{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TopGear Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook demonstrates most of the functionality offered by the robpy package through an application to the TopGear dataset. Utility functions and univariate estimators are not demonstrated separately, as they are mostly implemented as helpers to the multivariate estimators."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from robpy.datasets import load_topgear\n",
    "from robpy.preprocessing import DataCleaner, RobustPowerTransformer, RobustScaler\n",
    "from robpy.covariance import FastMCDEstimator, OGKEstimator\n",
    "from robpy.pca import ROBPCAEstimator\n",
    "from robpy.outliers import DDCEstimator\n",
    "from robpy.regression import MMEstimator\n",
    "from robpy.univariate import adjusted_boxplot\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Robpy has a dataset module which allows the user quick access to a few common datasets used in robust statistics literature. In this demo, we will work with the TopGear dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _topgear_dataset:\n",
      "\n",
      "TopGear dataset\n",
      "--------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "- Number of Instances: 297 \n",
      "- Number of Attributes: 32 (13 numeric, 19 categorical)\n",
      "- Attribute Information:\n",
      "    * Maker (str): the car maker.\n",
      "    * Model (str): the car model.\n",
      "    * Type (str): the exact model type.\n",
      "    * Fuel (str): the type of fuel (\"Diesel\" or \"Petrol\").\n",
      "    * Price (float): the list price (in UK pounds)\n",
      "    * Cylinders (float): the number of cylinders in the engine.\n",
      "    * Displacement (float): the displacement of the engine (in cc).\n",
      "    * DriveWheel (str): the type of drive wheel (\"4WD\", \"Front\" or \"Rear\").\n",
      "    * BHP (float): the power of the engine (in bhp).\n",
      "    * Torque (float): the torque of the engine (in lb/ft).\n",
      "    * Acceleration (float): the time it takes the car to get from 0 to 62 mph (in seconds).\n",
      "    * TopSpeed (float): the car's top speed (in mph).\n",
      "    * MPG (float): the combined fuel consuption (urban + extra urban; in miles per gallon).\n",
      "    * Weight (float): the car's curb weight (in kg).\n",
      "    * Length (float): the car's length (in mm).\n",
      "    * Width (float): the car's width (in mm).\n",
      "    * Height (float): the car's height (in mm).\n",
      "    * AdaptiveHeadlights (str): whether the car has adaptive headlights (\"no\", \"optional\" or \"standard\").\n",
      "    * AdjustableSteering (str): whether the car has adjustable steering (\"no\" or \"standard\").\n",
      "    * AlarmSystem (str) whether the car has an alarm system (\"no/optional\" or \"standard\").\n",
      "    * Automatic (str) whether the car has an automatic transmission (\"no\", \"optional\" or \"standard\").\n",
      "    * Bluetooth (str) whether the car has bluetooth (\"no\", \"optional\" or \"standard\").\n",
      "    * ClimateControl (str) whether the car has climate control (\"no\", \"optional\" or \"standard\").\n",
      "    * CruiseControl (str) whether the car has cruise control (\"no\", \"optional\" or \"standard\").\n",
      "    * ElectricSeats (str) whether the car has electric seats (\"no\", \"optional\" or \"standard\").\n",
      "    * Leather (str) whether the car has a leather interior (\"no\", \"optional\" or \"standard\").\n",
      "    * ParkingSensors (str) whether the car has parking sensors (\"no\", \"optional\" or \"standard\").\n",
      "    * PowerSteering (str) whether the car has power steering (\"no\" or \"standard\").\n",
      "    * SatNav (str) whether the car has a satellite navigation system (\"no\", \"optional\" or \"standard\").\n",
      "    * ESP (str) whether the car has ESP (\"no\", \"optional\" or \"standard\").\n",
      "    * Verdict (float) review score between 1 (lowest) and 10 (highest).\n",
      "    * Origin (str) the origin of the car maker (\"Asia\", \"Europe\" or \"USA\").\n",
      "\n",
      "- Creator: BBC TopGear\n",
      "- Source: https://rdrr.io/cran/robustHD/man/TopGear.html\n",
      "\n",
      "**Data Set Description:**\n",
      "The data set contains information on cars featured on the website of the popular BBC television show Top Gear.\n",
      "The data were scraped from http://www.topgear.com/uk/ on 2014-02-24. Variable Origin was added based on the car maker information.\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data = load_topgear(as_frame=True)\n",
    "\n",
    "print(data.DESCR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `DESCR` attribute of the data object contains all metadata and a description of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "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</th>\n",
       "      <th>Model</th>\n",
       "      <th>Type</th>\n",
       "      <th>Fuel</th>\n",
       "      <th>Price</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>DriveWheel</th>\n",
       "      <th>BHP</th>\n",
       "      <th>Torque</th>\n",
       "      <th>...</th>\n",
       "      <th>ClimateControl</th>\n",
       "      <th>CruiseControl</th>\n",
       "      <th>ElectricSeats</th>\n",
       "      <th>Leather</th>\n",
       "      <th>ParkingSensors</th>\n",
       "      <th>PowerSteering</th>\n",
       "      <th>SatNav</th>\n",
       "      <th>ESP</th>\n",
       "      <th>Verdict</th>\n",
       "      <th>Origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alfa Romeo</td>\n",
       "      <td>Giulietta</td>\n",
       "      <td>Giulietta 1.6 JTDM-2 105 Veloce 5d</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>21250.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1598.0</td>\n",
       "      <td>Front</td>\n",
       "      <td>105.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>...</td>\n",
       "      <td>standard</td>\n",
       "      <td>standard</td>\n",
       "      <td>optional</td>\n",
       "      <td>optional</td>\n",
       "      <td>optional</td>\n",
       "      <td>standard</td>\n",
       "      <td>optional</td>\n",
       "      <td>standard</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alfa Romeo</td>\n",
       "      <td>MiTo</td>\n",
       "      <td>MiTo 1.4 TB MultiAir 105 Distinctive 3d</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>15155.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1368.0</td>\n",
       "      <td>Front</td>\n",
       "      <td>105.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>...</td>\n",
       "      <td>optional</td>\n",
       "      <td>standard</td>\n",
       "      <td>no</td>\n",
       "      <td>optional</td>\n",
       "      <td>standard</td>\n",
       "      <td>standard</td>\n",
       "      <td>optional</td>\n",
       "      <td>standard</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aston Martin</td>\n",
       "      <td>Cygnet</td>\n",
       "      <td>Cygnet 1.33 Standard 3d</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>30995.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1329.0</td>\n",
       "      <td>Front</td>\n",
       "      <td>98.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>...</td>\n",
       "      <td>standard</td>\n",
       "      <td>standard</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>standard</td>\n",
       "      <td>standard</td>\n",
       "      <td>standard</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aston Martin</td>\n",
       "      <td>DB9</td>\n",
       "      <td>DB9 6.0 517 Standard 2d 13MY</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>131995.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5935.0</td>\n",
       "      <td>Rear</td>\n",
       "      <td>517.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>...</td>\n",
       "      <td>standard</td>\n",
       "      <td>standard</td>\n",
       "      <td>standard</td>\n",
       "      <td>standard</td>\n",
       "      <td>standard</td>\n",
       "      <td>standard</td>\n",
       "      <td>standard</td>\n",
       "      <td>standard</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aston Martin</td>\n",
       "      <td>DB9 Volante</td>\n",
       "      <td>DB9 6.0 V12 517 Volante 2d 13MY</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>141995.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5935.0</td>\n",
       "      <td>Rear</td>\n",
       "      <td>517.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>...</td>\n",
       "      <td>standard</td>\n",
       "      <td>standard</td>\n",
       "      <td>standard</td>\n",
       "      <td>standard</td>\n",
       "      <td>standard</td>\n",
       "      <td>standard</td>\n",
       "      <td>standard</td>\n",
       "      <td>standard</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Maker        Model                                     Type    Fuel  \\\n",
       "0    Alfa Romeo    Giulietta       Giulietta 1.6 JTDM-2 105 Veloce 5d  Diesel   \n",
       "1    Alfa Romeo         MiTo  MiTo 1.4 TB MultiAir 105 Distinctive 3d  Petrol   \n",
       "2  Aston Martin       Cygnet                  Cygnet 1.33 Standard 3d  Petrol   \n",
       "3  Aston Martin          DB9             DB9 6.0 517 Standard 2d 13MY  Petrol   \n",
       "4  Aston Martin  DB9 Volante          DB9 6.0 V12 517 Volante 2d 13MY  Petrol   \n",
       "\n",
       "      Price  Cylinders  Displacement DriveWheel    BHP  Torque  ...  \\\n",
       "0   21250.0        4.0        1598.0      Front  105.0   236.0  ...   \n",
       "1   15155.0        4.0        1368.0      Front  105.0    95.0  ...   \n",
       "2   30995.0        4.0        1329.0      Front   98.0    92.0  ...   \n",
       "3  131995.0       12.0        5935.0       Rear  517.0   457.0  ...   \n",
       "4  141995.0       12.0        5935.0       Rear  517.0   457.0  ...   \n",
       "\n",
       "   ClimateControl  CruiseControl  ElectricSeats   Leather  ParkingSensors  \\\n",
       "0        standard       standard       optional  optional        optional   \n",
       "1        optional       standard             no  optional        standard   \n",
       "2        standard       standard             no        no              no   \n",
       "3        standard       standard       standard  standard        standard   \n",
       "4        standard       standard       standard  standard        standard   \n",
       "\n",
       "   PowerSteering    SatNav       ESP Verdict  Origin  \n",
       "0       standard  optional  standard     6.0  Europe  \n",
       "1       standard  optional  standard     5.0  Europe  \n",
       "2       standard  standard  standard     7.0  Europe  \n",
       "3       standard  standard  standard     7.0  Europe  \n",
       "4       standard  standard  standard     7.0  Europe  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use `DataCleaner` to remove non-numeric columns, as well as columns and rows with too many missing values, discrete columns, columns with a scale of zero and columns corresponding to the case numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaner = DataCleaner().fit(data.data)\n",
    "clean_data = cleaner.transform(data.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can inspect the dropped columns by type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"non_numeric_cols\": [\n",
      "        \"Maker\",\n",
      "        \"Model\",\n",
      "        \"Type\",\n",
      "        \"Fuel\",\n",
      "        \"DriveWheel\",\n",
      "        \"AdaptiveHeadlights\",\n",
      "        \"AdjustableSteering\",\n",
      "        \"AlarmSystem\",\n",
      "        \"Automatic\",\n",
      "        \"Bluetooth\",\n",
      "        \"ClimateControl\",\n",
      "        \"CruiseControl\",\n",
      "        \"ElectricSeats\",\n",
      "        \"Leather\",\n",
      "        \"ParkingSensors\",\n",
      "        \"PowerSteering\",\n",
      "        \"SatNav\",\n",
      "        \"ESP\",\n",
      "        \"Origin\"\n",
      "    ],\n",
      "    \"cols_rownumbers\": [],\n",
      "    \"cols_discrete\": [\n",
      "        \"Fuel\",\n",
      "        \"DriveWheel\",\n",
      "        \"AdaptiveHeadlights\",\n",
      "        \"AdjustableSteering\",\n",
      "        \"AlarmSystem\",\n",
      "        \"Automatic\",\n",
      "        \"Bluetooth\",\n",
      "        \"ClimateControl\",\n",
      "        \"CruiseControl\",\n",
      "        \"ElectricSeats\",\n",
      "        \"Leather\",\n",
      "        \"ParkingSensors\",\n",
      "        \"PowerSteering\",\n",
      "        \"SatNav\",\n",
      "        \"ESP\",\n",
      "        \"Origin\"\n",
      "    ],\n",
      "    \"cols_bad_scale\": [\n",
      "        \"Cylinders\"\n",
      "    ],\n",
      "    \"cols_missings\": []\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "print(json.dumps(cleaner.dropped_columns, indent=4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As well as the dropped rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rows_missings': [69, 95]}"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaner.dropped_rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also exclude the variable `Verdict` from the features for further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_data = clean_data.drop(columns=['Verdict'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transforming"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `Price` variable has a long tail as can be seen from its adjusted boxplot and its histogram. It's a good idea to apply a power transformation to it to make it more symmetric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Boxplot(median=26505.0, q1=18902.5, q3=44252.5, upper_whisker=191797.28737567586, lower_whisker=12666.161330745548)]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAANcAAADUCAYAAAAP6bYbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAZb0lEQVR4nO3de1BTZ/oH8G8MJNytrnKJIhdBtJWLoIBaqlRGsGDNUhy02mLX2zpa64haaEfZXeuiLbqMrgOuq6jjKrsVil0teGFAcYm6Rq2lWAQKFhUq0uWuWJL394dDfh4TMAkcAsnzmTkjOefN4Y3m6znnTc77CBhjDISQPjfE0B0gxFhRuAjhCYWLEJ5QuAjhCYWLEJ5QuAjhCYWLEJ5QuAjhCYWLEJ5QuAjhidGF6+LFi5g7dy4kEgkEAgFycnJ03gdjDCkpKRg3bhzEYjFGjRqFbdu29X1niVEzM3QH+lpbWxt8fX3xu9/9DtHR0Xrt46OPPsLZs2eRkpICb29v/PLLL/jll1/6uKfE6DEjBoB99dVXnHVPnjxh8fHxTCKRMCsrKxYYGMgKCgpU20tLS5mZmRn74Ycf+rezxOgY3Wnhy6xZswYymQyZmZm4desW5s+fj4iICJSXlwMA/v3vf8Pd3R2nTp2Cm5sbXF1dsWzZMjpyEd0ZOt18wgtHrrt37zKhUMju37/PaTdr1iyWmJjIGGNs5cqVTCwWs6CgIHbx4kVWUFDA/Pz8WGhoaH92nRgBo7vm6sl3330HhUKBcePGcdZ3dHTgN7/5DQBAqVSio6MDR44cUbU7cOAAAgICUFZWBi8vr37vNxmcTCpcra2tEAqFkMvlEAqFnG02NjYAACcnJ5iZmXECOGHCBADATz/9ROEiWjOpcE2aNAkKhQIPHz5ESEiIxjbTp09HZ2cnKisrMXbsWADAnTt3AAAuLi791lcy+AkYM67b/FtbW1FRUQHgWZh27dqF0NBQDB8+HGPGjMHixYvxn//8Bzt37sSkSZNQX1+P/Px8+Pj4IDIyEkqlElOmTIGNjQ1SU1OhVCqxevVq2NnZ4ezZswZ+dWRQMfRFX18rKChgANSWuLg4xhhjT58+ZVu2bGGurq7M3NycOTk5sd/+9rfs1q1bqn3cv3+fRUdHMxsbG+bg4MCWLFnCGhoaDPSKyGBldEcuQgYKk/uci5D+YhQDGkqlEg8ePICtrS0EAoGhu0OMGGMMLS0tkEgkGDKk52OTUYTrwYMHcHZ2NnQ3iAmpqanB6NGje2xjFOGytbUF8OwF29nZGbg3xJg1NzfD2dlZ9Z7riVGEq+tU0M7OjsKlJYVCgaKiItTW1sLJyQkhISFqH6yT7mlz+UEDGiYoOzsbHh4eCA0NxbvvvovQ0FB4eHggOzvb0F0zKhQuE5OdnY2YmBh4e3tDJpOhpaUFMpkM3t7eiImJoYD1IaP4nKu5uRlDhw5FU1MTnRb2QKFQwMPDA97e3sjJyeGMdimVSkilUpSUlKC8vJxOEbuhy3uNjlwmpKioCNXV1fjkk0/UhpGHDBmCxMREVFVVoaioyEA9NC4ULhNSW1sLAJg4caLG7V3ru9qR3qFwmRAnJycAQElJicbtXeu72pHeoXCZkJCQELi6uuLPf/4zlEolZ5tSqURycjLc3Ny6vR2H6IbCZUKEQiF27tyJU6dOQSqVckYLpVIpTp06hZSUFBrM6CNG8SEy0V50dDROnDiB+Ph4TJs2TbXezc0NJ06c0Hs6OqKOhuJNFH1DQz+6vNfoyGWihEIhZs6caehuGDW65iKEJxQuQnhC4SKEJxQuQnhC4SKEJxQuQnhC4SKEJzqHS5/KjYWFhfD394dYLIaHhwcOHTqk1mbv3r1wdXWFhYUFgoKCcPXqVV27RnSgUChQWFiI48ePo7CwEAqFwtBdMjo6h6urcuPevXu1al9VVYXIyEiEhobi5s2bWLduHZYtW4YzZ86o2vzzn//E+vXrkZSUhOvXr8PX1xfh4eF4+PChrt0jWqDb/PtJb6brhYbKjS/atGkTe+211zjrYmNjWXh4uOpxYGAgW716teqxQqFgEomEJScna9WPpqYmBoA1NTVp33kTlZWVxQQCAZs7dy6TyWSspaWFyWQyNnfuXCYQCFhWVpahuzig6fJe4/2aSyaTISwsjLMuPDwcMpkMAPD06VPI5XJOmyFDhiAsLEzV5kUdHR1obm7mLOTlFAoF4uPjERUVhZycHAQHB8PGxgbBwcHIyclBVFQUNmzYQKeIfYT3cNXV1cHBwYGzzsHBAc3NzXj8+DEePXoEhUKhsU1dXZ3GfSYnJ2Po0KGqhSYE1Q7d5t+/BuVoYWJiIpqamlRLTU2Nobs0KNBt/v2L92/FOzo64ueff+as+/nnn2FnZwdLS0sIhUIIhUKNbRwdHTXuUywWQywW89ZnY/X8bf7BwcFq2+k2/77F+5Fr6tSpyM/P56w7d+4cpk6dCgAQiUQICAjgtFEqlcjPz1e1IX2DbvPvZ7qOlrS0tLAbN26wGzduMABs165d7MaNG+zu3buMMcYSEhLYe++9p2r/448/MisrK7Zx40Z2+/ZttnfvXiYUClleXp6qTWZmJhOLxezQoUOstLSUrVixgr3yyiusrq5Oqz7RaKH2nh8tLC4uZs3Nzay4uJhGC7Wky3tN53C9rHJjXFwcmzFjhtpz/Pz8mEgkYu7u7iwjI0Ntv3v27GFjxoxhIpGIBQYGssuXL2vdJwqXbrKyspirqyvn38/NzY2CpQVd3mt0m7+Jotv89UO3+ZOXotv8+Tcoh+IJGQwoXITwhMJFCE8oXITwhMJFCE8oXITwhMJFCE8oXITwhMJFCE8oXITwhMJFCE8oXITwhMJFCE8oXITwhMJFCE8oXITwhMJFCE8oXITwhMJFCE/0Cpcu5X5mzpwJgUCgtkRGRqraLFmyRG17RESEPl0jZMDQeYKarnI/6enpCAoKQmpqKsLDw1FWVgZ7e3u19tnZ2Xj69KnqcUNDA3x9fTF//nxOu4iICGRkZKge04y6ZLDTOVy7du3C8uXL8cEHHwAA0tPTcfr0aRw8eBAJCQlq7YcPH855nJmZCSsrK7VwicXibqevflFHRwc6OjpUj6nKCRmIdDot1Kfcz4sOHDiABQsWwNramrO+sLAQ9vb28PLywqpVq9DQ0NDtPqjKCRkMdAqXPuV+nnf16lWUlJRg2bJlnPURERE4cuQI8vPzsWPHDly4cAFz5szptk4UVTkhg0G/Tgp64MABeHt7IzAwkLN+wYIFqp+9vb3h4+ODsWPHorCwELNmzVLbD1U5IYOBTkeuESNG6Fzup0tbWxsyMzOxdOnSl/4ed3d3jBgxAhUVFbp0j5ABRadw9abcz5dffomOjg4sXrz4pb/n3r17aGhooDpRZHDTtcrDy8r9vPfeeywhIUHtea+//jqLjY1VW9/S0sI2bNjAZDIZq6qqYufPn2f+/v7M09OTPXnyRKs+UZUT0l90ea/pfM0VGxuL+vp6bNmyBXV1dfDz80NeXp5qkOOnn35Sq7dbVlaGS5cu4ezZs2r7EwqFuHXrFg4fPozGxkZIJBLMnj0bW7dupesqMqhRCSFCdKDLe42+W0gITyhchPCEwkUITyhchPCEwkUITyhchPCEwkUITyhchPCEwkUITyhchPCEwkUITyhchPCEwkUITyhchPCEwkUITyhchPCEwkUITyhchPCEwkUITyhchPCE9xJChw4dUisPZGFhwWnDGMOWLVvg5OQES0tLhIWFoby8XJ+uETJg6ByurhJCSUlJuH79Onx9fREeHo6HDx92+xw7OzvU1taqlrt373K2f/7559i9ezfS09Nx5coVWFtbIzw8HE+ePNH9FREyUOg6KWJgYCBbvXq16rFCoWASiYQlJydrbJ+RkcGGDh3a7f6USiVzdHRkX3zxhWpdY2MjE4vF7Pjx4xqf8+TJE9bU1KRaampqaFJQ0i90mRS0X0oItba2wsXFBc7Ozpg3bx6+//571baqqirU1dVx9jl06FAEBQV1u08qIUQGA95LCHl5eeHgwYM4efIkjh49CqVSiWnTpuHevXsAoHqeLvukEkJkMOC9hNDUqVM5RRqmTZuGCRMmYN++fdi6date+6QSQmQw6LcSQl3Mzc0xadIkVXmgruf1Zp+EDET9VkKoi0KhwHfffacqD+Tm5gZHR0fOPpubm3HlyhWt90nIgKTraImuJYT++Mc/sjNnzrDKykoml8vZggULmIWFBfv+++9VbbZv385eeeUVdvLkSXbr1i02b9485ubmxh4/fqxVn6iEEOkvA6qE0P/+9z8sX74cdXV1GDZsGAICAlBcXIxXX31V1WbTpk1oa2vDihUr0NjYiNdffx15eXlqHzYTMphQCSFCdKDLe61fC46TgUOhUKCoqAi1tbVwcnJCSEgIhEKhobtlVOiLuyYoOzsbHh4eCA0NxbvvvovQ0FB4eHggOzvb0F0zKhQuE5OdnY2YmBh4e3tDJpOhpaUFMpkM3t7eiImJoYD1IbrmMiEKhQIeHh7w9vZGTk4OZ+BJqVRCKpWipKQE5eXldIrYDSrbSjQqKipCdXU1PvnkE7Wi8EOGDEFiYiKqqqpQVFRkoB4aFwqXCamtrQUATJw4UeP2rvVd7UjvULhMSNe3YkpKSqBQKFBYWIjjx4+jsLAQCoUCJSUlnHakd+iay4R0XXONGDECjx49QnV1tWqbq6srRowYgYaGBrrm6gFdcxGNhEIh5s+fj2vXrqG9vR3x8fHYu3cv4uPj0d7ejmvXriEmJoaC1UfoyGVCuo5cQqEQ1dXVUCgUqm1mZmZwcXGBUqmkI1cP6BsaRKOu0UKBQIDIyEjMmTMHlpaWePz4MXJzc3H69GkwxlBUVISZM2cauruDHoXLhNy/fx8AEBERgZMnT3KG43//+98jKioKubm5qnakd+iay4TU19cDAKKjozV+ziWVSjntSO9QuEzIyJEjATz7CpRSqeRsUyqVyMnJ4bQjvUPhMiGjRo0CAOTm5kIqlXK+WyiVSpGbm8tpR3qHRgtNyPOfc9XX13MmZ6XPubRDo4VEI6FQiJ07dyImJgZvvfUWpFIpHj9+DEtLS1RUVOCbb77BiRMnKFh9ha+5BvoTzaGhm40bNzIzMzMGQLWYmZmxjRs3GrprAx6vc2iQwS07OxspKSkaP+dKSUlBcHAwoqOjDd1No0DXXCaE7ufqPd6/W6hLCaH9+/cjJCQEw4YNw7BhwxAWFqbWfsmSJWplhiIiIvTpGunB8/dzMcY434pnjNH9XH2M9xJChYWFWLhwIQoKCiCTyeDs7IzZs2erfQsgIiKCU2bo+PHj+r0i0q2u+7QqKyvh7u7OmUPD3d0dP/74I6cd6SVdL+h0LSH0os7OTmZra8sOHz6sWhcXF8fmzZundR+ohJB+CgoKOIMY3S0FBQWG7uqANeBKCD2vvb0dv/76K4YPH85ZX1hYCHt7e3h5eWHVqlVoaGjodh9UQkg/06ZNU/0sEomQkJCAiooKJCQkQCQSaWxH9Md7CaEXffzxx5BIJJyARkRE4MiRI8jPz8eOHTtw4cIFzJkzh3NLxPOohJB+np+PPywsDKNHj8aFCxcwevRozr/H8+1IL+hySLx//z4DwIqLiznrN27cyAIDA1/6/OTkZDZs2DD27bff9tiusrKSAWDnz5/Xql/0OZd2wsLCGAA2efJkjZ9zBQQEMAAsLCzM0F0dsHg7LexNCaGUlBRs374dZ8+ehY+PT49t3d3dMWLECFWZIdI3GhsbAQDXrl2Dubk5Z5u5uTnkcjmnHemdfikh9Pnnn2Pr1q3Iy8vD5MmTX/p77t27h4aGBpoopY8FBASofn7zzTc5X9x98803NbYj+tN5KH79+vXYv38/Dh8+jNu3b2PVqlVoa2vDBx98AAB4//33kZiYqGq/Y8cObN68GQcPHoSrqyvq6upQV1eH1tZWAM/qJW/cuBGXL19GdXU18vPzMW/ePHh4eCA8PLyPXiYBgLffflv1M2NMbdHUjvSCPuede/bsYWPGjGEikYgFBgayy5cvq7bNmDGDxcXFqR67uLhoHO5NSkpijDHW3t7OZs+ezUaOHMnMzc2Zi4sLW758uarelzbomqtnbW1tTC6Xs6VLl2o1FL906VIml8tZW1ubobs+4OjyXqOvP5mA69ev63WqJ5fL4e/vz0OPBi+aWo1wjB8/HnK5HGlpaQAAPz8/XLx4UTX8HhYWhosXL8LPzw8AkJaWBrlcjvHjxxuqy8aB9+NoP6DTQu10dnaykSNHMgAsKiqKZWRkMAAsIyODRUVFMQDM3t6edXZ2GrqrAxadFpJuZWdn45133lHdatLFysoK7e3tyMrKoltOekCnhaRb0dHRyMrKgr29PWe9vb09BauPUbhMUHR0NCorK7Fv3z4AwL59+1BRUUHB6mMULhMlFApVH+hPnjyZbo7kAYWLEJ5QuAjhCYWLEJ5QuAjhCYWLEJ5QuAjhCYWLEJ5QuAjhCYWLEJ5QuAjhCYWLEJ5QlRMjU15ejpaWlpe2y8vLw6effgrg2YQ027Zt02p+fltbW3h6eva6n6aA7ucyIuXl5Rg3bhzvv+fOnTsmGzCqLGmiWlpa4GgjwIHUbXBzc9PYZtGiRZzHEokEDx484Kz7xz/+ofG5VVVVWLruU62OjETPcO3duxdffPEF6urq4Ovriz179iAwMLDb9l9++SU2b96M6upqeHp6YseOHXjrrbdU2xljSEpKwv79+9HY2Ijp06cjLS3NZP931Fd7eztWBojwVs12oJsZvq+vtHlhTTOAF9ZdXKnxuRMArAwQadxG1Okcrq4SQunp6QgKCkJqairCw8NRVlamdncrABQXF2PhwoVITk5GVFQUjh07BqlUiuvXr2PixIkAnk0aunv3bhw+fBhubm7YvHkzwsPDUVpaCgsLi96/ShPxww8/YJ/8Kb4u+5W331HbyrDI1pa3/RsTna+5goKCMGXKFPz1r38F8GzGXWdnZ3z44YdISEhQax8bG4u2tjacOnVKtS44OBh+fn5IT08HYwwSiQTx8fHYsGEDAKCpqQkODg44dOgQFixYoLbPjo4OdHR0qB43NzfD2dnZ5K+5Hj16hJycHIwfPx5WVlaq9bdv38bixYt13t/Ro0cxYcIEzjpTH9DQ6fpel5lvOjo6mFAoZF999RVn/fvvv8/efvttjc9xdnZmf/nLXzjrtmzZwnx8fBhj/1904caNG5w2b7zxBlu7dq3GfSYlJWmczJJmf9Ksa1LQ5/+u5HI5u3TpEjt69Ci7dOmSxu00Kag63gox6FNCqK6ursf2XX/qsk8qIaQbKysr+Pv749ixY6p17e3tmD59OhYtWoTp06ejvb1dte3YsWPw9/fnHP2I7gblh8hisRh2dnachbzcwoULVT+HhIRAIBDA19cXAoEAISEhGtsR/fFeQsjR0bHH9l1/6lOWiOiOvXCJfevWrR63E/3xXkJo6tSpapUKz507p2rv5uYGR0dHTpvm5mZcuXKlx7JERH+MMc4pIvDsVJCC1bd0Hopfv3494uLiMHnyZAQGBiI1NVWthNCoUaOQnJwMAPjoo48wY8YM7Ny5E5GRkcjMzMS1a9fwt7/9DQAgEAiwbt06fPbZZ/D09FQNxUskEkilUq361PWmaG5u1vXlmKzIyEg0NTVx1tHf38t1/R1p9R+RPiMmupQQYoyxf/3rX2zcuHFMJBKx1157jZ0+fZqzXalUss2bNzMHBwcmFovZrFmzWFlZmdb9qamp0ao0Di209NVSU1Pz0velUXy3UKlU4sGDB7C1tYVAIDB0dwaNrs8Ha2pqaFBIS4wxtLS0QCKRYMiQnq+qjCJcRD/0hWd+DcqheEIGAwoXITyhcJkwsViMpKQkiMViQ3fFKNE1FyE8oSMXITyhcBHCEwoXITyhcBHCEwqXCXJ1dUVqaqqhu2H0KFyD3JIlSyAQCCAQCCASieDh4YE//elP6Ozs7PY5//3vf7FixYp+7KVpoqnVjEBERAQyMjLQ0dGBb775BqtXr4a5uTkSExM57Z4+fQqRSISRI0caqKemhY5cRkAsFsPR0REuLi5YtWoVwsLC8PXXX2PJkiWQSqXYtm0bJBIJvLy8AKifFjY2NmLlypVwcHCAhYUFJk6cyJlQ6NKlSwgJCYGlpSWcnZ2xdu1atLW19ffLHHToyGWELC0t0dDQAADIz8+HnZ0dzp07p7GtUqnEnDlz0NLSgqNHj2Ls2LEoLS2FUCgEAFRWViIiIgKfffYZDh48iPr6eqxZswZr1qxBRkZGv72mwYjCZUQYY8jPz8eZM2fw4Ycfor6+HtbW1vj73/8OkUjzZJ7nz5/H1atXcfv2bdVU2O7u7qrtycnJWLRoEdatWwcA8PT0xO7duzFjxgykpaXRvJI9oNNCI3Dq1CnY2NjAwsICc+bMQWxsLP7whz8AALy9vbsNFgDcvHkTo0eP7naO+W+//RaHDh2CjY2NagkPD4dSqURVVRUfL8do0JHLCISGhiItLQ0ikQgSiQRmZv//z2ptbd3jcy0tLXvc3traipUrV2Lt2rVq28aMGaNfh00EhcsIWFtbw8PDQ6/n+vj44N69e7hz547Go5e/vz9KS0v13r8po9NCEzdjxgy88cYbeOedd3Du3DlUVVUhNzcXeXl5AICPP/4YxcXFWLNmDW7evIny8nKcPHkSa9asMXDPBz4KF0FWVhamTJmChQsX4tVXX8WmTZugUCgAPDuyXbhwAXfu3EFISAgmTZqELVu2QCKRGLjXAx/dz0UIT+jIRQhPKFyE8ITCRQhPKFyE8ITCRQhPKFyE8ITCRQhPKFyE8ITCRQhPKFyE8ITCRQhP/g/JPO5ZJaY61gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adjusted_boxplot(clean_data['Price'],figsize=(2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clean_data['Price'].hist(bins=20, figsize=(4,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "price_transformer = RobustPowerTransformer(method='auto').fit(clean_data['Price'])\n",
    "\n",
    "clean_data['Price_transformed'] = price_transformer.transform(clean_data['Price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can inspect which method was selected as well as which lambda value was applied for the transformation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('boxcox', -0.42354039562300644)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_transformer.method, price_transformer.lambda_rew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Boxplot(median=-0.03153400648124502, q1=-0.5679781382767539, q3=0.6485562387582648, upper_whisker=2.7449937256642296, lower_whisker=-2.0160158561602897)]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adjusted_boxplot(clean_data['Price_transformed'],figsize=(2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clean_data['Price_transformed'].hist(bins=20, figsize=(4,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's best to also transform `displacement`, `HP`, `Torque` and `Topspeed` as these variables are also skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2, figsize=(15, 8))\n",
    "for col, ax in zip(['Displacement', 'BHP', 'Torque', 'TopSpeed'], axs.flatten()):\n",
    "    clean_data[col].hist(ax=ax, bins=20, alpha=0.3)\n",
    "    transformer = RobustPowerTransformer(method='auto').fit(clean_data[col].dropna())\n",
    "    clean_data.loc[~np.isnan(clean_data[col]), col] = transformer.transform(clean_data[col].dropna())\n",
    "    ax2=ax.twiny()\n",
    "    clean_data[col].hist(ax=ax2, bins=20, label='transformed', color='orange', alpha=0.3)\n",
    "    ax.grid(False)\n",
    "    ax2.grid(False)\n",
    "    ax2.legend(loc='upper right')\n",
    "    ax.set_title(f'{col}: method = {transformer.method}, lambda = {transformer.lambda_rew:.3f}')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping NA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some methods require the data to be entirely NA free."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_data2 = clean_data.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Covariance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we study the covariance of the dataset by calculating a robust covariance matrix on the numeric features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MCD: Minimum Covariance Determinant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "mcd = FastMCDEstimator().fit(clean_data2.drop(columns=['Price']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = mcd.distance_distance_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OGK: Orthogonalized Gnanadesikan-Kettenring covariance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can do the same analysis with other robust covariance estimators, e.g. the OGK covariance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "ogk = OGKEstimator().fit(clean_data2.drop(columns=['Price']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = ogk.distance_distance_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the point that seems to lie very far from the majority of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "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</th>\n",
       "      <th>Model</th>\n",
       "      <th>Torque</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>TopSpeed</th>\n",
       "      <th>MPG</th>\n",
       "      <th>BHP</th>\n",
       "      <th>Length</th>\n",
       "      <th>Width</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Price</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>BMW</td>\n",
       "      <td>i3</td>\n",
       "      <td>184.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>3999.0</td>\n",
       "      <td>1775.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>33830.0</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>1315.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Maker Model  Torque  Displacement  TopSpeed    MPG    BHP  Length   Width  \\\n",
       "41   BMW    i3   184.0         647.0      93.0  470.0  170.0  3999.0  1775.0   \n",
       "\n",
       "    Acceleration    Price  Height  Weight  \n",
       "41           7.9  33830.0  1578.0  1315.0  "
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.data.loc[\n",
    "    clean_data2.index[(ogk._robust_distances > 80) & (ogk._mahalanobis_distances > 12)], \n",
    "    ['Maker', 'Model'] + list(set(clean_data2.columns).intersection(set(data.data.columns)))\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CellMCD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another robust covariance estimator that can handle missing values is the CellMCD. For more details, we refer to the separate notebook on this topic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we apply a well-known robust PCA method to the data, specifically ROBPCA. As the variables of interest have different measurement units and different scales, we first scale the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_data = RobustScaler(with_centering=False).fit_transform(clean_data2.drop(columns=['Price']))\n",
    "pca = ROBPCAEstimator().fit(scaled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "score_distances, orthogonal_distances, score_cutoff, od_cutoff = pca.plot_outlier_map(scaled_data, return_distances=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can inspect the bad leverage points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "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</th>\n",
       "      <th>Model</th>\n",
       "      <th>Torque</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>TopSpeed</th>\n",
       "      <th>MPG</th>\n",
       "      <th>BHP</th>\n",
       "      <th>Length</th>\n",
       "      <th>Width</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Price</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>BMW</td>\n",
       "      <td>i3</td>\n",
       "      <td>184.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>3999.0</td>\n",
       "      <td>1775.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>33830.0</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>1315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Bugatti</td>\n",
       "      <td>Veyron</td>\n",
       "      <td>922.0</td>\n",
       "      <td>7993.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>987.0</td>\n",
       "      <td>4462.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1139985.0</td>\n",
       "      <td>1204.0</td>\n",
       "      <td>1990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Jeep</td>\n",
       "      <td>Wrangler</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2777.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>4223.0</td>\n",
       "      <td>1873.0</td>\n",
       "      <td>12.9</td>\n",
       "      <td>25995.0</td>\n",
       "      <td>1840.0</td>\n",
       "      <td>2075.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>Land Rover</td>\n",
       "      <td>Defender</td>\n",
       "      <td>265.0</td>\n",
       "      <td>2198.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>4785.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>14.7</td>\n",
       "      <td>28195.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>2120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>G-Class</td>\n",
       "      <td>398.0</td>\n",
       "      <td>2987.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>4662.0</td>\n",
       "      <td>1760.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>82945.0</td>\n",
       "      <td>1951.0</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Pagani</td>\n",
       "      <td>Huayra</td>\n",
       "      <td>811.0</td>\n",
       "      <td>5980.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>730.0</td>\n",
       "      <td>4605.0</td>\n",
       "      <td>2036.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>990000.0</td>\n",
       "      <td>1169.0</td>\n",
       "      <td>1350.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Maker     Model  Torque  Displacement  TopSpeed    MPG    BHP  \\\n",
       "41             BMW        i3   184.0         647.0      93.0  470.0  170.0   \n",
       "49         Bugatti    Veyron   922.0        7993.0     252.0   10.0  987.0   \n",
       "124           Jeep  Wrangler   339.0        2777.0     107.0   34.0  197.0   \n",
       "135     Land Rover  Defender   265.0        2198.0      90.0   25.0  122.0   \n",
       "164  Mercedes-Benz   G-Class   398.0        2987.0     108.0   25.0  211.0   \n",
       "196         Pagani    Huayra   811.0        5980.0     230.0   23.0  730.0   \n",
       "\n",
       "     Length   Width  Acceleration      Price  Height  Weight  \n",
       "41   3999.0  1775.0           7.9    33830.0  1578.0  1315.0  \n",
       "49   4462.0  1998.0           2.5  1139985.0  1204.0  1990.0  \n",
       "124  4223.0  1873.0          12.9    25995.0  1840.0  2075.0  \n",
       "135  4785.0  1790.0          14.7    28195.0  1790.0  2120.0  \n",
       "164  4662.0  1760.0           9.1    82945.0  1951.0  2500.0  \n",
       "196  4605.0  2036.0           3.3   990000.0  1169.0  1350.0  "
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.data.loc[\n",
    "    clean_data2.loc[(score_distances > score_cutoff) & (orthogonal_distances > od_cutoff)].index, \n",
    "    ['Maker', 'Model'] + list(set(clean_data2.columns).intersection(set(data.data.columns)))\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outlier detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also detect cellwise outliers with the DDC estimator. Here we can use the data with NAs, as DDC can handle missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "ddc = DDCEstimator().fit(clean_data.drop(columns=['Price']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x3000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ddc.cellmap(clean_data.drop(columns=['Price']), figsize=(15,30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "row_indices = np.array([ 11,  41,  55,  73,  81,  94,  99, 135, 150, 164, 176, 198, 209,\n",
    "       215, 234, 241, 277])\n",
    "ax = ddc.cellmap(clean_data.drop(columns=['Price']), figsize=(8,10), row_zoom=row_indices)\n",
    "cars = data.data.apply(lambda row: f\"{row['Maker']} {row['Model']}\", axis=1).tolist()\n",
    "ax.set_yticklabels([cars[i] for i in row_indices], rotation=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can additionally zoom in on 2 BMWs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x100 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = ddc.cellmap(clean_data.drop(columns=['Price']),row_zoom=[31,41], figsize=(7,1))\n",
    "ax.set_yticklabels([cars[i] for i in [31,41]], rotation=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "total flagged cells:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddc.cellwise_outliers_.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DDC also predicts rowwise outliers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "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>Price</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>BHP</th>\n",
       "      <th>Torque</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>TopSpeed</th>\n",
       "      <th>MPG</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Length</th>\n",
       "      <th>Width</th>\n",
       "      <th>Height</th>\n",
       "      <th>Price_transformed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>44995.0</td>\n",
       "      <td>0.219169</td>\n",
       "      <td>0.701029</td>\n",
       "      <td>-0.220938</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.921638</td>\n",
       "      <td>NaN</td>\n",
       "      <td>575.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>1685.0</td>\n",
       "      <td>1140.0</td>\n",
       "      <td>0.668256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>22995.0</td>\n",
       "      <td>-0.569636</td>\n",
       "      <td>-0.524540</td>\n",
       "      <td>-1.071051</td>\n",
       "      <td>5.9</td>\n",
       "      <td>-0.748566</td>\n",
       "      <td>NaN</td>\n",
       "      <td>550.0</td>\n",
       "      <td>3380.0</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>1115.0</td>\n",
       "      <td>-0.247651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>30245.0</td>\n",
       "      <td>0.613284</td>\n",
       "      <td>0.109823</td>\n",
       "      <td>0.297162</td>\n",
       "      <td>12.8</td>\n",
       "      <td>-0.590064</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2305.0</td>\n",
       "      <td>5218.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1818.0</td>\n",
       "      <td>0.157953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>25995.0</td>\n",
       "      <td>0.613284</td>\n",
       "      <td>0.276559</td>\n",
       "      <td>0.782252</td>\n",
       "      <td>12.9</td>\n",
       "      <td>-1.033160</td>\n",
       "      <td>34.0</td>\n",
       "      <td>2075.0</td>\n",
       "      <td>4223.0</td>\n",
       "      <td>1873.0</td>\n",
       "      <td>1840.0</td>\n",
       "      <td>-0.060330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>28195.0</td>\n",
       "      <td>0.161107</td>\n",
       "      <td>-0.571367</td>\n",
       "      <td>0.297162</td>\n",
       "      <td>14.7</td>\n",
       "      <td>-2.246128</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2120.0</td>\n",
       "      <td>4785.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>0.058518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>36200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>876.0</td>\n",
       "      <td>3785.0</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>1117.0</td>\n",
       "      <td>0.399504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>29045.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.988366</td>\n",
       "      <td>-0.920764</td>\n",
       "      <td>15.9</td>\n",
       "      <td>-2.161801</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1110.0</td>\n",
       "      <td>3475.0</td>\n",
       "      <td>1475.0</td>\n",
       "      <td>1610.0</td>\n",
       "      <td>0.100958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>-0.075638</td>\n",
       "      <td>-0.775908</td>\n",
       "      <td>-1.348513</td>\n",
       "      <td>4.5</td>\n",
       "      <td>-0.344059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>490.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.146581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>6950.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.267469</td>\n",
       "      <td>-2.504821</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.518526</td>\n",
       "      <td>NaN</td>\n",
       "      <td>450.0</td>\n",
       "      <td>2337.0</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>-2.690158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>17995.0</td>\n",
       "      <td>-0.042303</td>\n",
       "      <td>-0.128325</td>\n",
       "      <td>0.297162</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.154841</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2059.0</td>\n",
       "      <td>5125.0</td>\n",
       "      <td>1915.0</td>\n",
       "      <td>1845.0</td>\n",
       "      <td>-0.652657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Price  Displacement       BHP    Torque  Acceleration  TopSpeed   MPG  \\\n",
       "50   44995.0      0.219169  0.701029 -0.220938           3.1  0.921638   NaN   \n",
       "51   22995.0     -0.569636 -0.524540 -1.071051           5.9 -0.748566   NaN   \n",
       "61   30245.0      0.613284  0.109823  0.297162          12.8 -0.590064  35.0   \n",
       "124  25995.0      0.613284  0.276559  0.782252          12.9 -1.033160  34.0   \n",
       "135  28195.0      0.161107 -0.571367  0.297162          14.7 -2.246128  25.0   \n",
       "145  36200.0           NaN       NaN       NaN           0.0       NaN   NaN   \n",
       "180  29045.0           NaN -1.988366 -0.920764          15.9 -2.161801   NaN   \n",
       "184  30000.0     -0.075638 -0.775908 -1.348513           4.5 -0.344059   NaN   \n",
       "219   6950.0           NaN -6.267469 -2.504821           0.0 -8.518526   NaN   \n",
       "234  17995.0     -0.042303 -0.128325  0.297162           0.0 -1.154841  38.0   \n",
       "\n",
       "     Weight  Length   Width  Height  Price_transformed  \n",
       "50    575.0  3300.0  1685.0  1140.0           0.668256  \n",
       "51    550.0  3380.0  1575.0  1115.0          -0.247651  \n",
       "61   2305.0  5218.0  1998.0  1818.0           0.157953  \n",
       "124  2075.0  4223.0  1873.0  1840.0          -0.060330  \n",
       "135  2120.0  4785.0  1790.0  1790.0           0.058518  \n",
       "145   876.0  3785.0  1850.0  1117.0           0.399504  \n",
       "180  1110.0  3475.0  1475.0  1610.0           0.100958  \n",
       "184   490.0     NaN     NaN     NaN           0.146581  \n",
       "219   450.0  2337.0  1237.0  1461.0          -2.690158  \n",
       "234  2059.0  5125.0  1915.0  1845.0          -0.652657  "
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_data.loc[ddc.predict(clean_data.drop(columns=['Price']), rowwise=True)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, DDC can impute missing data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_2842a\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_2842a_level0_col0\" class=\"col_heading level0 col0\" >Displacement</th>\n",
       "      <th id=\"T_2842a_level0_col1\" class=\"col_heading level0 col1\" >BHP</th>\n",
       "      <th id=\"T_2842a_level0_col2\" class=\"col_heading level0 col2\" >Torque</th>\n",
       "      <th id=\"T_2842a_level0_col3\" class=\"col_heading level0 col3\" >Acceleration</th>\n",
       "      <th id=\"T_2842a_level0_col4\" class=\"col_heading level0 col4\" >TopSpeed</th>\n",
       "      <th id=\"T_2842a_level0_col5\" class=\"col_heading level0 col5\" >MPG</th>\n",
       "      <th id=\"T_2842a_level0_col6\" class=\"col_heading level0 col6\" >Weight</th>\n",
       "      <th id=\"T_2842a_level0_col7\" class=\"col_heading level0 col7\" >Length</th>\n",
       "      <th id=\"T_2842a_level0_col8\" class=\"col_heading level0 col8\" >Width</th>\n",
       "      <th id=\"T_2842a_level0_col9\" class=\"col_heading level0 col9\" >Height</th>\n",
       "      <th id=\"T_2842a_level0_col10\" class=\"col_heading level0 col10\" >Price_transformed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_2842a_level0_row0\" class=\"row_heading level0 row0\" >50</th>\n",
       "      <td id=\"T_2842a_row0_col0\" class=\"data row0 col0\" >0.22</td>\n",
       "      <td id=\"T_2842a_row0_col1\" class=\"data row0 col1\" >-0.04</td>\n",
       "      <td id=\"T_2842a_row0_col2\" class=\"data row0 col2\" >-0.22</td>\n",
       "      <td id=\"T_2842a_row0_col3\" class=\"data row0 col3\" >8.61</td>\n",
       "      <td id=\"T_2842a_row0_col4\" class=\"data row0 col4\" >0.92</td>\n",
       "      <td id=\"T_2842a_row0_col5\" class=\"data row0 col5\" >41.82</td>\n",
       "      <td id=\"T_2842a_row0_col6\" class=\"data row0 col6\" >1364.54</td>\n",
       "      <td id=\"T_2842a_row0_col7\" class=\"data row0 col7\" >4331.18</td>\n",
       "      <td id=\"T_2842a_row0_col8\" class=\"data row0 col8\" >1685.00</td>\n",
       "      <td id=\"T_2842a_row0_col9\" class=\"data row0 col9\" >1140.00</td>\n",
       "      <td id=\"T_2842a_row0_col10\" class=\"data row0 col10\" >-0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2842a_level0_row1\" class=\"row_heading level0 row1\" >51</th>\n",
       "      <td id=\"T_2842a_row1_col0\" class=\"data row1 col0\" >-0.57</td>\n",
       "      <td id=\"T_2842a_row1_col1\" class=\"data row1 col1\" >-0.52</td>\n",
       "      <td id=\"T_2842a_row1_col2\" class=\"data row1 col2\" >-1.07</td>\n",
       "      <td id=\"T_2842a_row1_col3\" class=\"data row1 col3\" >11.65</td>\n",
       "      <td id=\"T_2842a_row1_col4\" class=\"data row1 col4\" >-0.75</td>\n",
       "      <td id=\"T_2842a_row1_col5\" class=\"data row1 col5\" >55.07</td>\n",
       "      <td id=\"T_2842a_row1_col6\" class=\"data row1 col6\" >550.00</td>\n",
       "      <td id=\"T_2842a_row1_col7\" class=\"data row1 col7\" >3380.00</td>\n",
       "      <td id=\"T_2842a_row1_col8\" class=\"data row1 col8\" >1722.44</td>\n",
       "      <td id=\"T_2842a_row1_col9\" class=\"data row1 col9\" >1482.87</td>\n",
       "      <td id=\"T_2842a_row1_col10\" class=\"data row1 col10\" >-1.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2842a_level0_row2\" class=\"row_heading level0 row2\" >61</th>\n",
       "      <td id=\"T_2842a_row2_col0\" class=\"data row2 col0\" >0.61</td>\n",
       "      <td id=\"T_2842a_row2_col1\" class=\"data row2 col1\" >0.11</td>\n",
       "      <td id=\"T_2842a_row2_col2\" class=\"data row2 col2\" >0.30</td>\n",
       "      <td id=\"T_2842a_row2_col3\" class=\"data row2 col3\" >7.88</td>\n",
       "      <td id=\"T_2842a_row2_col4\" class=\"data row2 col4\" >-0.59</td>\n",
       "      <td id=\"T_2842a_row2_col5\" class=\"data row2 col5\" >35.00</td>\n",
       "      <td id=\"T_2842a_row2_col6\" class=\"data row2 col6\" >2305.00</td>\n",
       "      <td id=\"T_2842a_row2_col7\" class=\"data row2 col7\" >5218.00</td>\n",
       "      <td id=\"T_2842a_row2_col8\" class=\"data row2 col8\" >1869.97</td>\n",
       "      <td id=\"T_2842a_row2_col9\" class=\"data row2 col9\" >1818.00</td>\n",
       "      <td id=\"T_2842a_row2_col10\" class=\"data row2 col10\" >0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2842a_level0_row3\" class=\"row_heading level0 row3\" >124</th>\n",
       "      <td id=\"T_2842a_row3_col0\" class=\"data row3 col0\" >0.61</td>\n",
       "      <td id=\"T_2842a_row3_col1\" class=\"data row3 col1\" >0.28</td>\n",
       "      <td id=\"T_2842a_row3_col2\" class=\"data row3 col2\" >0.78</td>\n",
       "      <td id=\"T_2842a_row3_col3\" class=\"data row3 col3\" >8.73</td>\n",
       "      <td id=\"T_2842a_row3_col4\" class=\"data row3 col4\" >-1.03</td>\n",
       "      <td id=\"T_2842a_row3_col5\" class=\"data row3 col5\" >34.00</td>\n",
       "      <td id=\"T_2842a_row3_col6\" class=\"data row3 col6\" >2075.00</td>\n",
       "      <td id=\"T_2842a_row3_col7\" class=\"data row3 col7\" >4223.00</td>\n",
       "      <td id=\"T_2842a_row3_col8\" class=\"data row3 col8\" >1873.00</td>\n",
       "      <td id=\"T_2842a_row3_col9\" class=\"data row3 col9\" >1840.00</td>\n",
       "      <td id=\"T_2842a_row3_col10\" class=\"data row3 col10\" >-0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2842a_level0_row4\" class=\"row_heading level0 row4\" >135</th>\n",
       "      <td id=\"T_2842a_row4_col0\" class=\"data row4 col0\" >0.16</td>\n",
       "      <td id=\"T_2842a_row4_col1\" class=\"data row4 col1\" >-0.57</td>\n",
       "      <td id=\"T_2842a_row4_col2\" class=\"data row4 col2\" >0.30</td>\n",
       "      <td id=\"T_2842a_row4_col3\" class=\"data row4 col3\" >9.85</td>\n",
       "      <td id=\"T_2842a_row4_col4\" class=\"data row4 col4\" >-0.23</td>\n",
       "      <td id=\"T_2842a_row4_col5\" class=\"data row4 col5\" >25.00</td>\n",
       "      <td id=\"T_2842a_row4_col6\" class=\"data row4 col6\" >1483.18</td>\n",
       "      <td id=\"T_2842a_row4_col7\" class=\"data row4 col7\" >4785.00</td>\n",
       "      <td id=\"T_2842a_row4_col8\" class=\"data row4 col8\" >1790.00</td>\n",
       "      <td id=\"T_2842a_row4_col9\" class=\"data row4 col9\" >1790.00</td>\n",
       "      <td id=\"T_2842a_row4_col10\" class=\"data row4 col10\" >0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2842a_level0_row5\" class=\"row_heading level0 row5\" >145</th>\n",
       "      <td id=\"T_2842a_row5_col0\" class=\"data row5 col0\" >0.21</td>\n",
       "      <td id=\"T_2842a_row5_col1\" class=\"data row5 col1\" >0.49</td>\n",
       "      <td id=\"T_2842a_row5_col2\" class=\"data row5 col2\" >0.12</td>\n",
       "      <td id=\"T_2842a_row5_col3\" class=\"data row5 col3\" >8.79</td>\n",
       "      <td id=\"T_2842a_row5_col4\" class=\"data row5 col4\" >0.87</td>\n",
       "      <td id=\"T_2842a_row5_col5\" class=\"data row5 col5\" >36.24</td>\n",
       "      <td id=\"T_2842a_row5_col6\" class=\"data row5 col6\" >1515.46</td>\n",
       "      <td id=\"T_2842a_row5_col7\" class=\"data row5 col7\" >3785.00</td>\n",
       "      <td id=\"T_2842a_row5_col8\" class=\"data row5 col8\" >1850.00</td>\n",
       "      <td id=\"T_2842a_row5_col9\" class=\"data row5 col9\" >1117.00</td>\n",
       "      <td id=\"T_2842a_row5_col10\" class=\"data row5 col10\" >0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2842a_level0_row6\" class=\"row_heading level0 row6\" >180</th>\n",
       "      <td id=\"T_2842a_row6_col0\" class=\"data row6 col0\" >-1.54</td>\n",
       "      <td id=\"T_2842a_row6_col1\" class=\"data row6 col1\" >-1.99</td>\n",
       "      <td id=\"T_2842a_row6_col2\" class=\"data row6 col2\" >-0.92</td>\n",
       "      <td id=\"T_2842a_row6_col3\" class=\"data row6 col3\" >15.90</td>\n",
       "      <td id=\"T_2842a_row6_col4\" class=\"data row6 col4\" >-2.16</td>\n",
       "      <td id=\"T_2842a_row6_col5\" class=\"data row6 col5\" >65.02</td>\n",
       "      <td id=\"T_2842a_row6_col6\" class=\"data row6 col6\" >1110.00</td>\n",
       "      <td id=\"T_2842a_row6_col7\" class=\"data row6 col7\" >3475.00</td>\n",
       "      <td id=\"T_2842a_row6_col8\" class=\"data row6 col8\" >1693.47</td>\n",
       "      <td id=\"T_2842a_row6_col9\" class=\"data row6 col9\" >1610.00</td>\n",
       "      <td id=\"T_2842a_row6_col10\" class=\"data row6 col10\" >-1.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2842a_level0_row7\" class=\"row_heading level0 row7\" >184</th>\n",
       "      <td id=\"T_2842a_row7_col0\" class=\"data row7 col0\" >-0.08</td>\n",
       "      <td id=\"T_2842a_row7_col1\" class=\"data row7 col1\" >-0.78</td>\n",
       "      <td id=\"T_2842a_row7_col2\" class=\"data row7 col2\" >-1.35</td>\n",
       "      <td id=\"T_2842a_row7_col3\" class=\"data row7 col3\" >11.16</td>\n",
       "      <td id=\"T_2842a_row7_col4\" class=\"data row7 col4\" >-0.34</td>\n",
       "      <td id=\"T_2842a_row7_col5\" class=\"data row7 col5\" >50.55</td>\n",
       "      <td id=\"T_2842a_row7_col6\" class=\"data row7 col6\" >1391.73</td>\n",
       "      <td id=\"T_2842a_row7_col7\" class=\"data row7 col7\" >4163.08</td>\n",
       "      <td id=\"T_2842a_row7_col8\" class=\"data row7 col8\" >1757.11</td>\n",
       "      <td id=\"T_2842a_row7_col9\" class=\"data row7 col9\" >1447.99</td>\n",
       "      <td id=\"T_2842a_row7_col10\" class=\"data row7 col10\" >0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2842a_level0_row8\" class=\"row_heading level0 row8\" >219</th>\n",
       "      <td id=\"T_2842a_row8_col0\" class=\"data row8 col0\" >-1.47</td>\n",
       "      <td id=\"T_2842a_row8_col1\" class=\"data row8 col1\" >-1.12</td>\n",
       "      <td id=\"T_2842a_row8_col2\" class=\"data row8 col2\" >-1.12</td>\n",
       "      <td id=\"T_2842a_row8_col3\" class=\"data row8 col3\" >15.50</td>\n",
       "      <td id=\"T_2842a_row8_col4\" class=\"data row8 col4\" >-0.61</td>\n",
       "      <td id=\"T_2842a_row8_col5\" class=\"data row8 col5\" >58.45</td>\n",
       "      <td id=\"T_2842a_row8_col6\" class=\"data row8 col6\" >450.00</td>\n",
       "      <td id=\"T_2842a_row8_col7\" class=\"data row8 col7\" >3667.15</td>\n",
       "      <td id=\"T_2842a_row8_col8\" class=\"data row8 col8\" >1654.07</td>\n",
       "      <td id=\"T_2842a_row8_col9\" class=\"data row8 col9\" >1461.00</td>\n",
       "      <td id=\"T_2842a_row8_col10\" class=\"data row8 col10\" >-0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2842a_level0_row9\" class=\"row_heading level0 row9\" >234</th>\n",
       "      <td id=\"T_2842a_row9_col0\" class=\"data row9 col0\" >-0.04</td>\n",
       "      <td id=\"T_2842a_row9_col1\" class=\"data row9 col1\" >0.55</td>\n",
       "      <td id=\"T_2842a_row9_col2\" class=\"data row9 col2\" >0.30</td>\n",
       "      <td id=\"T_2842a_row9_col3\" class=\"data row9 col3\" >9.32</td>\n",
       "      <td id=\"T_2842a_row9_col4\" class=\"data row9 col4\" >0.50</td>\n",
       "      <td id=\"T_2842a_row9_col5\" class=\"data row9 col5\" >38.00</td>\n",
       "      <td id=\"T_2842a_row9_col6\" class=\"data row9 col6\" >2059.00</td>\n",
       "      <td id=\"T_2842a_row9_col7\" class=\"data row9 col7\" >5125.00</td>\n",
       "      <td id=\"T_2842a_row9_col8\" class=\"data row9 col8\" >1915.00</td>\n",
       "      <td id=\"T_2842a_row9_col9\" class=\"data row9 col9\" >1428.55</td>\n",
       "      <td id=\"T_2842a_row9_col10\" class=\"data row9 col10\" >0.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fe130178820>"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddc.impute(clean_data.drop(columns=['Price'])).loc[ddc.predict(clean_data.drop(columns=['Price']), rowwise=True), :].style.format('{:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use robust regression to predict the (transformed) price using the remaining variables. For regression, we again have to drop all missings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = clean_data2.drop(columns=['Price', 'Price_transformed'])\n",
    "y = clean_data2['Price_transformed']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an example, we use the MM-estimator of regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.71338919e-01,  4.24224370e-01,  2.04835077e-01,  3.63925688e-02,\n",
       "        8.75191584e-02,  3.77330897e-03,  4.65624836e-04, -2.97257613e-04,\n",
       "        8.37238866e-04, -9.42993337e-04])"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator = MMEstimator().fit(X, y)\n",
    "estimator.model.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now get a diagnostic plot and ask for the underlying data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resid, std_resid, distances, vt, ht = estimator.diagnostic_plot(X, y.to_numpy(), return_data=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can get an overview of the bad leverage points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "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</th>\n",
       "      <th>Model</th>\n",
       "      <th>Price</th>\n",
       "      <th>predicted_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aston Martin</td>\n",
       "      <td>Cygnet</td>\n",
       "      <td>30995.0</td>\n",
       "      <td>15326.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Aston Martin</td>\n",
       "      <td>V12 Zagato</td>\n",
       "      <td>396000.0</td>\n",
       "      <td>117938.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>G-Class</td>\n",
       "      <td>82945.0</td>\n",
       "      <td>34576.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>Rolls-Royce</td>\n",
       "      <td>Phantom</td>\n",
       "      <td>352720.0</td>\n",
       "      <td>116166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>Rolls-Royce</td>\n",
       "      <td>Phantom Coupe</td>\n",
       "      <td>333130.0</td>\n",
       "      <td>111003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Prius</td>\n",
       "      <td>24045.0</td>\n",
       "      <td>16272.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Maker          Model     Price  predicted_price\n",
       "2     Aston Martin         Cygnet   30995.0          15326.0\n",
       "5     Aston Martin     V12 Zagato  396000.0         117938.0\n",
       "164  Mercedes-Benz        G-Class   82945.0          34576.0\n",
       "222    Rolls-Royce        Phantom  352720.0         116166.0\n",
       "223    Rolls-Royce  Phantom Coupe  333130.0         111003.0\n",
       "253         Toyota          Prius   24045.0          16272.0"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_leverage_idx = (np.abs(std_resid) > vt) & (distances > ht)\n",
    "data.data.loc[\n",
    "    clean_data2[bad_leverage_idx].index, ['Maker', 'Model', 'Price']\n",
    "].assign(predicted_price=price_transformer.inverse_transform(estimator.predict(X.loc[bad_leverage_idx])).round())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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