{
 "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": 1,
   "metadata": {},
   "outputs": [],
   "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 FastMCD, OGK\n",
    "from robpy.pca import ROBPCA\n",
    "from robpy.outliers import DDC\n",
    "from robpy.regression import MMRegression\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": 2,
   "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",
      "    * Make (str): the car brand.\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": 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>Make</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": [
       "           Make        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": 3,
     "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": 4,
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"non_numeric_cols\": [\n",
      "        \"Make\",\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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rows_missings': [69, 95]}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaner.dropped_rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also exclude the subjective variable `Verdict` from the features for further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 8,
   "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": 8,
     "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'],figsize=(2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('boxcox', -0.42354039562300644)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_transformer.method, price_transformer.lambda_rew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 12,
     "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "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": 15,
   "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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "mcd = FastMCD().fit(clean_data2.drop(columns=['Price']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ogk = OGK().fit(clean_data2.drop(columns=['Price']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Price</th>\n",
       "      <th>Torque</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>BHP</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Height</th>\n",
       "      <th>Length</th>\n",
       "      <th>TopSpeed</th>\n",
       "      <th>Weight</th>\n",
       "      <th>MPG</th>\n",
       "      <th>Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>BMW</td>\n",
       "      <td>i3</td>\n",
       "      <td>33830.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>3999.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>1315.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>1775.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Make Model    Price  Torque  Displacement    BHP  Acceleration  Height  \\\n",
       "41  BMW    i3  33830.0   184.0         647.0  170.0           7.9  1578.0   \n",
       "\n",
       "    Length  TopSpeed  Weight    MPG   Width  \n",
       "41  3999.0      93.0  1315.0  470.0  1775.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.data.loc[\n",
    "    clean_data2.index[(ogk._robust_distances > 80) & (ogk._mahalanobis_distances > 12)], \n",
    "    ['Make', 'Model'] + list(set(clean_data2.columns).intersection(set(data.data.columns)))\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CellMCD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A 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": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_data = RobustScaler(with_centering=False).fit_transform(clean_data2.drop(columns=['Price']))\n",
    "pca = ROBPCA().fit(scaled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 23,
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Price</th>\n",
       "      <th>Torque</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>BHP</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Height</th>\n",
       "      <th>Length</th>\n",
       "      <th>TopSpeed</th>\n",
       "      <th>Weight</th>\n",
       "      <th>MPG</th>\n",
       "      <th>Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>BMW</td>\n",
       "      <td>i3</td>\n",
       "      <td>33830.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>3999.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>1315.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>1775.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Bugatti</td>\n",
       "      <td>Veyron</td>\n",
       "      <td>1139985.0</td>\n",
       "      <td>922.0</td>\n",
       "      <td>7993.0</td>\n",
       "      <td>987.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1204.0</td>\n",
       "      <td>4462.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1998.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>Land Rover</td>\n",
       "      <td>Defender</td>\n",
       "      <td>28195.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>2198.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>14.7</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>4785.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>2120.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1790.0</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>398.0</td>\n",
       "      <td>2987.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>1951.0</td>\n",
       "      <td>4662.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Pagani</td>\n",
       "      <td>Huayra</td>\n",
       "      <td>990000.0</td>\n",
       "      <td>811.0</td>\n",
       "      <td>5980.0</td>\n",
       "      <td>730.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1169.0</td>\n",
       "      <td>4605.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2036.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Make     Model      Price  Torque  Displacement    BHP  \\\n",
       "41             BMW        i3    33830.0   184.0         647.0  170.0   \n",
       "49         Bugatti    Veyron  1139985.0   922.0        7993.0  987.0   \n",
       "135     Land Rover  Defender    28195.0   265.0        2198.0  122.0   \n",
       "164  Mercedes-Benz   G-Class    82945.0   398.0        2987.0  211.0   \n",
       "196         Pagani    Huayra   990000.0   811.0        5980.0  730.0   \n",
       "\n",
       "     Acceleration  Height  Length  TopSpeed  Weight    MPG   Width  \n",
       "41            7.9  1578.0  3999.0      93.0  1315.0  470.0  1775.0  \n",
       "49            2.5  1204.0  4462.0     252.0  1990.0   10.0  1998.0  \n",
       "135          14.7  1790.0  4785.0      90.0  2120.0   25.0  1790.0  \n",
       "164           9.1  1951.0  4662.0     108.0  2500.0   25.0  1760.0  \n",
       "196           3.3  1169.0  4605.0     230.0  1350.0   23.0  2036.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.data.loc[\n",
    "    clean_data2.loc[(score_distances > score_cutoff) & (orthogonal_distances > od_cutoff)].index, \n",
    "    ['Make', '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": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "ddc = DDC().fit(clean_data.drop(columns=['Price']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 25,
     "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": 26,
   "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['Make']} {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": 27,
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88"
      ]
     },
     "execution_count": 28,
     "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": 29,
   "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": 29,
     "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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_be260\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_be260_level0_col0\" class=\"col_heading level0 col0\" >Displacement</th>\n",
       "      <th id=\"T_be260_level0_col1\" class=\"col_heading level0 col1\" >BHP</th>\n",
       "      <th id=\"T_be260_level0_col2\" class=\"col_heading level0 col2\" >Torque</th>\n",
       "      <th id=\"T_be260_level0_col3\" class=\"col_heading level0 col3\" >Acceleration</th>\n",
       "      <th id=\"T_be260_level0_col4\" class=\"col_heading level0 col4\" >TopSpeed</th>\n",
       "      <th id=\"T_be260_level0_col5\" class=\"col_heading level0 col5\" >MPG</th>\n",
       "      <th id=\"T_be260_level0_col6\" class=\"col_heading level0 col6\" >Weight</th>\n",
       "      <th id=\"T_be260_level0_col7\" class=\"col_heading level0 col7\" >Length</th>\n",
       "      <th id=\"T_be260_level0_col8\" class=\"col_heading level0 col8\" >Width</th>\n",
       "      <th id=\"T_be260_level0_col9\" class=\"col_heading level0 col9\" >Height</th>\n",
       "      <th id=\"T_be260_level0_col10\" class=\"col_heading level0 col10\" >Price_transformed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_be260_level0_row0\" class=\"row_heading level0 row0\" >50</th>\n",
       "      <td id=\"T_be260_row0_col0\" class=\"data row0 col0\" >0.22</td>\n",
       "      <td id=\"T_be260_row0_col1\" class=\"data row0 col1\" >-0.04</td>\n",
       "      <td id=\"T_be260_row0_col2\" class=\"data row0 col2\" >-0.22</td>\n",
       "      <td id=\"T_be260_row0_col3\" class=\"data row0 col3\" >8.61</td>\n",
       "      <td id=\"T_be260_row0_col4\" class=\"data row0 col4\" >0.92</td>\n",
       "      <td id=\"T_be260_row0_col5\" class=\"data row0 col5\" >41.83</td>\n",
       "      <td id=\"T_be260_row0_col6\" class=\"data row0 col6\" >1364.54</td>\n",
       "      <td id=\"T_be260_row0_col7\" class=\"data row0 col7\" >4331.18</td>\n",
       "      <td id=\"T_be260_row0_col8\" class=\"data row0 col8\" >1685.00</td>\n",
       "      <td id=\"T_be260_row0_col9\" class=\"data row0 col9\" >1140.00</td>\n",
       "      <td id=\"T_be260_row0_col10\" class=\"data row0 col10\" >-0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be260_level0_row1\" class=\"row_heading level0 row1\" >51</th>\n",
       "      <td id=\"T_be260_row1_col0\" class=\"data row1 col0\" >-0.57</td>\n",
       "      <td id=\"T_be260_row1_col1\" class=\"data row1 col1\" >-0.52</td>\n",
       "      <td id=\"T_be260_row1_col2\" class=\"data row1 col2\" >-1.07</td>\n",
       "      <td id=\"T_be260_row1_col3\" class=\"data row1 col3\" >11.65</td>\n",
       "      <td id=\"T_be260_row1_col4\" class=\"data row1 col4\" >-0.75</td>\n",
       "      <td id=\"T_be260_row1_col5\" class=\"data row1 col5\" >55.07</td>\n",
       "      <td id=\"T_be260_row1_col6\" class=\"data row1 col6\" >550.00</td>\n",
       "      <td id=\"T_be260_row1_col7\" class=\"data row1 col7\" >3380.00</td>\n",
       "      <td id=\"T_be260_row1_col8\" class=\"data row1 col8\" >1722.44</td>\n",
       "      <td id=\"T_be260_row1_col9\" class=\"data row1 col9\" >1482.87</td>\n",
       "      <td id=\"T_be260_row1_col10\" class=\"data row1 col10\" >-1.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be260_level0_row2\" class=\"row_heading level0 row2\" >61</th>\n",
       "      <td id=\"T_be260_row2_col0\" class=\"data row2 col0\" >0.61</td>\n",
       "      <td id=\"T_be260_row2_col1\" class=\"data row2 col1\" >0.11</td>\n",
       "      <td id=\"T_be260_row2_col2\" class=\"data row2 col2\" >0.30</td>\n",
       "      <td id=\"T_be260_row2_col3\" class=\"data row2 col3\" >7.88</td>\n",
       "      <td id=\"T_be260_row2_col4\" class=\"data row2 col4\" >-0.59</td>\n",
       "      <td id=\"T_be260_row2_col5\" class=\"data row2 col5\" >35.00</td>\n",
       "      <td id=\"T_be260_row2_col6\" class=\"data row2 col6\" >2305.00</td>\n",
       "      <td id=\"T_be260_row2_col7\" class=\"data row2 col7\" >5218.00</td>\n",
       "      <td id=\"T_be260_row2_col8\" class=\"data row2 col8\" >1869.97</td>\n",
       "      <td id=\"T_be260_row2_col9\" class=\"data row2 col9\" >1818.00</td>\n",
       "      <td id=\"T_be260_row2_col10\" class=\"data row2 col10\" >0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be260_level0_row3\" class=\"row_heading level0 row3\" >124</th>\n",
       "      <td id=\"T_be260_row3_col0\" class=\"data row3 col0\" >0.61</td>\n",
       "      <td id=\"T_be260_row3_col1\" class=\"data row3 col1\" >0.28</td>\n",
       "      <td id=\"T_be260_row3_col2\" class=\"data row3 col2\" >0.78</td>\n",
       "      <td id=\"T_be260_row3_col3\" class=\"data row3 col3\" >8.73</td>\n",
       "      <td id=\"T_be260_row3_col4\" class=\"data row3 col4\" >-1.03</td>\n",
       "      <td id=\"T_be260_row3_col5\" class=\"data row3 col5\" >34.00</td>\n",
       "      <td id=\"T_be260_row3_col6\" class=\"data row3 col6\" >2075.00</td>\n",
       "      <td id=\"T_be260_row3_col7\" class=\"data row3 col7\" >4223.00</td>\n",
       "      <td id=\"T_be260_row3_col8\" class=\"data row3 col8\" >1873.00</td>\n",
       "      <td id=\"T_be260_row3_col9\" class=\"data row3 col9\" >1840.00</td>\n",
       "      <td id=\"T_be260_row3_col10\" class=\"data row3 col10\" >-0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be260_level0_row4\" class=\"row_heading level0 row4\" >135</th>\n",
       "      <td id=\"T_be260_row4_col0\" class=\"data row4 col0\" >0.16</td>\n",
       "      <td id=\"T_be260_row4_col1\" class=\"data row4 col1\" >-0.57</td>\n",
       "      <td id=\"T_be260_row4_col2\" class=\"data row4 col2\" >0.30</td>\n",
       "      <td id=\"T_be260_row4_col3\" class=\"data row4 col3\" >9.85</td>\n",
       "      <td id=\"T_be260_row4_col4\" class=\"data row4 col4\" >-0.23</td>\n",
       "      <td id=\"T_be260_row4_col5\" class=\"data row4 col5\" >25.00</td>\n",
       "      <td id=\"T_be260_row4_col6\" class=\"data row4 col6\" >1483.18</td>\n",
       "      <td id=\"T_be260_row4_col7\" class=\"data row4 col7\" >4785.00</td>\n",
       "      <td id=\"T_be260_row4_col8\" class=\"data row4 col8\" >1790.00</td>\n",
       "      <td id=\"T_be260_row4_col9\" class=\"data row4 col9\" >1790.00</td>\n",
       "      <td id=\"T_be260_row4_col10\" class=\"data row4 col10\" >0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be260_level0_row5\" class=\"row_heading level0 row5\" >145</th>\n",
       "      <td id=\"T_be260_row5_col0\" class=\"data row5 col0\" >0.21</td>\n",
       "      <td id=\"T_be260_row5_col1\" class=\"data row5 col1\" >0.49</td>\n",
       "      <td id=\"T_be260_row5_col2\" class=\"data row5 col2\" >0.12</td>\n",
       "      <td id=\"T_be260_row5_col3\" class=\"data row5 col3\" >8.79</td>\n",
       "      <td id=\"T_be260_row5_col4\" class=\"data row5 col4\" >0.87</td>\n",
       "      <td id=\"T_be260_row5_col5\" class=\"data row5 col5\" >36.24</td>\n",
       "      <td id=\"T_be260_row5_col6\" class=\"data row5 col6\" >1515.46</td>\n",
       "      <td id=\"T_be260_row5_col7\" class=\"data row5 col7\" >3785.00</td>\n",
       "      <td id=\"T_be260_row5_col8\" class=\"data row5 col8\" >1850.00</td>\n",
       "      <td id=\"T_be260_row5_col9\" class=\"data row5 col9\" >1117.00</td>\n",
       "      <td id=\"T_be260_row5_col10\" class=\"data row5 col10\" >0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be260_level0_row6\" class=\"row_heading level0 row6\" >180</th>\n",
       "      <td id=\"T_be260_row6_col0\" class=\"data row6 col0\" >-1.54</td>\n",
       "      <td id=\"T_be260_row6_col1\" class=\"data row6 col1\" >-1.99</td>\n",
       "      <td id=\"T_be260_row6_col2\" class=\"data row6 col2\" >-0.92</td>\n",
       "      <td id=\"T_be260_row6_col3\" class=\"data row6 col3\" >15.90</td>\n",
       "      <td id=\"T_be260_row6_col4\" class=\"data row6 col4\" >-2.16</td>\n",
       "      <td id=\"T_be260_row6_col5\" class=\"data row6 col5\" >65.02</td>\n",
       "      <td id=\"T_be260_row6_col6\" class=\"data row6 col6\" >1110.00</td>\n",
       "      <td id=\"T_be260_row6_col7\" class=\"data row6 col7\" >3475.00</td>\n",
       "      <td id=\"T_be260_row6_col8\" class=\"data row6 col8\" >1693.47</td>\n",
       "      <td id=\"T_be260_row6_col9\" class=\"data row6 col9\" >1610.00</td>\n",
       "      <td id=\"T_be260_row6_col10\" class=\"data row6 col10\" >-1.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be260_level0_row7\" class=\"row_heading level0 row7\" >184</th>\n",
       "      <td id=\"T_be260_row7_col0\" class=\"data row7 col0\" >-0.08</td>\n",
       "      <td id=\"T_be260_row7_col1\" class=\"data row7 col1\" >-0.78</td>\n",
       "      <td id=\"T_be260_row7_col2\" class=\"data row7 col2\" >-1.35</td>\n",
       "      <td id=\"T_be260_row7_col3\" class=\"data row7 col3\" >11.16</td>\n",
       "      <td id=\"T_be260_row7_col4\" class=\"data row7 col4\" >-0.34</td>\n",
       "      <td id=\"T_be260_row7_col5\" class=\"data row7 col5\" >50.55</td>\n",
       "      <td id=\"T_be260_row7_col6\" class=\"data row7 col6\" >1391.73</td>\n",
       "      <td id=\"T_be260_row7_col7\" class=\"data row7 col7\" >4163.08</td>\n",
       "      <td id=\"T_be260_row7_col8\" class=\"data row7 col8\" >1757.12</td>\n",
       "      <td id=\"T_be260_row7_col9\" class=\"data row7 col9\" >1447.99</td>\n",
       "      <td id=\"T_be260_row7_col10\" class=\"data row7 col10\" >0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be260_level0_row8\" class=\"row_heading level0 row8\" >219</th>\n",
       "      <td id=\"T_be260_row8_col0\" class=\"data row8 col0\" >-1.47</td>\n",
       "      <td id=\"T_be260_row8_col1\" class=\"data row8 col1\" >-1.12</td>\n",
       "      <td id=\"T_be260_row8_col2\" class=\"data row8 col2\" >-1.12</td>\n",
       "      <td id=\"T_be260_row8_col3\" class=\"data row8 col3\" >15.50</td>\n",
       "      <td id=\"T_be260_row8_col4\" class=\"data row8 col4\" >-0.61</td>\n",
       "      <td id=\"T_be260_row8_col5\" class=\"data row8 col5\" >58.45</td>\n",
       "      <td id=\"T_be260_row8_col6\" class=\"data row8 col6\" >450.00</td>\n",
       "      <td id=\"T_be260_row8_col7\" class=\"data row8 col7\" >3667.15</td>\n",
       "      <td id=\"T_be260_row8_col8\" class=\"data row8 col8\" >1654.07</td>\n",
       "      <td id=\"T_be260_row8_col9\" class=\"data row8 col9\" >1461.00</td>\n",
       "      <td id=\"T_be260_row8_col10\" class=\"data row8 col10\" >-0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be260_level0_row9\" class=\"row_heading level0 row9\" >234</th>\n",
       "      <td id=\"T_be260_row9_col0\" class=\"data row9 col0\" >-0.04</td>\n",
       "      <td id=\"T_be260_row9_col1\" class=\"data row9 col1\" >0.56</td>\n",
       "      <td id=\"T_be260_row9_col2\" class=\"data row9 col2\" >0.30</td>\n",
       "      <td id=\"T_be260_row9_col3\" class=\"data row9 col3\" >9.32</td>\n",
       "      <td id=\"T_be260_row9_col4\" class=\"data row9 col4\" >0.50</td>\n",
       "      <td id=\"T_be260_row9_col5\" class=\"data row9 col5\" >38.00</td>\n",
       "      <td id=\"T_be260_row9_col6\" class=\"data row9 col6\" >2059.00</td>\n",
       "      <td id=\"T_be260_row9_col7\" class=\"data row9 col7\" >5125.00</td>\n",
       "      <td id=\"T_be260_row9_col8\" class=\"data row9 col8\" >1915.00</td>\n",
       "      <td id=\"T_be260_row9_col9\" class=\"data row9 col9\" >1428.56</td>\n",
       "      <td id=\"T_be260_row9_col10\" class=\"data row9 col10\" >0.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x758e315f6ad0>"
      ]
     },
     "execution_count": 30,
     "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": 31,
   "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": 32,
   "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator = MMRegression().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": 33,
   "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.outlier_map(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": 34,
   "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>Make</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": [
       "              Make          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": 34,
     "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, ['Make', 'Model', 'Price']\n",
    "].assign(predicted_price=price_transformer.inverse_transform(estimator.predict(X.loc[bad_leverage_idx])).round())"
   ]
  }
 ],
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