06fa7ec1f1b74aedb3509c88dc4ee1c0' # Указываем run id\n",
     "mlflow.register_model(f\"runs:/{run_id}/models\", REGISTRY_MODEL_NAME)"
@@ -3864,21 +448,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Registered model 'estate_model_rf' already exists. Creating a new version of this model...\n",
-      "2024/10/10 13:37:21 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: estate_model_rf, version 2\n",
-      "Created version '2' of model 'estate_model_rf'.\n",
-      "2024/10/10 13:37:21 INFO mlflow.tracking._tracking_service.client: 🏃 View run register_at_run at: http://127.0.0.1:5000/#/experiments/1/runs/a34ac1da523a4687846c26f32d44c051.\n",
-      "2024/10/10 13:37:21 INFO mlflow.tracking._tracking_service.client: 🧪 View experiment at: http://127.0.0.1:5000/#/experiments/1.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Можно регистрировать сразу при создании прогона\n",
     "\n",
@@ -3904,20 +476,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<RegisteredModel: aliases={}, creation_timestamp=1728556610195, description='', last_updated_timestamp=1728556641409, latest_versions=[<ModelVersion: aliases=[], creation_timestamp=1728556641409, current_stage='None', description='', last_updated_timestamp=1728556641409, name='estate_model_rf', run_id='a34ac1da523a4687846c26f32d44c051', run_link='', source='mlflow-artifacts:/1/a34ac1da523a4687846c26f32d44c051/artifacts/models', status='READY', status_message='', tags={}, user_id='', version='2'>], name='estate_model_rf', tags={}>"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Можно найти зарегистрированные модели\n",
     "model_reg = mlflow.search_registered_models()\n",
@@ -3939,40 +500,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([3083461.0078044])"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model_loaded.predict(X_test.iloc[0:1])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
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     "y_test.iloc[0]"
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@@ -4035,261 +574,18 @@
   },
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-     "execution_count": 39,
-     "metadata": {},
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    "source": [
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    "cell_type": "code",
-   "execution_count": 42,
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    "metadata": {},
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    "source": [
     "pf.fit_transform(X_train_sklearn[['area','kitchen_area']])"
    ]
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   },
   {
    "cell_type": "code",
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-       "      <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>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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Удобно использовать для отображения всех строк\\столбцов в DataFrame\n",
     "with pd.option_context('display.max_rows', 5, 'display.max_columns', None):\n",
@@ -4764,1017 +749,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "pipeline_sklearn = Pipeline(steps=[\n",
     "    ('transform', preprocessor_sklearn),\n",
@@ -5786,539 +763,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-4 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-4 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-4 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-4 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-4 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-4 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;transform&#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;quantile&#x27;,\n",
-       "                                                  QuantileTransformer(),\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;poly&#x27;,\n",
-       "                                                  Pipeline(steps=[(&#x27;poly&#x27;,\n",
-       "                                                                   PolynomialFeatures()),\n",
-       "                                                                  (&#x27;scale&#x27;,\n",
-       "                                                                   StandardScaler())]),\n",
-       "                                                  [&#x27;area&#x27;, &#x27;kitchen_area&#x27;]),\n",
-       "                                                 (&#x27;spline&#x27;,\n",
-       "                                                  SplineTransformer(n_knots=3),\n",
-       "                                                  [&#x27;area&#x27;])])),\n",
-       "                (&#x27;model&#x27;,\n",
-       "                 &lt;catboost.core.CatBoostRegressor object at 0x7448bd575f60&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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;transform&#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;quantile&#x27;,\n",
-       "                                                  QuantileTransformer(),\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;poly&#x27;,\n",
-       "                                                  Pipeline(steps=[(&#x27;poly&#x27;,\n",
-       "                                                                   PolynomialFeatures()),\n",
-       "                                                                  (&#x27;scale&#x27;,\n",
-       "                                                                   StandardScaler())]),\n",
-       "                                                  [&#x27;area&#x27;, &#x27;kitchen_area&#x27;]),\n",
-       "                                                 (&#x27;spline&#x27;,\n",
-       "                                                  SplineTransformer(n_knots=3),\n",
-       "                                                  [&#x27;area&#x27;])])),\n",
-       "                (&#x27;model&#x27;,\n",
-       "                 &lt;catboost.core.CatBoostRegressor object at 0x7448bd575f60&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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;transform: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for transform: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><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;]),\n",
-       "                                (&#x27;quantile&#x27;, QuantileTransformer(),\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;poly&#x27;,\n",
-       "                                 Pipeline(steps=[(&#x27;poly&#x27;, PolynomialFeatures()),\n",
-       "                                                 (&#x27;scale&#x27;, StandardScaler())]),\n",
-       "                                 [&#x27;area&#x27;, &#x27;kitchen_area&#x27;]),\n",
-       "                                (&#x27;spline&#x27;, SplineTransformer(n_knots=3),\n",
-       "                                 [&#x27;area&#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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;, unknown_value=99999999)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">quantile</label><div class=\"sk-toggleable__content fitted\"><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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;QuantileTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.QuantileTransformer.html\">?<span>Documentation for QuantileTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>QuantileTransformer()</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">poly</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;area&#x27;, &#x27;kitchen_area&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;PolynomialFeatures<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.PolynomialFeatures.html\">?<span>Documentation for PolynomialFeatures</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>PolynomialFeatures()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">spline</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;area&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SplineTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.SplineTransformer.html\">?<span>Documentation for SplineTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SplineTransformer(n_knots=3)</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">CatBoostRegressor</label><div class=\"sk-toggleable__content fitted\"><pre>&lt;catboost.core.CatBoostRegressor object at 0x7448bd575f60&gt;</pre></div> </div></div></div></div></div></div>"
-      ],
-      "text/plain": [
-       "Pipeline(steps=[('transform',\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",
-       "                                                 ('quantile',\n",
-       "                                                  QuantileTransformer(),\n",
-       "                                                  ['geo_lat', 'geo_lon',\n",
-       "                                                   'level', 'levels', 'rooms',\n",
-       "                                                   'area', 'kitchen_area']),\n",
-       "                                                 ('poly',\n",
-       "                                                  Pipeline(steps=[('poly',\n",
-       "                                                                   PolynomialFeatures()),\n",
-       "                                                                  ('scale',\n",
-       "                                                                   StandardScaler())]),\n",
-       "                                                  ['area', 'kitchen_area']),\n",
-       "                                                 ('spline',\n",
-       "                                                  SplineTransformer(n_knots=3),\n",
-       "                                                  ['area'])])),\n",
-       "                ('model',\n",
-       "                 <catboost.core.CatBoostRegressor object at 0x7448bd575f60>)])"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model_sklearn"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'mae': 1371773.3280469999,\n",
-       " 'mape': 1.651780914989046e+18,\n",
-       " 'mse': 271999053666354.03}"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "predictions = model_sklearn.predict(X_test) \n",
     "metrics = {}\n",
@@ -6331,18 +787,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024/10/10 14:29:09 INFO mlflow.tracking._tracking_service.client: 🏃 View run fe_sklearn at: http://127.0.0.1:5000/#/experiments/1/runs/aa74ed5ed8aa48458ae26929bc237338.\n",
-      "2024/10/10 14:29:09 INFO mlflow.tracking._tracking_service.client: 🧪 View experiment at: http://127.0.0.1:5000/#/experiments/1.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "experiment_id = mlflow.get_experiment_by_name(EXPERIMENT_NAME).experiment_id\n",
     "RUN_NAME = 'fe_sklearn'\n",
@@ -6383,525 +830,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-10-10 14:34:43,949 INFO: [AutoFeat] The 2 step feature engineering process could generate up to 105 features.\n",
-      "2024-10-10 14:34:43,950 INFO: [AutoFeat] With 410775 data points this new feature matrix would use about 0.17 gb of space.\n",
-      "2024-10-10 14:34:43,986 INFO: [feateng] Step 1: transformation of original features\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[feateng]               0/              7 features transformed\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-10-10 14:34:45,030 INFO: [feateng] Generated 12 transformed features from 7 original features - done.\n",
-      "2024-10-10 14:34:45,061 INFO: [feateng] Step 2: first combination of features\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[feateng]             100/            171 feature tuples combined\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-10-10 14:34:47,541 INFO: [feateng] Generated 171 feature combinations from 171 original feature tuples - done.\n",
-      "2024-10-10 14:34:48,231 INFO: [feateng] Generated altogether 183 new features in 2 steps\n",
-      "2024-10-10 14:34:48,231 INFO: [feateng] Removing correlated features, as well as additions at the highest level\n",
-      "2024-10-10 14:34:49,298 INFO: [feateng] Generated a total of 98 additional features\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[featsel] Scaling data..."
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-10-10 14:34:49,789 INFO: [featsel] Feature selection run 1/5\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "done.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-10-10 14:35:35,384 INFO: [featsel] Feature selection run 2/5\n",
-      "2024-10-10 14:36:01,168 INFO: [featsel] Feature selection run 3/5\n",
-      "2024-10-10 14:36:22,873 INFO: [featsel] Feature selection run 4/5\n",
-      "2024-10-10 14:36:47,567 INFO: [featsel] Feature selection run 5/5\n",
-      "2024-10-10 14:37:16,308 INFO: [featsel] 53 features after 5 feature selection runs\n",
-      "/home/andrey/work/institute/MLE/assets/mlflow/.venv_ml2/lib/python3.10/site-packages/autofeat/featsel.py:270: FutureWarning: Series.ravel is deprecated. The underlying array is already 1D, so ravel is not necessary.  Use `to_numpy()` for conversion to a numpy array instead.\n",
-      "  if np.max(np.abs(correlations[c].ravel()[:i])) < 0.9:\n",
-      "2024-10-10 14:37:21,842 INFO: [featsel] 35 features after correlation filtering\n",
-      "2024-10-10 14:37:28,155 INFO: [featsel] 24 features after noise filtering\n",
-      "2024-10-10 14:37:28,214 INFO: [AutoFeat] Computing 18 new features.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[AutoFeat]    17/   18 new features\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-10-10 14:37:31,290 INFO: [AutoFeat]    18/   18 new features ...done.\n",
-      "2024-10-10 14:37:31,430 INFO: [AutoFeat] Final dataframe with 28 feature columns (18 new).\n",
-      "2024-10-10 14:37:31,431 INFO: [AutoFeat] Training final regression model.\n",
-      "2024-10-10 14:37:32,884 INFO: [AutoFeat] Trained model: largest coefficients:\n",
-      "2024-10-10 14:37:32,885 INFO: 1397647.486074062\n",
-      "2024-10-10 14:37:32,885 INFO: -444567.439631 * sqrt(area)*log(geo_lon)\n",
-      "2024-10-10 14:37:32,886 INFO: -229561.254963 * kitchen_area\n",
-      "2024-10-10 14:37:32,886 INFO: 228699.320633 * geo_lon\n",
-      "2024-10-10 14:37:32,887 INFO: -77449.633774 * sqrt(geo_lon)*sqrt(kitchen_area)\n",
-      "2024-10-10 14:37:32,887 INFO: 68865.207107 * sqrt(area)*log(levels)\n",
-      "2024-10-10 14:37:32,887 INFO: -58234.749991 * geo_lat*log(geo_lon)\n",
-      "2024-10-10 14:37:32,888 INFO: 53857.792425 * sqrt(area)*kitchen_area\n",
-      "2024-10-10 14:37:32,888 INFO: 48541.235065 * sqrt(area)*geo_lat\n",
-      "2024-10-10 14:37:32,888 INFO: -27253.343264 * geo_lon*rooms\n",
-      "2024-10-10 14:37:32,888 INFO: 27008.036497 * area*rooms\n",
-      "2024-10-10 14:37:32,889 INFO: -18811.054781 * object_type\n",
-      "2024-10-10 14:37:32,889 INFO: 15811.284677 * sqrt(area)*log(level)\n",
-      "2024-10-10 14:37:32,890 INFO: 5197.560812 * geo_lon*log(levels)\n",
-      "2024-10-10 14:37:32,890 INFO: 3144.707961 * geo_lat*log(kitchen_area)\n",
-      "2024-10-10 14:37:32,891 INFO: -1348.579984 * area*geo_lon\n",
-      "2024-10-10 14:37:32,891 INFO: -674.637306 * area*kitchen_area\n",
-      "2024-10-10 14:37:32,891 INFO: -445.508078 * region\n",
-      "2024-10-10 14:37:32,892 INFO: 63.854761 * area**(3/2)\n",
-      "2024-10-10 14:37:32,953 INFO: [AutoFeat] Final score: 0.0394\n"
-     ]
-    },
-    {
-     "data": {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>geo_lat</th>\n",
-       "      <th>geo_lon</th>\n",
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-       "      <th>building_type</th>\n",
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-       "      <th>...</th>\n",
-       "      <th>geo_lat*log(kitchen_area)</th>\n",
-       "      <th>sqrt(geo_lon)*sqrt(level)</th>\n",
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-       "      <th>sqrt(area)*log(levels)</th>\n",
-       "      <th>sqrt(area)*log(geo_lon)</th>\n",
-       "      <th>sqrt(area)*kitchen_area</th>\n",
-       "      <th>area**(3/2)</th>\n",
-       "      <th>geo_lon*rooms</th>\n",
-       "      <th>geo_lon*log(levels)</th>\n",
-       "      <th>geo_lat*log(geo_lon)</th>\n",
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-       "  <tbody>\n",
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-       "      <th>0</th>\n",
-       "      <td>56.327686</td>\n",
-       "      <td>43.928062</td>\n",
-       "      <td>2871.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>56.0000</td>\n",
-       "      <td>8.500000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>120.544976</td>\n",
-       "      <td>18.746320</td>\n",
-       "      <td>19.323264</td>\n",
-       "      <td>17.230969</td>\n",
-       "      <td>28.306037</td>\n",
-       "      <td>63.608176</td>\n",
-       "      <td>419.065627</td>\n",
-       "      <td>87.856125</td>\n",
-       "      <td>101.148102</td>\n",
-       "      <td>213.062479</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>54.859310</td>\n",
-       "      <td>82.975624</td>\n",
-       "      <td>9654.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>50.1875</td>\n",
-       "      <td>8.500000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>117.402553</td>\n",
-       "      <td>20.368557</td>\n",
-       "      <td>26.557349</td>\n",
-       "      <td>15.565828</td>\n",
-       "      <td>31.302372</td>\n",
-       "      <td>60.216666</td>\n",
-       "      <td>355.543992</td>\n",
-       "      <td>165.951248</td>\n",
-       "      <td>182.316081</td>\n",
-       "      <td>242.398434</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>56.072163</td>\n",
-       "      <td>54.231270</td>\n",
-       "      <td>2722.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>39.0000</td>\n",
-       "      <td>9.000000</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>123.203134</td>\n",
-       "      <td>14.728377</td>\n",
-       "      <td>22.092565</td>\n",
-       "      <td>13.721663</td>\n",
-       "      <td>24.937886</td>\n",
-       "      <td>56.204982</td>\n",
-       "      <td>243.554922</td>\n",
-       "      <td>54.231270</td>\n",
-       "      <td>119.158279</td>\n",
-       "      <td>223.910594</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>46.704327</td>\n",
-       "      <td>38.273636</td>\n",
-       "      <td>2843.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>48.0000</td>\n",
-       "      <td>9.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>102.619894</td>\n",
-       "      <td>13.833589</td>\n",
-       "      <td>18.559707</td>\n",
-       "      <td>11.150513</td>\n",
-       "      <td>25.251647</td>\n",
-       "      <td>62.353829</td>\n",
-       "      <td>332.553755</td>\n",
-       "      <td>76.547272</td>\n",
-       "      <td>61.599041</td>\n",
-       "      <td>170.226122</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>60.933483</td>\n",
-       "      <td>76.593094</td>\n",
-       "      <td>2484.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>74.0000</td>\n",
-       "      <td>10.500000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>143.277485</td>\n",
-       "      <td>23.154949</td>\n",
-       "      <td>28.358905</td>\n",
-       "      <td>23.295529</td>\n",
-       "      <td>37.321248</td>\n",
-       "      <td>90.324415</td>\n",
-       "      <td>636.572070</td>\n",
-       "      <td>153.186188</td>\n",
-       "      <td>207.417943</td>\n",
-       "      <td>264.360338</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",
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-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
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-       "    <tr>\n",
-       "      <th>410770</th>\n",
-       "      <td>56.771339</td>\n",
-       "      <td>60.612518</td>\n",
-       "      <td>6171.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>51.3125</td>\n",
-       "      <td>9.570312</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>128.227486</td>\n",
-       "      <td>23.356213</td>\n",
-       "      <td>24.084865</td>\n",
-       "      <td>20.704526</td>\n",
-       "      <td>29.401670</td>\n",
-       "      <td>68.554774</td>\n",
-       "      <td>367.565517</td>\n",
-       "      <td>121.225037</td>\n",
-       "      <td>175.192711</td>\n",
-       "      <td>233.018045</td>\n",
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-       "    <tr>\n",
-       "      <th>410771</th>\n",
-       "      <td>55.148613</td>\n",
-       "      <td>61.393780</td>\n",
-       "      <td>5282.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>43.0000</td>\n",
-       "      <td>6.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>98.813050</td>\n",
-       "      <td>13.571343</td>\n",
-       "      <td>19.192777</td>\n",
-       "      <td>10.553790</td>\n",
-       "      <td>26.998998</td>\n",
-       "      <td>39.344631</td>\n",
-       "      <td>281.969857</td>\n",
-       "      <td>122.787560</td>\n",
-       "      <td>98.809477</td>\n",
-       "      <td>227.063854</td>\n",
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-       "    <tr>\n",
-       "      <th>410772</th>\n",
-       "      <td>45.098129</td>\n",
-       "      <td>38.971218</td>\n",
-       "      <td>2843.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>39.0000</td>\n",
-       "      <td>9.000000</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>99.090718</td>\n",
-       "      <td>17.657003</td>\n",
-       "      <td>18.728080</td>\n",
-       "      <td>17.693412</td>\n",
-       "      <td>22.874325</td>\n",
-       "      <td>56.204982</td>\n",
-       "      <td>243.554922</td>\n",
-       "      <td>38.971218</td>\n",
-       "      <td>110.413775</td>\n",
-       "      <td>165.186482</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>410773</th>\n",
-       "      <td>50.583969</td>\n",
-       "      <td>36.581867</td>\n",
-       "      <td>5952.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>98.5000</td>\n",
-       "      <td>27.796875</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>168.187833</td>\n",
-       "      <td>10.475953</td>\n",
-       "      <td>31.888267</td>\n",
-       "      <td>27.517157</td>\n",
-       "      <td>35.724540</td>\n",
-       "      <td>275.876107</td>\n",
-       "      <td>977.584587</td>\n",
-       "      <td>73.163734</td>\n",
-       "      <td>101.426472</td>\n",
-       "      <td>182.079662</td>\n",
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-       "    <tr>\n",
-       "      <th>410774</th>\n",
-       "      <td>59.844570</td>\n",
-       "      <td>30.409077</td>\n",
-       "      <td>2661.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>72.0000</td>\n",
-       "      <td>9.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>131.491960</td>\n",
-       "      <td>13.507570</td>\n",
-       "      <td>16.543328</td>\n",
-       "      <td>21.085132</td>\n",
-       "      <td>28.975039</td>\n",
-       "      <td>76.367532</td>\n",
-       "      <td>610.940259</td>\n",
-       "      <td>91.227230</td>\n",
-       "      <td>75.563717</td>\n",
-       "      <td>204.353716</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>410775 rows × 28 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          geo_lat    geo_lon  region  building_type  level  levels  rooms  \\\n",
-       "0       56.327686  43.928062  2871.0            1.0    8.0    10.0    2.0   \n",
-       "1       54.859310  82.975624  9654.0            1.0    5.0     9.0    2.0   \n",
-       "2       56.072163  54.231270  2722.0            3.0    4.0     9.0    1.0   \n",
-       "3       46.704327  38.273636  2843.0            1.0    5.0     5.0    2.0   \n",
-       "4       60.933483  76.593094  2484.0            2.0    7.0    15.0    2.0   \n",
-       "...           ...        ...     ...            ...    ...     ...    ...   \n",
-       "410770  56.771339  60.612518  6171.0            2.0    9.0    18.0    2.0   \n",
-       "410771  55.148613  61.393780  5282.0            3.0    3.0     5.0    2.0   \n",
-       "410772  45.098129  38.971218  2843.0            0.0    8.0    17.0    1.0   \n",
-       "410773  50.583969  36.581867  5952.0            3.0    3.0    16.0    2.0   \n",
-       "410774  59.844570  30.409077  2661.0            1.0    6.0    12.0    3.0   \n",
-       "\n",
-       "           area  kitchen_area  object_type  ...  geo_lat*log(kitchen_area)  \\\n",
-       "0       56.0000      8.500000          1.0  ...                 120.544976   \n",
-       "1       50.1875      8.500000          1.0  ...                 117.402553   \n",
-       "2       39.0000      9.000000         11.0  ...                 123.203134   \n",
-       "3       48.0000      9.000000          1.0  ...                 102.619894   \n",
-       "4       74.0000     10.500000          1.0  ...                 143.277485   \n",
-       "...         ...           ...          ...  ...                        ...   \n",
-       "410770  51.3125      9.570312          1.0  ...                 128.227486   \n",
-       "410771  43.0000      6.000000          1.0  ...                  98.813050   \n",
-       "410772  39.0000      9.000000         11.0  ...                  99.090718   \n",
-       "410773  98.5000     27.796875          1.0  ...                 168.187833   \n",
-       "410774  72.0000      9.000000          1.0  ...                 131.491960   \n",
-       "\n",
-       "        sqrt(geo_lon)*sqrt(level)  sqrt(geo_lon)*sqrt(kitchen_area)  \\\n",
-       "0                       18.746320                         19.323264   \n",
-       "1                       20.368557                         26.557349   \n",
-       "2                       14.728377                         22.092565   \n",
-       "3                       13.833589                         18.559707   \n",
-       "4                       23.154949                         28.358905   \n",
-       "...                           ...                               ...   \n",
-       "410770                  23.356213                         24.084865   \n",
-       "410771                  13.571343                         19.192777   \n",
-       "410772                  17.657003                         18.728080   \n",
-       "410773                  10.475953                         31.888267   \n",
-       "410774                  13.507570                         16.543328   \n",
-       "\n",
-       "        sqrt(area)*log(levels)  sqrt(area)*log(geo_lon)  \\\n",
-       "0                    17.230969                28.306037   \n",
-       "1                    15.565828                31.302372   \n",
-       "2                    13.721663                24.937886   \n",
-       "3                    11.150513                25.251647   \n",
-       "4                    23.295529                37.321248   \n",
-       "...                        ...                      ...   \n",
-       "410770               20.704526                29.401670   \n",
-       "410771               10.553790                26.998998   \n",
-       "410772               17.693412                22.874325   \n",
-       "410773               27.517157                35.724540   \n",
-       "410774               21.085132                28.975039   \n",
-       "\n",
-       "        sqrt(area)*kitchen_area  area**(3/2)  geo_lon*rooms  \\\n",
-       "0                     63.608176   419.065627      87.856125   \n",
-       "1                     60.216666   355.543992     165.951248   \n",
-       "2                     56.204982   243.554922      54.231270   \n",
-       "3                     62.353829   332.553755      76.547272   \n",
-       "4                     90.324415   636.572070     153.186188   \n",
-       "...                         ...          ...            ...   \n",
-       "410770                68.554774   367.565517     121.225037   \n",
-       "410771                39.344631   281.969857     122.787560   \n",
-       "410772                56.204982   243.554922      38.971218   \n",
-       "410773               275.876107   977.584587      73.163734   \n",
-       "410774                76.367532   610.940259      91.227230   \n",
-       "\n",
-       "        geo_lon*log(levels)  geo_lat*log(geo_lon)  \n",
-       "0                101.148102            213.062479  \n",
-       "1                182.316081            242.398434  \n",
-       "2                119.158279            223.910594  \n",
-       "3                 61.599041            170.226122  \n",
-       "4                207.417943            264.360338  \n",
-       "...                     ...                   ...  \n",
-       "410770           175.192711            233.018045  \n",
-       "410771            98.809477            227.063854  \n",
-       "410772           110.413775            165.186482  \n",
-       "410773           101.426472            182.079662  \n",
-       "410774            75.563717            204.353716  \n",
-       "\n",
-       "[410775 rows x 28 columns]"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "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",
@@ -6973,18 +904,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/andrey/work/institute/MLE/assets/mlflow/.venv_ml2/lib/python3.10/site-packages/autofeat/featsel.py:270: FutureWarning: Series.ravel is deprecated. The underlying array is already 1D, so ravel is not necessary.  Use `to_numpy()` for conversion to a numpy array instead.\n",
-      "  if np.max(np.abs(correlations[c].ravel()[:i])) < 0.9:\n"
-     ]
-    }
-   ],
+   "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())"
@@ -6992,368 +914,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th></th>\n",
-       "      <th>num__geo_lat</th>\n",
-       "      <th>num__geo_lon</th>\n",
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-       "      <th>num__levels</th>\n",
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-       "      <th>cat__building_type</th>\n",
-       "      <th>cat__object_type</th>\n",
-       "      <th>afr__geo_lat</th>\n",
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-       "      <th>afr__levels</th>\n",
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-       "      <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",
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-       "      <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",
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-       "      <td>0.0</td>\n",
-       "      <td>0.495902</td>\n",
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-       "      <td>0.359235</td>\n",
-       "      <td>-0.214789</td>\n",
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-       "      <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",
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-       "      <td>-0.186285</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>1.0</td>\n",
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-       "      <td>1.191304</td>\n",
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-       "      <td>0.552794</td>\n",
-       "      <td>-0.056540</td>\n",
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-       "      <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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "with pd.option_context('display.max_rows', 5, 'display.max_columns', None):\n",
     "    display (X_train_afr)\n"
@@ -7380,22 +943,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'mae': 1381270.9152113453,\n",
-       " 'mape': 1.97323058892439e+18,\n",
-       " 'mse': 262290784020649.34}"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "predictions = pipeline_afr.predict(X_test) \n",
     "\n",
@@ -7409,18 +959,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024/10/10 14:53:08 INFO mlflow.tracking._tracking_service.client: 🏃 View run autofeat at: http://127.0.0.1:5000/#/experiments/1/runs/65961cc40d284389b693dcaf1c25a5cd.\n",
-      "2024/10/10 14:53:08 INFO mlflow.tracking._tracking_service.client: 🧪 View experiment at: http://127.0.0.1:5000/#/experiments/1.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "\n",
     "experiment_id = mlflow.get_experiment_by_name(EXPERIMENT_NAME).experiment_id\n",