notebooks/mod_07_topic_13_mlpipe.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Побудова моделі-прототипа. EDA датасету BigMart Sales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Знайомство з даними"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.compose import ColumnTransformer, make_column_selector\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.metrics import root_mean_squared_error\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import TargetEncoder\n",
    "from sklearn.utils import estimator_html_repr\n",
    "from scripts.mod_07_topic_13_transformer import VisRatioEstimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7503</th>\n",
       "      <td>FDI28</td>\n",
       "      <td>14.300</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.026300</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>79.4302</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>1743.0644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2957</th>\n",
       "      <td>NCM17</td>\n",
       "      <td>7.930</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.071136</td>\n",
       "      <td>Health and Hygiene</td>\n",
       "      <td>42.7086</td>\n",
       "      <td>OUT046</td>\n",
       "      <td>1997</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>356.8688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7031</th>\n",
       "      <td>FDC14</td>\n",
       "      <td>14.500</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.041313</td>\n",
       "      <td>Canned</td>\n",
       "      <td>42.0454</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>377.5086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1084</th>\n",
       "      <td>DRC36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.044767</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>173.7054</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>5778.4782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>FDS27</td>\n",
       "      <td>10.195</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.012456</td>\n",
       "      <td>Meat</td>\n",
       "      <td>197.5110</td>\n",
       "      <td>OUT035</td>\n",
       "      <td>2004</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2356.9320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4304</th>\n",
       "      <td>DRK12</td>\n",
       "      <td>9.500</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.041951</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>32.4900</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>865.5400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2132</th>\n",
       "      <td>FDO25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.126832</td>\n",
       "      <td>Canned</td>\n",
       "      <td>209.0270</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>4613.9940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385</th>\n",
       "      <td>FDB58</td>\n",
       "      <td>10.500</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.013551</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>142.0154</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>2410.8618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5239</th>\n",
       "      <td>FDY48</td>\n",
       "      <td>14.000</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.023832</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>103.1332</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>1948.1308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6516</th>\n",
       "      <td>NCP42</td>\n",
       "      <td>8.510</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016176</td>\n",
       "      <td>Household</td>\n",
       "      <td>192.1478</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>1937.4780</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Item_Identifier  Item_Weight  ...        Outlet_Type  Item_Outlet_Sales\n",
       "7503           FDI28       14.300  ...  Supermarket Type1          1743.0644\n",
       "2957           NCM17        7.930  ...  Supermarket Type1           356.8688\n",
       "7031           FDC14       14.500  ...  Supermarket Type1           377.5086\n",
       "1084           DRC36          NaN  ...  Supermarket Type3          5778.4782\n",
       "856            FDS27       10.195  ...  Supermarket Type1          2356.9320\n",
       "4304           DRK12        9.500  ...  Supermarket Type1           865.5400\n",
       "2132           FDO25          NaN  ...  Supermarket Type3          4613.9940\n",
       "1385           FDB58       10.500  ...  Supermarket Type2          2410.8618\n",
       "5239           FDY48       14.000  ...  Supermarket Type2          1948.1308\n",
       "6516           NCP42        8.510  ...  Supermarket Type2          1937.4780\n",
       "\n",
       "[10 rows x 12 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('../datasets/mod_07_topic_13_bigmart_data.csv')\n",
    "data.sample(10, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Перевірка типів даних і відсутніх значень"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8523 entries, 0 to 8522\n",
      "Data columns (total 12 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Item_Identifier            8523 non-null   object \n",
      " 1   Item_Weight                7060 non-null   float64\n",
      " 2   Item_Fat_Content           8523 non-null   object \n",
      " 3   Item_Visibility            8523 non-null   float64\n",
      " 4   Item_Type                  8523 non-null   object \n",
      " 5   Item_MRP                   8523 non-null   float64\n",
      " 6   Outlet_Identifier          8523 non-null   object \n",
      " 7   Outlet_Establishment_Year  8523 non-null   int64  \n",
      " 8   Outlet_Size                6113 non-null   object \n",
      " 9   Outlet_Location_Type       8523 non-null   object \n",
      " 10  Outlet_Type                8523 non-null   object \n",
      " 11  Item_Outlet_Sales          8523 non-null   float64\n",
      "dtypes: float64(4), int64(1), object(7)\n",
      "memory usage: 799.2+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Побудова моделі-прототипа. Підготовка й обробка даних"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Очистка даних. Генерація ознак"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Outlet_Establishment_Year'] = 2013 - data['Outlet_Establishment_Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Item_Visibility</th>\n",
       "      <th>Item_Visibility_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.070440</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.048885</td>\n",
       "      <td>0.643141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.003575</td>\n",
       "      <td>0.053965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.033085</td>\n",
       "      <td>0.489270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.060700</td>\n",
       "      <td>0.891396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.096335</td>\n",
       "      <td>1.367361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.328391</td>\n",
       "      <td>3.418374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Item_Visibility  Item_Visibility_Ratio\n",
       "count      8523.000000            8523.000000\n",
       "mean          0.070440               1.000000\n",
       "std           0.048885               0.643141\n",
       "min           0.003575               0.053965\n",
       "25%           0.033085               0.489270\n",
       "50%           0.060700               0.891396\n",
       "75%           0.096335               1.367361\n",
       "max           0.328391               3.418374"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Item_Visibility'] = (data['Item_Visibility']\n",
    "                           .mask(data['Item_Visibility'].eq(0), np.nan))\n",
    "\n",
    "data['Item_Visibility_Avg'] = (data\n",
    "                               .groupby(['Item_Type',\n",
    "                                         'Outlet_Type'])['Item_Visibility']\n",
    "                               .transform('mean'))\n",
    "\n",
    "data['Item_Visibility'] = (\n",
    "    data['Item_Visibility'].fillna(data['Item_Visibility_Avg']))\n",
    "\n",
    "data['Item_Visibility_Ratio'] = (\n",
    "    data['Item_Visibility'] / data['Item_Visibility_Avg'])\n",
    "\n",
    "data[['Item_Visibility', 'Item_Visibility_Ratio']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Low Fat', 'Regular', 'low fat', 'LF', 'reg'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Item_Fat_Content'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Item_Fat_Content'] = data['Item_Fat_Content'].replace({\n",
    "    'low fat': 'Low Fat',\n",
    "    'LF': 'Low Fat',\n",
    "    'reg': 'Regular'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Item_Identifier</th>\n",
       "      <th>Item_Identifier_Type</th>\n",
       "      <th>Item_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>FD</td>\n",
       "      <td>Dairy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>DR</td>\n",
       "      <td>Soft Drinks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>FD</td>\n",
       "      <td>Meat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>FD</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>NC</td>\n",
       "      <td>Household</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier Item_Identifier_Type              Item_Type\n",
       "0           FDA15                   FD                  Dairy\n",
       "1           DRC01                   DR            Soft Drinks\n",
       "2           FDN15                   FD                   Meat\n",
       "3           FDX07                   FD  Fruits and Vegetables\n",
       "4           NCD19                   NC              Household"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Item_Identifier_Type'] = data['Item_Identifier'].str[:2]\n",
    "\n",
    "data[['Item_Identifier', 'Item_Identifier_Type', 'Item_Type']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Розбиття на тренувальну і тестову вибірки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_num = data.select_dtypes(include=np.number)\n",
    "data_cat = data.select_dtypes(include='object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_num, X_test_num, X_train_cat,  X_test_cat, y_train, y_test = (\n",
    "    train_test_split(\n",
    "        data_num.drop(['Item_Outlet_Sales',\n",
    "                       'Item_Visibility_Avg'], axis=1),\n",
    "        data_cat.drop('Item_Identifier', axis=1),\n",
    "        data['Item_Outlet_Sales'],\n",
    "        test_size=0.2,\n",
    "        random_state=42))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Відновлення відсутніх значень"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_imputer = SimpleImputer().set_output(transform='pandas')\n",
    "\n",
    "X_train_num = num_imputer.fit_transform(X_train_num)\n",
    "X_test_num = num_imputer.transform(X_test_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_imputer = SimpleImputer(\n",
    "    strategy='most_frequent').set_output(transform='pandas')\n",
    "\n",
    "X_train_cat = cat_imputer.fit_transform(X_train_cat)\n",
    "X_test_cat = cat_imputer.transform(X_test_cat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Кодування категоріальних змінних"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "enc_auto = TargetEncoder(random_state=42).set_output(transform='pandas')\n",
    "\n",
    "X_train_cat = enc_auto.fit_transform(X_train_cat, y_train)\n",
    "X_test_cat = enc_auto.transform(X_test_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Item_Weight</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Item_Visibility_Ratio</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Identifier_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>9.500</td>\n",
       "      <td>0.035206</td>\n",
       "      <td>171.3448</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.522019</td>\n",
       "      <td>2238.717144</td>\n",
       "      <td>2313.314771</td>\n",
       "      <td>2396.339935</td>\n",
       "      <td>2322.325498</td>\n",
       "      <td>1923.240458</td>\n",
       "      <td>2349.148360</td>\n",
       "      <td>2249.390936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7757</th>\n",
       "      <td>18.000</td>\n",
       "      <td>0.047473</td>\n",
       "      <td>170.5422</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.797281</td>\n",
       "      <td>2185.153492</td>\n",
       "      <td>2242.978703</td>\n",
       "      <td>2235.553393</td>\n",
       "      <td>2307.426590</td>\n",
       "      <td>2345.791359</td>\n",
       "      <td>2330.820050</td>\n",
       "      <td>2134.228026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>17.600</td>\n",
       "      <td>0.076122</td>\n",
       "      <td>111.7202</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.338587</td>\n",
       "      <td>2239.327320</td>\n",
       "      <td>2273.782538</td>\n",
       "      <td>2281.275956</td>\n",
       "      <td>1942.398659</td>\n",
       "      <td>1889.358447</td>\n",
       "      <td>2330.820050</td>\n",
       "      <td>2250.249120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6867</th>\n",
       "      <td>8.325</td>\n",
       "      <td>0.029845</td>\n",
       "      <td>41.6138</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.442534</td>\n",
       "      <td>2146.154980</td>\n",
       "      <td>2322.811898</td>\n",
       "      <td>2227.452476</td>\n",
       "      <td>2263.075687</td>\n",
       "      <td>2319.032125</td>\n",
       "      <td>2318.980798</td>\n",
       "      <td>2212.157250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2716</th>\n",
       "      <td>12.850</td>\n",
       "      <td>0.137228</td>\n",
       "      <td>155.5630</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.086298</td>\n",
       "      <td>2146.154980</td>\n",
       "      <td>2272.157755</td>\n",
       "      <td>2267.795479</td>\n",
       "      <td>1911.170252</td>\n",
       "      <td>1874.494379</td>\n",
       "      <td>2318.980798</td>\n",
       "      <td>2212.157250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Item_Weight  Item_Visibility  ...  Outlet_Type  Item_Identifier_Type\n",
       "549         9.500         0.035206  ...  2349.148360           2249.390936\n",
       "7757       18.000         0.047473  ...  2330.820050           2134.228026\n",
       "764        17.600         0.076122  ...  2330.820050           2250.249120\n",
       "6867        8.325         0.029845  ...  2318.980798           2212.157250\n",
       "2716       12.850         0.137228  ...  2318.980798           2212.157250\n",
       "\n",
       "[5 rows x 12 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_concat = pd.concat([X_train_num, X_train_cat], axis=1)\n",
    "X_test_concat = pd.concat([X_test_num, X_test_cat], axis=1)\n",
    "\n",
    "X_train_concat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Побудова й оцінка моделі-прототипа"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Побудова базової моделі"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prototype's RMSE on test: 1047.4\n"
     ]
    }
   ],
   "source": [
    "clf = (RandomForestRegressor(\n",
    "    n_jobs=-1,\n",
    "    random_state=42)\n",
    "    .fit(X_train_concat,\n",
    "         y_train))\n",
    "\n",
    "pred_clf = clf.predict(X_test_concat)\n",
    "\n",
    "rmse_clf = root_mean_squared_error(y_test, pred_clf)\n",
    "\n",
    "print(f\"Prototype's RMSE on test: {rmse_clf:.1f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Важливість ознак. Відбір ознак у конвеєр"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme()\n",
    "\n",
    "(pd.Series(\n",
    "    data=clf.feature_importances_,\n",
    "    index=X_train_concat.columns)\n",
    "    .sort_values(ascending=True)\n",
    "    .plot\n",
    "    .barh())\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML-конвеєр. Особливості реалізації"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Створення нестандартного трансформера"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Item_Visibility_Ratio_prev</th>\n",
       "      <th>Item_Visibility_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7503</th>\n",
       "      <td>0.402694</td>\n",
       "      <td>0.402694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2957</th>\n",
       "      <td>1.319377</td>\n",
       "      <td>1.319377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7031</th>\n",
       "      <td>0.611682</td>\n",
       "      <td>0.611682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1084</th>\n",
       "      <td>0.700654</td>\n",
       "      <td>0.700654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>0.219033</td>\n",
       "      <td>0.219033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4304</th>\n",
       "      <td>0.663149</td>\n",
       "      <td>0.663149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2132</th>\n",
       "      <td>2.033351</td>\n",
       "      <td>2.033351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385</th>\n",
       "      <td>0.206023</td>\n",
       "      <td>0.206023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5239</th>\n",
       "      <td>0.341605</td>\n",
       "      <td>0.341605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6516</th>\n",
       "      <td>0.262587</td>\n",
       "      <td>0.262587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Item_Visibility_Ratio_prev  Item_Visibility_Ratio\n",
       "7503                    0.402694               0.402694\n",
       "2957                    1.319377               1.319377\n",
       "7031                    0.611682               0.611682\n",
       "1084                    0.700654               0.700654\n",
       "856                     0.219033               0.219033\n",
       "4304                    0.663149               0.663149\n",
       "2132                    2.033351               2.033351\n",
       "1385                    0.206023               0.206023\n",
       "5239                    0.341605               0.341605\n",
       "6516                    0.262587               0.262587"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vis_est = VisRatioEstimator()\n",
    "\n",
    "data = (data.rename(columns={\n",
    "    'Item_Visibility_Ratio': 'Item_Visibility_Ratio_prev'}))\n",
    "\n",
    "data = vis_est.fit_transform(data)\n",
    "\n",
    "(data[['Item_Visibility_Ratio_prev', 'Item_Visibility_Ratio']]\n",
    " .sample(10, random_state=42))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Побудова конвеєра"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
    "    ('encoder', TargetEncoder(random_state=42))])\n",
    "\n",
    "num_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer())])\n",
    "\n",
    "preprocessor = (ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('cat',\n",
    "         cat_transformer,\n",
    "         make_column_selector(dtype_include=object)),\n",
    "        ('num',\n",
    "         num_transformer,\n",
    "         make_column_selector(dtype_include=np.number))],\n",
    "    n_jobs=-1,\n",
    "    verbose_feature_names_out=False)\n",
    "    .set_output(transform='pandas'))\n",
    "\n",
    "model_pipeline = Pipeline(steps=[\n",
    "    ('vis_estimator', VisRatioEstimator()),\n",
    "    ('pre_processor', preprocessor),\n",
    "    ('reg_estimator', RandomForestRegressor(\n",
    "        n_jobs=-1,\n",
    "        random_state=42))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../derived/mod_07_topic_13_mlpipe.html', 'w', encoding='utf-8') as fl:\n",
    "    fl.write(estimator_html_repr(model_pipeline))\n",
    "\n",
    "with open('../models/mod_07_topic_13_mlpipe.joblib', 'wb') as fl:\n",
    "    joblib.dump(model_pipeline, fl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Відновлення даних. Розбивка на тренувальну і тестову вибірки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7503</th>\n",
       "      <td>FDI28</td>\n",
       "      <td>14.300</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.026300</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>79.4302</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>26</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>1743.0644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2957</th>\n",
       "      <td>NCM17</td>\n",
       "      <td>7.930</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.071136</td>\n",
       "      <td>Health and Hygiene</td>\n",
       "      <td>42.7086</td>\n",
       "      <td>OUT046</td>\n",
       "      <td>16</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>356.8688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7031</th>\n",
       "      <td>FDC14</td>\n",
       "      <td>14.500</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.041313</td>\n",
       "      <td>Canned</td>\n",
       "      <td>42.0454</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>14</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>377.5086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1084</th>\n",
       "      <td>DRC36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.044767</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>173.7054</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>28</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>5778.4782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>FDS27</td>\n",
       "      <td>10.195</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.012456</td>\n",
       "      <td>Meat</td>\n",
       "      <td>197.5110</td>\n",
       "      <td>OUT035</td>\n",
       "      <td>9</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2356.9320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4304</th>\n",
       "      <td>DRK12</td>\n",
       "      <td>9.500</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.041951</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>32.4900</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>14</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>865.5400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2132</th>\n",
       "      <td>FDO25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.126832</td>\n",
       "      <td>Canned</td>\n",
       "      <td>209.0270</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>28</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>4613.9940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385</th>\n",
       "      <td>FDB58</td>\n",
       "      <td>10.500</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.013551</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>142.0154</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>4</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>2410.8618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5239</th>\n",
       "      <td>FDY48</td>\n",
       "      <td>14.000</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.023832</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>103.1332</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>4</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>1948.1308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6516</th>\n",
       "      <td>NCP42</td>\n",
       "      <td>8.510</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016176</td>\n",
       "      <td>Household</td>\n",
       "      <td>192.1478</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>4</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>1937.4780</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Item_Identifier  Item_Weight  ...        Outlet_Type  Item_Outlet_Sales\n",
       "7503           FDI28       14.300  ...  Supermarket Type1          1743.0644\n",
       "2957           NCM17        7.930  ...  Supermarket Type1           356.8688\n",
       "7031           FDC14       14.500  ...  Supermarket Type1           377.5086\n",
       "1084           DRC36          NaN  ...  Supermarket Type3          5778.4782\n",
       "856            FDS27       10.195  ...  Supermarket Type1          2356.9320\n",
       "4304           DRK12        9.500  ...  Supermarket Type1           865.5400\n",
       "2132           FDO25          NaN  ...  Supermarket Type3          4613.9940\n",
       "1385           FDB58       10.500  ...  Supermarket Type2          2410.8618\n",
       "5239           FDY48       14.000  ...  Supermarket Type2          1948.1308\n",
       "6516           NCP42        8.510  ...  Supermarket Type2          1937.4780\n",
       "\n",
       "[10 rows x 12 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop([\n",
    "    'Item_Visibility_Avg',\n",
    "    'Item_Visibility_Ratio_prev',\n",
    "    'Item_Visibility_Ratio',\n",
    "    'Item_Identifier_Type'],\n",
    "    axis=1,\n",
    "    inplace=True)\n",
    "\n",
    "data.sample(10, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_pickle('../derived/mod_07_topic_13_bigmart_data_upd.pkl.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>9.500</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.035206</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>171.3448</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>14</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7757</th>\n",
       "      <td>18.000</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.047473</td>\n",
       "      <td>Household</td>\n",
       "      <td>170.5422</td>\n",
       "      <td>OUT045</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>17.600</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.076122</td>\n",
       "      <td>Meat</td>\n",
       "      <td>111.7202</td>\n",
       "      <td>OUT046</td>\n",
       "      <td>16</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6867</th>\n",
       "      <td>8.325</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.029845</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>41.6138</td>\n",
       "      <td>OUT045</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2716</th>\n",
       "      <td>12.850</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.137228</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>155.5630</td>\n",
       "      <td>OUT046</td>\n",
       "      <td>16</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5404</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.099780</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>225.2088</td>\n",
       "      <td>OUT019</td>\n",
       "      <td>28</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6657</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.014009</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>171.3422</td>\n",
       "      <td>OUT019</td>\n",
       "      <td>28</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1372</th>\n",
       "      <td>12.150</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.132831</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>190.6872</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2992</th>\n",
       "      <td>13.850</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.031339</td>\n",
       "      <td>Others</td>\n",
       "      <td>41.0480</td>\n",
       "      <td>OUT046</td>\n",
       "      <td>16</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.056597</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>230.9984</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>28</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Item_Weight Item_Fat_Content  ...  Outlet_Location_Type        Outlet_Type\n",
       "549         9.500          Regular  ...                Tier 1  Supermarket Type1\n",
       "7757       18.000          Low Fat  ...                Tier 2  Supermarket Type1\n",
       "764        17.600          Regular  ...                Tier 1  Supermarket Type1\n",
       "6867        8.325          Low Fat  ...                Tier 2  Supermarket Type1\n",
       "2716       12.850          Low Fat  ...                Tier 1  Supermarket Type1\n",
       "5404          NaN          Regular  ...                Tier 1      Grocery Store\n",
       "6657          NaN          Low Fat  ...                Tier 1      Grocery Store\n",
       "1372       12.150          Regular  ...                Tier 2  Supermarket Type1\n",
       "2992       13.850          Low Fat  ...                Tier 1  Supermarket Type1\n",
       "333           NaN          Regular  ...                Tier 3  Supermarket Type3\n",
       "\n",
       "[10 rows x 10 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = (\n",
    "    train_test_split(\n",
    "        data.drop(['Item_Identifier',\n",
    "                   'Item_Outlet_Sales'],\n",
    "                  axis=1),\n",
    "        data['Item_Outlet_Sales'],\n",
    "        test_size=0.2,\n",
    "        random_state=42))\n",
    "\n",
    "X_train.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Навчання й оцінка моделі в конвеєрі"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\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-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 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-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 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-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;vis_estimator&#x27;, VisRatioEstimator()),\n",
       "                (&#x27;pre_processor&#x27;,\n",
       "                 ColumnTransformer(n_jobs=-1,\n",
       "                                   transformers=[(&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;encoder&#x27;,\n",
       "                                                                   TargetEncoder(random_state=42))]),\n",
       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001BB7F242C50&gt;),\n",
       "                                                 (&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer())]),\n",
       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001BB0002AFD0&gt;)],\n",
       "                                   verbose_feature_names_out=False)),\n",
       "                (&#x27;reg_estimator&#x27;,\n",
       "                 RandomForestRegressor(n_jobs=-1, random_state=42))])</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-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" 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/1.4/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;vis_estimator&#x27;, VisRatioEstimator()),\n",
       "                (&#x27;pre_processor&#x27;,\n",
       "                 ColumnTransformer(n_jobs=-1,\n",
       "                                   transformers=[(&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;encoder&#x27;,\n",
       "                                                                   TargetEncoder(random_state=42))]),\n",
       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001BB7F242C50&gt;),\n",
       "                                                 (&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer())]),\n",
       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001BB0002AFD0&gt;)],\n",
       "                                   verbose_feature_names_out=False)),\n",
       "                (&#x27;reg_estimator&#x27;,\n",
       "                 RandomForestRegressor(n_jobs=-1, random_state=42))])</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-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">VisRatioEstimator</label><div class=\"sk-toggleable__content fitted\"><pre>VisRatioEstimator()</pre></div> </div></div><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-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;pre_processor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for pre_processor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(n_jobs=-1,\n",
       "                  transformers=[(&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;encoder&#x27;,\n",
       "                                                  TargetEncoder(random_state=42))]),\n",
       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001BB7F242C50&gt;),\n",
       "                                (&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer())]),\n",
       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001BB0002AFD0&gt;)],\n",
       "                  verbose_feature_names_out=False)</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-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001BB7F242C50&gt;</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-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</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-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TargetEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.TargetEncoder.html\">?<span>Documentation for TargetEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>TargetEncoder(random_state=42)</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-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001BB0002AFD0&gt;</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-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer()</pre></div> </div></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-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" 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/1.4/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(n_jobs=-1, random_state=42)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('vis_estimator', VisRatioEstimator()),\n",
       "                ('pre_processor',\n",
       "                 ColumnTransformer(n_jobs=-1,\n",
       "                                   transformers=[('cat',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('encoder',\n",
       "                                                                   TargetEncoder(random_state=42))]),\n",
       "                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x000001BB7F242C50>),\n",
       "                                                 ('num',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer())]),\n",
       "                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x000001BB0002AFD0>)],\n",
       "                                   verbose_feature_names_out=False)),\n",
       "                ('reg_estimator',\n",
       "                 RandomForestRegressor(n_jobs=-1, random_state=42))])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = model_pipeline.fit(X_train, y_train)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipe's RMSE on test: 1048.5\n"
     ]
    }
   ],
   "source": [
    "pred_pipe = model.predict(X_test)\n",
    "rmse_pipe = root_mean_squared_error(y_test, pred_pipe)\n",
    "\n",
    "print(f\"Pipe's RMSE on test: {rmse_pipe:.1f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Крос-валідація моделі в конвеєрі"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipe's RMSE on CV: 1128.6\n"
     ]
    }
   ],
   "source": [
    "cv_results = cross_val_score(\n",
    "    estimator=model_pipeline,\n",
    "    X=X_train,\n",
    "    y=y_train,\n",
    "    scoring='neg_root_mean_squared_error',\n",
    "    cv=5,\n",
    "    verbose=1)\n",
    "\n",
    "rmse_cv = np.abs(cv_results).mean()\n",
    "\n",
    "print(f\"Pipe's RMSE on CV: {rmse_cv:.1f}\")"
   ]
  }
 ],
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