data_challenge_bmw.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dash import Dash, dcc, html\n",
    "import dash_bootstrap_components as dbc\n",
    "from dash.dependencies import Input, Output, State\n",
    "from datetime import date, datetime\n",
    "from jupyter_dash import JupyterDash\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas_profiling import ProfileReport\n",
    "import plotly.express as px\n",
    "import plotly.figure_factory as ff\n",
    "import plotly.graph_objs as go\n",
    "from pprint import pprint\n",
    "import seaborn as sns\n",
    "from sklearn.compose import make_column_selector, make_column_transformer, ColumnTransformer\n",
    "from sklearn.dummy import DummyRegressor\n",
    "from sklearn.ensemble import HistGradientBoostingRegressor, RandomForestRegressor, GradientBoostingRegressor\n",
    "from sklearn.inspection import permutation_importance\n",
    "from sklearn.linear_model import Ridge, RidgeCV\n",
    "from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error\n",
    "from sklearn.model_selection import cross_validate, RandomizedSearchCV, train_test_split, ShuffleSplit, KFold\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder, StandardScaler\n",
    "from sklearn.utils import estimator_html_repr\n",
    "from scipy.stats import loguniform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>maker_key</th>\n",
       "      <th>model_key</th>\n",
       "      <th>mileage</th>\n",
       "      <th>engine_power</th>\n",
       "      <th>registration_date</th>\n",
       "      <th>fuel</th>\n",
       "      <th>paint_color</th>\n",
       "      <th>car_type</th>\n",
       "      <th>feature_1</th>\n",
       "      <th>feature_2</th>\n",
       "      <th>feature_3</th>\n",
       "      <th>feature_4</th>\n",
       "      <th>feature_5</th>\n",
       "      <th>feature_6</th>\n",
       "      <th>feature_7</th>\n",
       "      <th>feature_8</th>\n",
       "      <th>price</th>\n",
       "      <th>sold_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BMW</td>\n",
       "      <td>118</td>\n",
       "      <td>140411</td>\n",
       "      <td>100</td>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>diesel</td>\n",
       "      <td>black</td>\n",
       "      <td>convertible</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>11300</td>\n",
       "      <td>2018-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>M4</td>\n",
       "      <td>13929</td>\n",
       "      <td>317</td>\n",
       "      <td>2016-04-01</td>\n",
       "      <td>petrol</td>\n",
       "      <td>grey</td>\n",
       "      <td>convertible</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>69700</td>\n",
       "      <td>2018-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BMW</td>\n",
       "      <td>320</td>\n",
       "      <td>183297</td>\n",
       "      <td>120</td>\n",
       "      <td>2012-04-01</td>\n",
       "      <td>diesel</td>\n",
       "      <td>white</td>\n",
       "      <td>convertible</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>10200</td>\n",
       "      <td>2018-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>420</td>\n",
       "      <td>128035</td>\n",
       "      <td>135</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>diesel</td>\n",
       "      <td>red</td>\n",
       "      <td>convertible</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>25100</td>\n",
       "      <td>2018-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BMW</td>\n",
       "      <td>425</td>\n",
       "      <td>97097</td>\n",
       "      <td>160</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>diesel</td>\n",
       "      <td>silver</td>\n",
       "      <td>convertible</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>33400</td>\n",
       "      <td>2018-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4838</th>\n",
       "      <td>BMW</td>\n",
       "      <td>218 Gran Tourer</td>\n",
       "      <td>39743</td>\n",
       "      <td>110</td>\n",
       "      <td>2015-10-01</td>\n",
       "      <td>diesel</td>\n",
       "      <td>black</td>\n",
       "      <td>van</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>14600</td>\n",
       "      <td>2018-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4839</th>\n",
       "      <td>BMW</td>\n",
       "      <td>218 Active Tourer</td>\n",
       "      <td>49832</td>\n",
       "      <td>100</td>\n",
       "      <td>2015-06-01</td>\n",
       "      <td>diesel</td>\n",
       "      <td>grey</td>\n",
       "      <td>van</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>17500</td>\n",
       "      <td>2018-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4840</th>\n",
       "      <td>BMW</td>\n",
       "      <td>218 Gran Tourer</td>\n",
       "      <td>19633</td>\n",
       "      <td>110</td>\n",
       "      <td>2015-10-01</td>\n",
       "      <td>diesel</td>\n",
       "      <td>grey</td>\n",
       "      <td>van</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>17000</td>\n",
       "      <td>2018-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4841</th>\n",
       "      <td>BMW</td>\n",
       "      <td>218 Active Tourer</td>\n",
       "      <td>27920</td>\n",
       "      <td>110</td>\n",
       "      <td>2016-04-01</td>\n",
       "      <td>diesel</td>\n",
       "      <td>brown</td>\n",
       "      <td>van</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>22700</td>\n",
       "      <td>2018-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4842</th>\n",
       "      <td>BMW</td>\n",
       "      <td>525</td>\n",
       "      <td>195840</td>\n",
       "      <td>160</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>diesel</td>\n",
       "      <td>grey</td>\n",
       "      <td>van</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>15500</td>\n",
       "      <td>2018-09-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4843 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     maker_key          model_key  mileage  engine_power registration_date  \\\n",
       "0          BMW                118   140411           100        2012-02-01   \n",
       "1          BMW                 M4    13929           317        2016-04-01   \n",
       "2          BMW                320   183297           120        2012-04-01   \n",
       "3          BMW                420   128035           135        2014-07-01   \n",
       "4          BMW                425    97097           160        2014-12-01   \n",
       "...        ...                ...      ...           ...               ...   \n",
       "4838       BMW    218 Gran Tourer    39743           110        2015-10-01   \n",
       "4839       BMW  218 Active Tourer    49832           100        2015-06-01   \n",
       "4840       BMW    218 Gran Tourer    19633           110        2015-10-01   \n",
       "4841       BMW  218 Active Tourer    27920           110        2016-04-01   \n",
       "4842       BMW                525   195840           160        2013-01-01   \n",
       "\n",
       "        fuel paint_color     car_type  feature_1  feature_2  feature_3  \\\n",
       "0     diesel       black  convertible       True       True      False   \n",
       "1     petrol        grey  convertible       True       True      False   \n",
       "2     diesel       white  convertible      False      False      False   \n",
       "3     diesel         red  convertible       True       True      False   \n",
       "4     diesel      silver  convertible       True       True      False   \n",
       "...      ...         ...          ...        ...        ...        ...   \n",
       "4838  diesel       black          van      False       True      False   \n",
       "4839  diesel        grey          van      False       True      False   \n",
       "4840  diesel        grey          van      False       True      False   \n",
       "4841  diesel       brown          van       True       True      False   \n",
       "4842  diesel        grey          van       True       True      False   \n",
       "\n",
       "      feature_4  feature_5  feature_6  feature_7  feature_8  price    sold_at  \n",
       "0         False       True       True       True      False  11300 2018-01-01  \n",
       "1         False      False       True       True       True  69700 2018-02-01  \n",
       "2         False       True      False       True      False  10200 2018-02-01  \n",
       "3         False       True       True       True       True  25100 2018-02-01  \n",
       "4         False      False       True       True       True  33400 2018-04-01  \n",
       "...         ...        ...        ...        ...        ...    ...        ...  \n",
       "4838      False      False      False       True      False  14600 2018-08-01  \n",
       "4839      False      False      False       True       True  17500 2018-08-01  \n",
       "4840      False      False      False       True       True  17000 2018-09-01  \n",
       "4841      False      False      False       True       True  22700 2018-09-01  \n",
       "4842      False       True      False       True       True  15500 2018-09-01  \n",
       "\n",
       "[4843 rows x 18 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_price_df = pd.read_csv(\"data_pricing_challenge.csv\", parse_dates=[\"registration_date\", \"sold_at\"])\n",
    "car_price_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Nulls in Dataset: 0\n",
      "Data types:\n",
      "maker_key                    object\n",
      "model_key                    object\n",
      "mileage                       int64\n",
      "engine_power                  int64\n",
      "registration_date    datetime64[ns]\n",
      "fuel                         object\n",
      "paint_color                  object\n",
      "car_type                     object\n",
      "feature_1                      bool\n",
      "feature_2                      bool\n",
      "feature_3                      bool\n",
      "feature_4                      bool\n",
      "feature_5                      bool\n",
      "feature_6                      bool\n",
      "feature_7                      bool\n",
      "feature_8                      bool\n",
      "price                         int64\n",
      "sold_at              datetime64[ns]\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# check for null values\n",
    "print(f\"Total Nulls in Dataset: {car_price_df.isnull().sum().sum()}\")\n",
    "# check data types\n",
    "print(f\"Data types:\\n{car_price_df.dtypes}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['BMW']\n"
     ]
    }
   ],
   "source": [
    "# check if all BMW\n",
    "print(car_price_df.maker_key.unique())\n",
    "features = [f\"feature_{i}\" for i in range(1,9)]\n",
    "cat_var = [\"model_key\", \"fuel\", \"paint_color\", \"car_type\", *features]\n",
    "num_var = [\"mileage\", \"engine_power\", \"registration_date\", \"sold_at\"]\n",
    "target = [\"price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sold year values: [2018]\n",
      "Sales per month:\n",
      "5    809\n",
      "3    739\n",
      "4    693\n",
      "6    604\n",
      "7    537\n",
      "8    528\n",
      "2    502\n",
      "9    223\n",
      "1    208\n",
      "Name: sold_at, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# check 2018 is sold year only\n",
    "print(f\"Sold year values: {car_price_df.sold_at.dt.year.unique()}\")\n",
    "# check number of sales per month\n",
    "print(f\"Sales per month:\\n{car_price_df.sold_at.dt.month.value_counts()}\")\n",
    "# transform column to sold month (model seasonality)\n",
    "car_price_df.loc[:, \"sold_at\"] = car_price_df.sold_at.dt.month\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Registration years:\n",
      "2013    1546\n",
      "2014    1288\n",
      "2012     820\n",
      "2015     311\n",
      "2011     215\n",
      "2008     103\n",
      "2010     103\n",
      "2009      87\n",
      "2016      83\n",
      "2006      67\n",
      "2007      59\n",
      "2005      54\n",
      "2004      28\n",
      "2001      17\n",
      "2003      16\n",
      "2017      11\n",
      "2000      10\n",
      "2002       9\n",
      "1997       5\n",
      "1999       4\n",
      "1996       2\n",
      "1998       2\n",
      "1995       1\n",
      "1990       1\n",
      "1994       1\n",
      "Name: registration_date, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# count registration years\n",
    "print(f\"Registration years:\\n{car_price_df.registration_date.dt.year.value_counts()}\")\n",
    "# transform column to registrated year (simplify car age)\n",
    "car_price_df.loc[:, \"registration_date\"] = car_price_df.registration_date.dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            mileage  engine_power  registration_date      sold_at  \\\n",
      "count  4.843000e+03    4843.00000        4843.000000  4843.000000   \n",
      "mean   1.409628e+05     128.98823        2012.467479     4.926698   \n",
      "std    6.019674e+04      38.99336           2.526997     2.157615   \n",
      "min   -6.400000e+01       0.00000        1990.000000     1.000000   \n",
      "25%    1.029135e+05     100.00000        2012.000000     3.000000   \n",
      "50%    1.410800e+05     120.00000        2013.000000     5.000000   \n",
      "75%    1.751955e+05     135.00000        2014.000000     7.000000   \n",
      "max    1.000376e+06     423.00000        2017.000000     9.000000   \n",
      "\n",
      "               price  \n",
      "count    4843.000000  \n",
      "mean    15828.081767  \n",
      "std      9220.285684  \n",
      "min       100.000000  \n",
      "25%     10800.000000  \n",
      "50%     14200.000000  \n",
      "75%     18600.000000  \n",
      "max    178500.000000  \n",
      "Cars with zero engine_power entries: 1\n",
      "Cars with negative mileage entries: 1\n"
     ]
    }
   ],
   "source": [
    "# describe numerical variables mileage, engine_power and target price\n",
    "print(car_price_df[[*num_var, *target]].describe())\n",
    "# zero engine_power entries\n",
    "print(f\"Cars with zero engine_power entries: {(car_price_df.engine_power == 0).sum()}\")\n",
    "# cars with negative mileage entries\n",
    "print(f\"Cars with negative mileage entries: {(car_price_df.mileage < 0).sum()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Engine Power counts:\n",
      "135    882\n",
      "120    785\n",
      "85     631\n",
      "100    570\n",
      "105    451\n",
      "190    319\n",
      "140    166\n",
      "160    153\n",
      "110    142\n",
      "230    119\n",
      "155     99\n",
      "180     62\n",
      "225     49\n",
      "130     47\n",
      "150     43\n",
      "90      40\n",
      "280     32\n",
      "122     30\n",
      "173     25\n",
      "210     21\n",
      "137     19\n",
      "125     16\n",
      "170     14\n",
      "70      11\n",
      "145      9\n",
      "75       9\n",
      "235      7\n",
      "142      6\n",
      "87       6\n",
      "240      5\n",
      "220      5\n",
      "127      5\n",
      "112      5\n",
      "80       4\n",
      "119      4\n",
      "309      4\n",
      "317      4\n",
      "77       3\n",
      "195      3\n",
      "107      3\n",
      "95       3\n",
      "200      3\n",
      "25       2\n",
      "192      2\n",
      "101      2\n",
      "189      2\n",
      "141      2\n",
      "270      2\n",
      "66       2\n",
      "183      2\n",
      "233      2\n",
      "224      1\n",
      "423      1\n",
      "300      1\n",
      "412      1\n",
      "250      1\n",
      "121      1\n",
      "239      1\n",
      "236      1\n",
      "261      1\n",
      "Name: engine_power, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# drop the two cases above\n",
    "car_price_df.drop(car_price_df[car_price_df.engine_power == 0].index, inplace=True)\n",
    "car_price_df.drop(car_price_df[car_price_df.mileage < 0].index, inplace=True)\n",
    "\n",
    "# count engine power\n",
    "print(f\"Engine Power counts:\\n{car_price_df.engine_power.value_counts()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inspect num_var distributions\n",
    "fig = plt.figure(figsize=[12,4],constrained_layout=True)\n",
    "fig.suptitle('Numerical Variables Distributions', size=14, weight='bold',y=1.05)\n",
    "spec = gridspec.GridSpec(ncols=5, nrows=1, figure=fig)\n",
    "ax = [fig.add_subplot(spec[0, i]) for i in range(5)]\n",
    "label_prop = {'size':13, 'style':'oblique'}\n",
    "\n",
    "for i, var in enumerate([*num_var, *target]):\n",
    "    sns.histplot(car_price_df[var], ax=ax[i])\n",
    "    # Tweak using Matplotlib\n",
    "    plt.sca(ax[i])\n",
    "    plt.xticks(fontsize=8)\n",
    "    plt.yticks(fontsize=10)\n",
    "    plt.xlabel(var, fontdict=label_prop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the correlation matrix on numeric variables\n",
    "corr_num_var = car_price_df[[*num_var, *target]].corr(method='spearman')\n",
    "\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.triu(np.ones_like(corr_num_var, dtype=bool))\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(6, 4))\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "g = sns.heatmap(\n",
    "    corr_num_var,\n",
    "    mask=mask,\n",
    "    cmap=cmap,\n",
    "    square=True,\n",
    "    linewidths=.5,\n",
    "    cbar_kws={\"shrink\": .5},\n",
    "    annot=True,\n",
    ")\n",
    "plt.xticks(fontsize=10, rotation=45)\n",
    "plt.yticks(fontsize=10)\n",
    "# use matplotlib.colorbar.Colorbar object\n",
    "cbar = g.collections[0].colorbar\n",
    "# here set the labelsize\n",
    "cbar.ax.tick_params(labelsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\">feature_8</text></g></g><g class=\"overaxes-above\"/></g></g><g class=\"polarlayer\"/><g class=\"smithlayer\"/><g class=\"ternarylayer\"/><g class=\"geolayer\"/><g class=\"funnelarealayer\"/><g class=\"pielayer\"/><g class=\"iciclelayer\"/><g class=\"treemaplayer\"/><g class=\"sunburstlayer\"/><g class=\"glimages\"/><defs id=\"topdefs-300eb9\"><g class=\"clips\"/></defs><g class=\"layer-above\"><g class=\"imagelayer\"/><g class=\"shapelayer\"/></g><g class=\"infolayer\"><g class=\"g-gtitle\"><text class=\"gtitle\" x=\"35\" y=\"50\" text-anchor=\"start\" dy=\"0em\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 17px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Categorical Variables Cardinality</text></g><g class=\"g-xtitle\"/><g class=\"g-ytitle\"/></g></svg>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = go.Figure()\n",
    "for col in cat_var:\n",
    "    counted_df = car_price_df.groupby(col).size().to_frame(col).T\n",
    "    bar_plot = px.bar(counted_df, orientation='h')\n",
    "    for category in bar_plot.data:\n",
    "        fig.add_trace(category)\n",
    "fig.update_layout(showlegend=False, barmode='stack', title='Categorical Variables Cardinality')\n",
    "fig.show(renderer=\"svg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "black     1632\n",
      "grey      1175\n",
      "blue       710\n",
      "white      537\n",
      "brown      341\n",
      "silver     329\n",
      "red         52\n",
      "beige       41\n",
      "green       18\n",
      "orange       6\n",
      "Name: paint_color, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Decisions to make on Categorical Variables\n",
    "# count car colors\n",
    "print(car_price_df.paint_color.value_counts())\n",
    "# Merge minority colors\n",
    "minority_colors = ['red','beige','green','orange'] # ['green','orange'] #\n",
    "car_price_df.loc[:, \"paint_color\"] = np.where(\n",
    "    car_price_df.paint_color.isin(minority_colors), 'other', car_price_df.paint_color\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "diesel           4639\n",
      "petrol            191\n",
      "hybrid_petrol       8\n",
      "electro             3\n",
      "Name: fuel, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# count fuel\n",
    "print(car_price_df.fuel.value_counts())\n",
    "\n",
    "# drop hybrids and electrics\n",
    "car_price_df.drop(car_price_df[car_price_df.fuel.isin(['hybrid_petrol','electro'])].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "72\n",
      "300 series    1807\n",
      "500 series    1152\n",
      "100 series     564\n",
      "X3 series      438\n",
      "X1 series      274\n",
      "X5 series      249\n",
      "400 series     111\n",
      "200 series      54\n",
      "700 series      54\n",
      "X6 series       51\n",
      "X4 series       46\n",
      "600 series      30\n",
      "Name: model_key, dtype: int64\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "# count model keys\n",
    "# print(car_price_df.model_key.value_counts())\n",
    "print(len(car_price_df.model_key.unique()))\n",
    "\n",
    "# group model keys by series\n",
    "for i in range(1, 8):\n",
    "    model_mask = (\n",
    "        car_price_df.model_key.str.startswith(f\"{i}\") |\n",
    "        car_price_df.model_key.str.startswith(f\"M{i}\") |\n",
    "        car_price_df.model_key.str.startswith(f\"Z{i}\")\n",
    "    )\n",
    "    car_price_df.loc[model_mask, \"model_key\"] = f\"{i}00 series\"\n",
    "    car_price_df.loc[car_price_df.model_key.str.startswith(f\"X{i}\"), \"model_key\"] = f\"X{i} series\"\n",
    "\n",
    "print(car_price_df.model_key.value_counts())\n",
    "print(len(car_price_df.model_key.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# matrix of features\n",
    "feature_df = car_price_df[cat_var + num_var]\n",
    "y = car_price_df[target].values.reshape(-1)\n",
    "\n",
    "n_cat = len(cat_var)\n",
    "n_num = len(num_var)\n",
    "\n",
    "numerical_columns_selector = make_column_selector(dtype_include=\"int64\")\n",
    "categorical_columns_selector = make_column_selector(dtype_exclude=\"int64\")\n",
    "\n",
    "numerical_columns = numerical_columns_selector(feature_df)\n",
    "categorical_columns = categorical_columns_selector(feature_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy_regressor = DummyRegressor(strategy=\"median\")\n",
    "\n",
    "# save pipeline diagram in html file\n",
    "with open('/workspaces/data_challenge/presentation/imgs/dummy_regressor.html', 'w') as f:\n",
    "    f.write(estimator_html_repr(dummy_regressor))\n",
    "\n",
    "cv_sfold = KFold(n_splits=5, shuffle=True, random_state=42)\n",
    "\n",
    "dummy_result = cross_validate(\n",
    "    dummy_regressor,\n",
    "    feature_df,\n",
    "    y,\n",
    "    cv=cv_sfold,\n",
    "    scoring=[\n",
    "        \"r2\",\n",
    "        \"neg_mean_absolute_percentage_error\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_preprocessor = OneHotEncoder(handle_unknown=\"ignore\")\n",
    "numerical_preprocessor = StandardScaler()\n",
    "\n",
    "ridge_preprocessor = ColumnTransformer([\n",
    "    ('one-hot-encoder', categorical_preprocessor, categorical_columns),\n",
    "    ('standard_scaler', numerical_preprocessor, numerical_columns)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_ridge_model = Pipeline([\n",
    "    (\"preprocessor\", ridge_preprocessor),\n",
    "    (\"ridge_reg\", Ridge()),\n",
    "])\n",
    "\n",
    "# save pipeline diagram in html file\n",
    "with open('/workspaces/data_challenge/presentation/imgs/ridge_regressor.html', 'w') as f:\n",
    "    f.write(estimator_html_repr(lin_ridge_model))\n",
    "\n",
    "lin_ridge_result = cross_validate(\n",
    "    lin_ridge_model,\n",
    "    feature_df,\n",
    "    y,\n",
    "    cv=cv_sfold,\n",
    "    scoring=[\n",
    "        \"r2\",\n",
    "        \"neg_mean_absolute_percentage_error\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_unknown_value = np.nan\n",
    "\n",
    "ordinal_encoder = make_column_transformer(\n",
    "    (\n",
    "        OrdinalEncoder(handle_unknown=\"use_encoded_value\", unknown_value=set_unknown_value),\n",
    "        categorical_columns_selector,\n",
    "    ),\n",
    "    remainder=\"passthrough\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "rand_forest = Pipeline([\n",
    "    (\"preprocessor\", ordinal_encoder),\n",
    "    (\"regressor\", RandomForestRegressor(\n",
    "        n_jobs=2,\n",
    "        # random_state=42,\n",
    "    )),\n",
    "])\n",
    "\n",
    "# save pipeline diagram in html file\n",
    "with open('/workspaces/data_challenge/presentation/imgs/rnd_for_regressor.html', 'w') as f:\n",
    "    f.write(estimator_html_repr(rand_forest))\n",
    "\n",
    "rnd_for_result = cross_validate(\n",
    "    rand_forest,\n",
    "    feature_df,\n",
    "    y,\n",
    "    cv=cv_sfold,\n",
    "    scoring=[\n",
    "        \"r2\",\n",
    "        \"neg_mean_absolute_percentage_error\",\n",
    "    ],\n",
    "    return_train_score=True,\n",
    "    return_estimator=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "grad_boost = Pipeline([\n",
    "    (\"preprocessor\", ordinal_encoder),\n",
    "    (\"regressor\", GradientBoostingRegressor()), # random_state=42)),\n",
    "])\n",
    "\n",
    "grad_boost_result = cross_validate(\n",
    "    grad_boost,\n",
    "    feature_df,\n",
    "    y,\n",
    "    cv=cv_sfold,\n",
    "    scoring=[\n",
    "        \"r2\",\n",
    "        \"neg_mean_absolute_percentage_error\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The ordinal encoder will first output the categorical features, and then the\n",
    "# continuous (passed-through) features\n",
    "categorical_mask = [True] * n_cat + [False] * n_num\n",
    "\n",
    "hist_native = Pipeline([\n",
    "    (\"preprocessor\", ordinal_encoder),\n",
    "    (\"regressor\", HistGradientBoostingRegressor(\n",
    "        # random_state=42,\n",
    "        categorical_features=categorical_mask,\n",
    "    )),\n",
    "])\n",
    "\n",
    "hist_native_result = cross_validate(\n",
    "    hist_native,\n",
    "    feature_df,\n",
    "    y,\n",
    "    cv=cv_sfold,\n",
    "    scoring=\"neg_mean_absolute_percentage_error\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'R2 score')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 6))\n",
    "x, width = np.arange(4), 0.9\n",
    "\n",
    "items = [\n",
    "    dummy_result[\"test_r2\"],\n",
    "    lin_ridge_result[\"test_r2\"],\n",
    "    rnd_for_result[\"test_r2\"],\n",
    "    grad_boost_result[\"test_r2\"],\n",
    "    # hist_native_result[\"test_score\"],\n",
    "]\n",
    "\n",
    "cv_mean = [np.mean(np.abs(item)) for item in items]\n",
    "cv_std = [np.std(item) for item in items]\n",
    "\n",
    "ax.bar(\n",
    "    x=x,\n",
    "    height=cv_mean,\n",
    "    width=width,\n",
    "    yerr=cv_std,\n",
    "    color=[\"C0\", \"C1\", \"C2\", \"C3\"],\n",
    ")\n",
    "ax.set(\n",
    "    # xlabel=\"Model\",\n",
    "    # ylabel=\"Mean Absolute Error [%]\",\n",
    "    # title=\"Mean Absolute Error [%]\",\n",
    "    xticks=x,\n",
    "    xticklabels=[\"Dummy\", \"Linear Ridge\", \"Rand. Forest\", \"Grad. Boost\"],\n",
    ")\n",
    "fig.suptitle(\"Regression Models - Baseline Performance\", size=14)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "ax.set_ylabel(\"R2 score\", fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Mean Absolute Error [%]')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 6))\n",
    "x, width = np.arange(4), 0.9\n",
    "\n",
    "items = [\n",
    "    dummy_result[\"test_neg_mean_absolute_percentage_error\"],\n",
    "    lin_ridge_result[\"test_neg_mean_absolute_percentage_error\"],\n",
    "    rnd_for_result[\"test_neg_mean_absolute_percentage_error\"],\n",
    "    grad_boost_result[\"test_neg_mean_absolute_percentage_error\"],\n",
    "    # hist_native_result[\"test_score\"],\n",
    "]\n",
    "\n",
    "cv_mean = [np.mean(np.abs(item))*100 for item in items]\n",
    "cv_std = [np.std(item)*100 for item in items]\n",
    "\n",
    "ax.bar(\n",
    "    x=x,\n",
    "    height=cv_mean,\n",
    "    width=width,\n",
    "    yerr=cv_std,\n",
    "    color=[\"C0\", \"C1\", \"C2\", \"C3\"],\n",
    ")\n",
    "ax.set(\n",
    "    # xlabel=\"Model\",\n",
    "    # ylabel=\"Mean Absolute Error [%]\",\n",
    "    # title=\"Mean Absolute Error [%]\",\n",
    "    xticks=x,\n",
    "    xticklabels=[\"Dummy\", \"Linear Ridge\", \"Rand. Forest\", \"Grad. Boost\"],\n",
    ")\n",
    "fig.suptitle(\"Regression Models - Baseline Performance\", size=14)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "ax.set_ylabel(\"Mean Absolute Error [%]\", fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_ridge_alphas = np.logspace(-2, 1, num=30)\n",
    "\n",
    "# Declare the inner and outer cross-validation strategies\n",
    "# inner_cv = KFold(n_splits=3, shuffle=True, random_state=42)\n",
    "# outer_cv = KFold(n_splits=3, shuffle=True, random_state=42)\n",
    "inner_cv = cv_sfold\n",
    "outer_cv = cv_sfold\n",
    "\n",
    "lin_ridge_cv = Pipeline([\n",
    "    (\"preprocessor\", ridge_preprocessor),\n",
    "    (\"ridge_cv\", RidgeCV(\n",
    "        alphas=lin_ridge_alphas,\n",
    "        # store_cv_values=True,\n",
    "        cv=inner_cv,\n",
    "        scoring=\"neg_mean_absolute_error\",\n",
    "    )),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_results_lin_ridge = cross_validate(\n",
    "    lin_ridge_cv,\n",
    "    feature_df,\n",
    "    y,\n",
    "    cv=outer_cv,\n",
    "    scoring=[\n",
    "        \"r2\",\n",
    "        \"neg_mean_absolute_percentage_error\",\n",
    "        \"neg_mean_squared_error\",\n",
    "        \"neg_root_mean_squared_error\",\n",
    "        \"neg_mean_absolute_error\",\n",
    "        \"neg_median_absolute_error\",\n",
    "    ],\n",
    "    return_train_score=True,\n",
    "    return_estimator=True,\n",
    "    n_jobs=2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_distributions_rand_forest = {\n",
    "    \"regressor__n_estimators\": [1, 2, 5, 10, 20, 50, 100, 200, 500],\n",
    "    \"regressor__max_leaf_nodes\": [2, 5, 10, 20, 50, 100],\n",
    "}\n",
    "\n",
    "rand_forest_search_cv = RandomizedSearchCV(\n",
    "    rand_forest,\n",
    "    param_distributions=param_distributions_rand_forest,\n",
    "    scoring=\"neg_mean_absolute_error\",\n",
    "    n_iter=40,\n",
    "    cv=inner_cv,\n",
    "    n_jobs=2,\n",
    "    verbose=1,\n",
    "    # random_state=42,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 40 candidates, totalling 200 fits\n",
      "Fitting 5 folds for each of 40 candidates, totalling 200 fits\n",
      "Fitting 5 folds for each of 40 candidates, totalling 200 fits\n",
      "Fitting 5 folds for each of 40 candidates, totalling 200 fits\n",
      "Fitting 5 folds for each of 40 candidates, totalling 200 fits\n"
     ]
    }
   ],
   "source": [
    "cv_results_rand_forest = cross_validate(\n",
    "    rand_forest_search_cv,\n",
    "    feature_df,\n",
    "    y,\n",
    "    cv=outer_cv,\n",
    "    scoring=[\n",
    "        \"r2\",\n",
    "        \"neg_mean_absolute_error\",\n",
    "        \"neg_mean_absolute_percentage_error\",\n",
    "    ],\n",
    "    return_train_score=True,\n",
    "    return_estimator=True,\n",
    "    n_jobs=2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_distributions_grad_boost = {\n",
    "    \"regressor__n_estimators\": [1, 2, 5, 10, 20, 50, 100, 200, 500],\n",
    "    \"regressor__max_leaf_nodes\": [2, 5, 10, 20, 50, 100],\n",
    "    \"regressor__learning_rate\": loguniform(0.01, 1),\n",
    "}\n",
    "\n",
    "grad_boost_search_cv = RandomizedSearchCV(\n",
    "    grad_boost,\n",
    "    param_distributions=param_distributions_grad_boost,\n",
    "    scoring=\"neg_mean_absolute_error\",\n",
    "    n_iter=80,\n",
    "    cv=inner_cv,\n",
    "    n_jobs=2,\n",
    "    verbose=1,\n",
    "    # random_state=42,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 80 candidates, totalling 400 fits\n",
      "Fitting 5 folds for each of 80 candidates, totalling 400 fits\n",
      "Fitting 5 folds for each of 80 candidates, totalling 400 fits\n",
      "Fitting 5 folds for each of 80 candidates, totalling 400 fits\n",
      "Fitting 5 folds for each of 80 candidates, totalling 400 fits\n"
     ]
    }
   ],
   "source": [
    "cv_results_grad_boost = cross_validate(\n",
    "    grad_boost_search_cv,\n",
    "    feature_df,\n",
    "    y,\n",
    "    cv=outer_cv,\n",
    "    scoring=[\n",
    "        \"r2\",\n",
    "        \"neg_mean_absolute_error\",\n",
    "        \"neg_mean_absolute_percentage_error\",\n",
    "    ],\n",
    "    return_train_score=True,\n",
    "    return_estimator=True,\n",
    "    n_jobs=2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Mean Absolute Error [%]')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 6))\n",
    "x, width = np.arange(3), 0.4\n",
    "\n",
    "items = [\n",
    "    (lin_ridge_result[\"test_neg_mean_absolute_percentage_error\"], cv_results_lin_ridge[\"test_neg_mean_absolute_percentage_error\"]),\n",
    "    (rnd_for_result[\"test_neg_mean_absolute_percentage_error\"], cv_results_rand_forest[\"test_neg_mean_absolute_percentage_error\"]),\n",
    "    (grad_boost_result[\"test_neg_mean_absolute_percentage_error\"], cv_results_grad_boost[\"test_neg_mean_absolute_percentage_error\"]),\n",
    "    # hist_native_result[\"test_score\"],\n",
    "]\n",
    "\n",
    "cv_mean = [[np.mean(np.abs(item1))*100, np.mean(np.abs(item2))*100] for item1, item2 in items]\n",
    "cv_std = [[np.std(item1)*100, np.std(item2)*100] for item1, item2 in items]\n",
    "\n",
    "ax.bar(\n",
    "    x=x - width/2,\n",
    "    height=[a_mean[0] for a_mean in cv_mean],\n",
    "    width=width,\n",
    "    yerr=[a_std[0] for a_std in cv_std],\n",
    "    color=[\"C1\", \"C2\", \"C3\"],\n",
    "    label=\"Default\"\n",
    ")\n",
    "ax.bar(\n",
    "    x=x + width/2,\n",
    "    height=[a_mean[1] for a_mean in cv_mean],\n",
    "    width=width,\n",
    "    yerr=[a_std[1] for a_std in cv_std],\n",
    "    color=[\"bisque\", \"limegreen\", \"tomato\"],\n",
    "    label=\"Tuned\"\n",
    ")\n",
    "ax.set(\n",
    "    # xlabel=\"Model\",\n",
    "    # ylabel=\"Mean Absolute Error [%]\",\n",
    "    # title=\"Mean Absolute Error [%]\",\n",
    "    xticks=x,\n",
    "    xticklabels=[\"Linear Ridge\", \"Rand. Forest\", \"Grad. Boost\"],\n",
    ")\n",
    "fig.suptitle(\"Regression Models - Tuned Performance\", size=14)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "ax.set_ylabel(\"Mean Absolute Error [%]\", fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data using Cross Validator\n",
    "# _ = cv_sfold.get_n_splits(feature_df)\n",
    "data_to_train = []\n",
    "target_to_train = []\n",
    "data_to_test = []\n",
    "target_to_test = []\n",
    "for train_index, test_index in cv_sfold.split(feature_df):\n",
    "    data_to_train.append(feature_df.iloc[train_index, :])\n",
    "    data_to_test.append(feature_df.iloc[test_index, :])\n",
    "    target_to_train.append(y[train_index])\n",
    "    target_to_test.append(y[test_index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for iFold in range(4,5):\n",
    "\n",
    "    train_pi = permutation_importance(\n",
    "        rnd_for_result['estimator'][iFold],\n",
    "        data_to_train[iFold],\n",
    "        target_to_train[iFold],\n",
    "        n_repeats=10,\n",
    "        random_state=42,\n",
    "        n_jobs=2,\n",
    "        scoring=[\n",
    "            \"r2\",\n",
    "            \"neg_mean_absolute_percentage_error\",\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    test_pi = permutation_importance(\n",
    "        rnd_for_result['estimator'][iFold],\n",
    "        data_to_test[iFold],\n",
    "        target_to_test[iFold],\n",
    "        n_repeats=10,\n",
    "        random_state=42,\n",
    "        n_jobs=2,\n",
    "        scoring=[\n",
    "            \"r2\",\n",
    "            \"neg_mean_absolute_percentage_error\",\n",
    "        ]\n",
    "    )\n",
    "    sorted_importances_idx = train_pi[\"neg_mean_absolute_percentage_error\"].importances_mean.argsort()\n",
    "    train_importances = pd.DataFrame(\n",
    "        train_pi[\"neg_mean_absolute_percentage_error\"].importances[sorted_importances_idx].T*100,\n",
    "        columns=feature_df.columns[sorted_importances_idx],\n",
    "    )\n",
    "    test_importances = pd.DataFrame(\n",
    "        test_pi[\"neg_mean_absolute_percentage_error\"].importances[sorted_importances_idx].T*100,\n",
    "        columns=feature_df.columns[sorted_importances_idx],\n",
    "    )\n",
    "\n",
    "    for name, importances in zip([\"train\", \"test\"], [train_importances, test_importances]):\n",
    "        ax = importances.plot.box(vert=False, whis=10)\n",
    "        ax.set_title(f\"Permutation Importances in {name} set {iFold+1}\")\n",
    "        ax.set_xlabel(\"Decrease in Mean Absolute Error [%]\")\n",
    "        plt.xlim([-5, 45])\n",
    "        ax.axvline(x=0, color=\"k\", linestyle=\"--\")\n",
    "        ax.figure.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# refit model with less features\n",
    "red_feature_df = car_price_df[[\n",
    "    'model_key',\n",
    "    'car_type',\n",
    "    'feature_2',\n",
    "    'feature_5',\n",
    "    'feature_7',\n",
    "    'feature_8'\n",
    "] + num_var]\n",
    "\n",
    "red_cat_cols = categorical_columns_selector(red_feature_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "red_rnd_for_result = cross_validate(\n",
    "    rand_forest,\n",
    "    red_feature_df,\n",
    "    y,\n",
    "    cv=cv_sfold,\n",
    "    scoring=[\n",
    "        \"r2\",\n",
    "        \"neg_mean_absolute_percentage_error\",\n",
    "    ],\n",
    "    return_train_score=True,\n",
    "    return_estimator=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4830, 10)\n",
      "[-0.18602494 -0.30860056 -0.60053015 -0.37476161 -0.42846616]\n",
      "[-0.18451835 -0.30045181 -0.55562036 -0.36756138 -0.4153516 ]\n"
     ]
    }
   ],
   "source": [
    "print(red_feature_df.shape)\n",
    "print(red_rnd_for_result[\"test_neg_mean_absolute_percentage_error\"])\n",
    "# Compare against model containing all features\n",
    "print(rnd_for_result[\"test_neg_mean_absolute_percentage_error\"])"
   ]
  }
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