notebooks/mod_05_topic_10_feature_engineering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Поширені прийоми генерації ознак. Математичні перетворення"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.feature_selection import mutual_info_regression\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../datasets/mod_05_topic_10_various_data.pkl', 'rb') as fl:\n",
    "    datasets = pickle.load(fl)"
   ]
  },
  {
   "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>stroke</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>0.772334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>0.772334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>1.294776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.40</td>\n",
       "      <td>3.19</td>\n",
       "      <td>1.065831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.40</td>\n",
       "      <td>3.19</td>\n",
       "      <td>1.065831</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   stroke  bore  stroke_ratio\n",
       "0    2.68  3.47      0.772334\n",
       "1    2.68  3.47      0.772334\n",
       "2    3.47  2.68      1.294776\n",
       "3    3.40  3.19      1.065831\n",
       "4    3.40  3.19      1.065831"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos = datasets['autos']\n",
    "\n",
    "autos['stroke_ratio'] = autos['stroke'] / autos['bore']\n",
    "\n",
    "autos[['stroke', 'bore', 'stroke_ratio']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='LogWindSpeed', ylabel='Density'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accidents = datasets['accidents']\n",
    "\n",
    "accidents['LogWindSpeed'] = accidents['WindSpeed'].apply(np.log1p)\n",
    "\n",
    "sns.set_theme()\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=(8, 4))\n",
    "sns.kdeplot(accidents.WindSpeed, fill=True, ax=axs[0])\n",
    "sns.kdeplot(accidents.LogWindSpeed, fill=True, ax=axs[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Amenity</th>\n",
       "      <th>Bump</th>\n",
       "      <th>Crossing</th>\n",
       "      <th>GiveWay</th>\n",
       "      <th>Junction</th>\n",
       "      <th>NoExit</th>\n",
       "      <th>Railway</th>\n",
       "      <th>Roundabout</th>\n",
       "      <th>Station</th>\n",
       "      <th>Stop</th>\n",
       "      <th>TrafficCalming</th>\n",
       "      <th>TrafficSignal</th>\n",
       "      <th>RoadwayFeatures</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Amenity   Bump  Crossing  ...  TrafficCalming  TrafficSignal  RoadwayFeatures\n",
       "0    False  False     False  ...           False          False                0\n",
       "1    False  False     False  ...           False          False                0\n",
       "2    False  False     False  ...           False          False                0\n",
       "3    False  False     False  ...           False          False                0\n",
       "4    False  False     False  ...           False          False                0\n",
       "5    False  False     False  ...           False          False                1\n",
       "6    False  False     False  ...           False          False                0\n",
       "7    False  False      True  ...           False           True                2\n",
       "8    False  False      True  ...           False           True                2\n",
       "9    False  False     False  ...           False          False                0\n",
       "\n",
       "[10 rows x 13 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roadway_features = ['Amenity',\n",
    "                    'Bump',\n",
    "                    'Crossing',\n",
    "                    'GiveWay',\n",
    "                    'Junction',\n",
    "                    'NoExit',\n",
    "                    'Railway',\n",
    "                    'Roundabout',\n",
    "                    'Station',\n",
    "                    'Stop',\n",
    "                    'TrafficCalming',\n",
    "                    'TrafficSignal']\n",
    "\n",
    "accidents['RoadwayFeatures'] = accidents[roadway_features].sum(axis=1)\n",
    "\n",
    "accidents[roadway_features + ['RoadwayFeatures']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Прийоми генерації ознак. Підрахунок кількості"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Cement</th>\n",
       "      <th>BlastFurnaceSlag</th>\n",
       "      <th>FlyAsh</th>\n",
       "      <th>Water</th>\n",
       "      <th>Superplasticizer</th>\n",
       "      <th>CoarseAggregate</th>\n",
       "      <th>FineAggregate</th>\n",
       "      <th>Components</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>102.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>887.0</td>\n",
       "      <td>942.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>108.3</td>\n",
       "      <td>162.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>203.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>938.2</td>\n",
       "      <td>849.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>116.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>909.8</td>\n",
       "      <td>891.9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>122.6</td>\n",
       "      <td>183.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>203.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>958.2</td>\n",
       "      <td>800.1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>132.0</td>\n",
       "      <td>206.5</td>\n",
       "      <td>160.9</td>\n",
       "      <td>178.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>866.9</td>\n",
       "      <td>735.6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>132.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>867.0</td>\n",
       "      <td>736.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>133.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>927.4</td>\n",
       "      <td>839.2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>133.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>949.0</td>\n",
       "      <td>795.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>133.1</td>\n",
       "      <td>210.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>195.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>949.4</td>\n",
       "      <td>795.3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>134.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>165.7</td>\n",
       "      <td>180.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>961.0</td>\n",
       "      <td>804.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cement  BlastFurnaceSlag  FlyAsh  ...  CoarseAggregate  FineAggregate  Components\n",
       "0   102.0             153.0     0.0  ...            887.0          942.0           5\n",
       "1   108.3             162.4     0.0  ...            938.2          849.0           5\n",
       "2   116.0             173.0     0.0  ...            909.8          891.9           5\n",
       "3   122.6             183.9     0.0  ...            958.2          800.1           5\n",
       "4   132.0             206.5   160.9  ...            866.9          735.6           7\n",
       "5   132.0             207.0   161.0  ...            867.0          736.0           7\n",
       "6   133.0             200.0     0.0  ...            927.4          839.2           5\n",
       "7   133.0             210.0     0.0  ...            949.0          795.0           6\n",
       "8   133.1             210.2     0.0  ...            949.4          795.3           6\n",
       "9   134.7               0.0   165.7  ...            961.0          804.9           6\n",
       "\n",
       "[10 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concrete = datasets['concrete']\n",
    "\n",
    "components = ['Cement',\n",
    "              'BlastFurnaceSlag',\n",
    "              'FlyAsh',\n",
    "              'Water',\n",
    "              'Superplasticizer',\n",
    "              'CoarseAggregate',\n",
    "              'FineAggregate']\n",
    "\n",
    "concrete['Components'] = concrete[components].gt(0).sum(axis=1)\n",
    "\n",
    "concrete[components + ['Components']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Прийоми генерації ознак. Розділення й об'єднання текстових ознак"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Policy</th>\n",
       "      <th>Type</th>\n",
       "      <th>Level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Corporate L3</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>L3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Personal</td>\n",
       "      <td>L3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Personal</td>\n",
       "      <td>L3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Corporate L2</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>L2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Personal L1</td>\n",
       "      <td>Personal</td>\n",
       "      <td>L1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Personal</td>\n",
       "      <td>L3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Corporate L3</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>L3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Corporate L3</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>L3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Corporate L3</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>L3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Special L2</td>\n",
       "      <td>Special</td>\n",
       "      <td>L2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Policy       Type Level\n",
       "0  Corporate L3  Corporate    L3\n",
       "1   Personal L3   Personal    L3\n",
       "2   Personal L3   Personal    L3\n",
       "3  Corporate L2  Corporate    L2\n",
       "4   Personal L1   Personal    L1\n",
       "5   Personal L3   Personal    L3\n",
       "6  Corporate L3  Corporate    L3\n",
       "7  Corporate L3  Corporate    L3\n",
       "8  Corporate L3  Corporate    L3\n",
       "9    Special L2    Special    L2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer = datasets['customer']\n",
    "\n",
    "customer[['Type', 'Level']] = (\n",
    "    customer['Policy']\n",
    "    .str\n",
    "    .split(' ', expand=True))\n",
    "\n",
    "customer[['Policy', 'Type', 'Level']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>make</th>\n",
       "      <th>body_style</th>\n",
       "      <th>make_and_style</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>convertible</td>\n",
       "      <td>alfa-romero_convertible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>convertible</td>\n",
       "      <td>alfa-romero_convertible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>alfa-romero_hatchback</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>audi</td>\n",
       "      <td>sedan</td>\n",
       "      <td>audi_sedan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>audi</td>\n",
       "      <td>sedan</td>\n",
       "      <td>audi_sedan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          make   body_style           make_and_style\n",
       "0  alfa-romero  convertible  alfa-romero_convertible\n",
       "1  alfa-romero  convertible  alfa-romero_convertible\n",
       "2  alfa-romero    hatchback    alfa-romero_hatchback\n",
       "3         audi        sedan               audi_sedan\n",
       "4         audi        sedan               audi_sedan"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autos['make_and_style'] = autos['make'] + '_' + autos['body_style']\n",
    "autos[['make', 'body_style', 'make_and_style']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Прийоми генерації ознак. Групові перетворення"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>State</th>\n",
       "      <th>Income</th>\n",
       "      <th>AverageIncome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Washington</td>\n",
       "      <td>56274</td>\n",
       "      <td>38122.733083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>0</td>\n",
       "      <td>37405.402231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>48767</td>\n",
       "      <td>38369.605442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>California</td>\n",
       "      <td>0</td>\n",
       "      <td>37558.946667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Washington</td>\n",
       "      <td>43836</td>\n",
       "      <td>38122.733083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>62902</td>\n",
       "      <td>37557.283353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>55350</td>\n",
       "      <td>37557.283353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>0</td>\n",
       "      <td>37405.402231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>14072</td>\n",
       "      <td>37557.283353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>28812</td>\n",
       "      <td>37557.283353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State  Income  AverageIncome\n",
       "0  Washington   56274   38122.733083\n",
       "1     Arizona       0   37405.402231\n",
       "2      Nevada   48767   38369.605442\n",
       "3  California       0   37558.946667\n",
       "4  Washington   43836   38122.733083\n",
       "5      Oregon   62902   37557.283353\n",
       "6      Oregon   55350   37557.283353\n",
       "7     Arizona       0   37405.402231\n",
       "8      Oregon   14072   37557.283353\n",
       "9      Oregon   28812   37557.283353"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer['AverageIncome'] = (customer\n",
    "                             .groupby('State')['Income']\n",
    "                             .transform('mean'))\n",
    "\n",
    "customer[['State', 'Income', 'AverageIncome']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>State</th>\n",
       "      <th>StateFreq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Washington</td>\n",
       "      <td>0.087366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.186446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>0.096562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>California</td>\n",
       "      <td>0.344865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Washington</td>\n",
       "      <td>0.087366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>0.284760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>0.284760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.186446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>0.284760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>0.284760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State  StateFreq\n",
       "0  Washington   0.087366\n",
       "1     Arizona   0.186446\n",
       "2      Nevada   0.096562\n",
       "3  California   0.344865\n",
       "4  Washington   0.087366\n",
       "5      Oregon   0.284760\n",
       "6      Oregon   0.284760\n",
       "7     Arizona   0.186446\n",
       "8      Oregon   0.284760\n",
       "9      Oregon   0.284760"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer = (customer\n",
    "            .assign(StateFreq=lambda x:\n",
    "                    x.groupby('State')['State']\n",
    "                    .transform('count') /\n",
    "                    x['State'].count()))\n",
    "\n",
    "customer[['State', 'StateFreq']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Coverage</th>\n",
       "      <th>AverageClaim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Basic</td>\n",
       "      <td>377.874433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Basic</td>\n",
       "      <td>377.874433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Premium</td>\n",
       "      <td>647.557320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Basic</td>\n",
       "      <td>377.874433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Basic</td>\n",
       "      <td>377.874433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Premium</td>\n",
       "      <td>647.557320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Basic</td>\n",
       "      <td>377.874433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Basic</td>\n",
       "      <td>377.874433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Basic</td>\n",
       "      <td>377.874433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Premium</td>\n",
       "      <td>647.557320</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Coverage  AverageClaim\n",
       "0    Basic    377.874433\n",
       "1    Basic    377.874433\n",
       "2  Premium    647.557320\n",
       "3    Basic    377.874433\n",
       "4    Basic    377.874433\n",
       "5  Premium    647.557320\n",
       "6    Basic    377.874433\n",
       "7    Basic    377.874433\n",
       "8    Basic    377.874433\n",
       "9  Premium    647.557320"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_train = customer.sample(frac=0.75)\n",
    "c_test = customer.drop(c_train.index)\n",
    "\n",
    "c_train['AverageClaim'] = (c_train\n",
    "                           .groupby('Coverage')['ClaimAmount']\n",
    "                           .transform('mean'))\n",
    "\n",
    "c_test = c_test.merge(\n",
    "    c_train[['Coverage', 'AverageClaim']].drop_duplicates(),\n",
    "    on='Coverage',\n",
    "    how='left')\n",
    "\n",
    "c_test[['Coverage', 'AverageClaim']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Відбір ознак. Взаємна інформація між ознаками й цільовою змінною"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T08pxKzwbTtp3aHwMdg+aP7cSa4GSGtmQWBQt2eIfIdHq13Q79O3Wv4CmC/tErhD2xcozTMebTlCmW5vHviK9hCXtB5eIL/+gFJS8cmUuIYS9JMSImZlS09F9KTrtcuVaKe47XsRg96j5cytdczAmWv3SHBv6g0Op163VNCTPKiY+KGjC7t9PLJUstPh3IJnYKr7GSWJFQfvR7H8nCzGCFQ/0RHhnG86Jq3M5kZv2HbdisJPrpV63FhidwtaI/xO5AYPd5TiZhAqF+5pzcBEwF0tMJrk0ZKRMJvnH+Z5iF41chvuaszDYXWzkZBIhBAI++2IJf3rv38UuGrkM9zVnYbC7WE//MPx66kvMySSUD9zXnIXB7mKVs4MwLJnyM04moXzgvuYsDHYX+8rCa2BZ9ip9Sqn4an2cTEK5x33NWRjsLjZ/biXW3nYdrprhR3TYxFUz/Fh723UcqUA5x33NWTjc0eU4mYQKhfuac7DFTkTkMmyxuwQnh5BTcd8sPLbYXYCTQ8ipuG8WB4PdBTg5hJyK+2ZxMNhdgJNDyKm4bxYHg90FODmEnIr7ZnEw2F2Ak0PIqbhvFkfJjooxLYnO7svo6Y869oIE+SA1DX390ZSf1VTOwJ23fAZvn+5A32AM5TMD+I+Ga1BTOQM9o+5bitLV2e3cUuds9k231HmyhBDJyx/m/LGVKn4sXrw4ACknXwzTktj14lvo7B3KY6mIiPKrriqE//Xgf8CnZ9d5omkCVVWzxt8+3YIREZGzTKrFfuTIEbzwwgswTRNbt27F5s2bU7afO3cOu3fvRl9fH8LhMJ577jmUl5dPuhDZttgBu9VuCQ0fftztqa6Y8vIQ+vouF7sYBcU6e4PX6iyEwK2fvwYDl7LvecjUYs/Yx97R0YG9e/filVdeQSAQwH333YfFixfjhhtuAGBfS/Kb3/wmmpubsWzZMjz77LNoaWnBY489lnVhs+HTNVRVhNDbd9kzwf6P8z049Jf/h0j3oKdm8M2pmAFN5qcv0qm8UueRs1LDc2biSzfXeGKfBgBNiKy7YCb92JnucPz4cSxZsgQVFRUIhUJobGxEa2trcvupU6cQCoWwbNkyAMD27dvHtOhp+hIz+PoGopzBR64welZq30CU+3SOZAz2zs5OhMPh5O2amhp0dHQkb58/fx7V1dXYuXMn1q1bh927dyMUCuWntB6WmMEX9HMGH7nD6FmpQT/36VzJ2BUjpYQQInlbKZVy2zRNvP322/jtb3+L+vp6PP/883jqqafw1FNPTboQE/UVTSRmWJhTORNe6InpvWxgZlAHAPh0+/nXNR19lw3MmTOzmEUrCC/UcTS31zmxT4/MkxkB7+zTiWqHw7Nz/tgZg72urg4nT55M3o5EIqipqUneDofDmDdvHurr6wEAq1evRlNTU1aFmMrJUwAorwihu2fQE33sFSE/Lg0ZCAV9MC27wjHTQnnIj+7uwSKXLr/mzJnp+jqO5oU6J/bpgO9Kg2Uo5o19GrD72MMVIUQi/dn/7nSHOy5duhQnTpxAd3c3hoaG0NbWluxPB4BbbrkF3d3deP/99wEAb775JhYsWJB1QWliiRl8wwZn8JE7jJ6VOmxwn86VjC322tpa7NixA1u2bIFhGNi4cSMaGhqwbds2NDU1ob6+Hr/85S+xa9cuDA0Noa6uDk8//XQhyu4p8+dWYi2AE3/v9NyoGHKnxD7t1VEx+VSSM08TyitCOPNhlye6YhK88BF9NNbZG7xWZ00I3PjfqtHbk32dOfOUiMhjGOxERC7DYCcicpmSXbbXzXjxX/I6HgPTwxa7w/Div+R1PAamj8HuMLz4L3kdj4HpY1eMw/T0D6MsmPqy8OK/k5eYpj1ymvqY+8TvJzQB+7/090n8Q0BAJRauGGdorRq1aWSLSQJQSkIp2F+wl+YA4KmhupPFY2D6GOwOUzk7mDLNGvDexX9F8n9XbmtCAALQNMCnadCEsINZ2CGuawIaBIQGaPGfCQhAqGR4ivgDC1x5A8hkZPBO9ncmIpUd6vZX/DYUpFSQCvZ3SyHg0xHw65BKQklvvRnwGJg+BrvDfGXhNTj8lw8Rg91KMSxZ8tOsRbzVK4Q9scKnadB0cSWAR35X8QAXV343sTa5Hg/y7IJNpA3kqYRjLgLVflMRGd8lwpUzICwz+XflqDcDYMQbgVJ2+Cv731Ih5Q3BfozSeVNw4zFQaAx2hxk9zdrJIwKSgQxA0wV0CGia3ZLWNDu47e/219SCGQj4dejxpC+FYMqVkXXVJvFmkDDyblLanwhGf0JQCrCkSn5CMJWEtFS8u0gVdcXUUjoGnIrB7kDz51Y6Yice2V+tCbsLRPdp0HUBnxDxVrf9lbhvpuD1UjAXy+juI7tLauI3hcQbrlQKpqVgSWl/NyVMKUd0IeW58HFOOQZKFYPdo64c8PF+a6FB1+0uktH91YlWNzB+MDOwS1vi9dOEQMAnkDj9m3g/SLbupYIpFQxDwrCsggc+TQ6D3YUSJx9TWtqjQlvTEW9pi2R/9kQHJw9cbxoZ+JouAB0IAEDQDv1k4Me7eABAKEAJwLQUTEPCkJbdJSSL28XjJQz2Ipru7LpEH7emAQFdh8+njejTBnQ9c0s70zai8Sg1IvDTCPqQ8gZgWgqmlDBiEoZpwZpCS58zUieHwV4kidl1uq6lzK5bC7t/UYxocWuaBl/8pOSsMj+sWQHoQoOmw26BC8EuEnKsxBtAwCcQgBZv8gOWJWFJwFISllQo88eHeMb79DHiU4BSmY8ZuoLBXiT/99QFzJzhR1nAB13T4PMJSAX846NefGnB1dA0u2979GiQ8tlBxKKxlMdieFMp0nUNug4k+vOrKmZAmfYQz2TfvVT2BC8JnPmoF1dXz4IAEDMlTEvictTAn977N4N9FAZ7niXGLWsa4Nd1+Pwa/Lod4uUzA8mxyMMxe6xupPsyAv4r8xYZ2uQlyclkiI/31wV0ANCBf37ch1khP/w+HT4NKAv4cFXIj5ghMbPMj5hlwbIUT+aCwZ5TI2dB+n0afD4NPk2DL37ScuTOpmsCFy8NI+i/Mrtu2LBQXV5WhJITOV91eRl6B2NQCogBwLCFYcNC5cwAZof8EMIPpQDTkjAsCcOwr6fqxZE7DPYpSoa4bi9S5PfZ/eCJE5ajd6LRt1cunot9r53BMICAT0PMtFvsKxfPLVgdiErJeMdMY/yYSRxjPl2DT9cwI2Afp1IpWNL+ihkSMcOEJd0d9gz2SbA/EQr7I6BPQNezC/F0Gq6vBgC0vnUeXX1RVJeXYeXiucmfE1GqqRwzStnzNXyagE8Dgj4dIuRPBr09WkfGx+VL14Q9g32UREvcp2vw++Jfmh3m6UxnJ2i4vppBTpSFXBwzI4dp+nUAfh1ihn3C1rIkZHykjmkpmPGTtIlZuaXC08GuJcaA++yTmj6hQdMAXbO/l9DrSETTYLfs7W4cjBipk1hqwZISlgWYUsI0JWKWsyddeSbYhbCDPNkK1+01T3Qt9y1xInKHRA7omgZdgz0OP82kK9NwVti7NthFfOZl0O8bcWJTgzaF1QWJiEZKN+lq3LC3Cr9ipmuCPdEiD/h0BIP6uP3iDHUiyodswz4XF24ZT8kHezBgT0P2x4c45fG5mrL2D7o4+oUoj5x6jGUKe03LT2KVdLAH/DoqZwUd3Qpv/6AL+147A13XECrzoXcwhn2vnQEAR+x4RKWu1I6xkWGv5ynY0585LCFODnXAHnOr6xqCfvuK60G/fcX11rfOF7toRK7AY2yskg92p+vqiyLgS32aAz4NXX3RIpWIyF14jI3FYM+z6vIyxEyZ8rOYKbkmDFGO8Bgbi8GeZysXz4VlSQwbFpRSGDYsrglDlEM8xsaaVLAfOXIEq1atwooVK7Bv375x73fs2DHceeedOSucGzRcX43Ny29ExcwALkdNVMwMYPPyGx15UoeoFPEYGyvjqJiOjg7s3bsXr7zyCgKBAO677z4sXrwYN9xwQ8r9urq68NOf/jRvBS1lXBOGKL94jKXK2GI/fvw4lixZgoqKCoRCITQ2NqK1tXXM/Xbt2oVHH300L4UkIqLJy9hi7+zsRDgcTt6uqalBe3t7yn1efvll3HzzzVi4cOGUClFVNWtKvwcA4fDsKf9uqWKdvYF19oZ81DljsEsp7UtUxSmlUm6fOXMGbW1teOmll3DhwoUpFeLixQFImf2A9HB4NiKR/in9zVLFOnsD6+wNU62zpokJG8QZg72urg4nT55M3o5EIqipqUnebm1tRSQSwYYNG2AYBjo7O7Fp0ybs378/68KWOqdOaybyKq8ekxn72JcuXYoTJ06gu7sbQ0NDaGtrw7Jly5Lbm5qacPToURw6dAgtLS2oqanxbKjve+0MegdjKdOa2z/oKnbRiDzJy8dkxmCvra3Fjh07sGXLFnzta1/D6tWr0dDQgG3btuFvf/tbIcpYEjitmchZvHxMTmoRsDVr1mDNmjUpP3vxxRfH3O8zn/kM3nzzzdyUrMR09UURKkt9Or0+rZmomLx8THLmaY5wWjORs3j5mGSw5winNRM5i5ePyZJej91JEmfavXgGnsiJvHxMMthziNOaiZzFq8cku2KIiFyGwU5E5DIMdiIil2GwExG5DE+eZsmra08QuY2bj2UGexYSa0/oupay9gQA1+wQRF7g9mOZXTFZ8PLaE0Ru4vZjmcGeha6+KAK+1KfMK2tPELmJ249lBnsWvLz2BJGbuP1YZrBnwctrTxC5iduPZZ48zYKX154gchO3H8sM9ix5de0JIrdx87HMrhgiIpdhsBMRuQyDnYjIZRjsREQuw5On43DzOhJEND43HPsM9jTcvo4EEaXnlmOfXTFpuH0dCSJKzy3HPoM9DbevI0FE6bnl2Gewp+H2dSSIKD23HPsM9jTcvo4EEaXnlmOfJ0/TcPs6EkSUnluOfQb7ONy8jgQRjc8Nxz67YoiIXIbBTkTkMpMK9iNHjmDVqlVYsWIF9u3bN2b766+/jnvuuQdr167FI488gr6+vpwXlIiIJidjsHd0dGDv3r3Yv38/Xn31VRw4cABnz55Nbh8YGMAPfvADtLS04PDhw5g/fz5+8Ytf5LXQREQ0vozBfvz4cSxZsgQVFRUIhUJobGxEa2trcrthGNi9ezdqa2sBAPPnz8enn36avxITEdGEMo6K6ezsRDgcTt6uqalBe3t78nZlZSWWL18OAIhGo2hpacEDDzyQh6LmhxsW/CGi/CulrMgY7FJKCCGSt5VSKbcT+vv78a1vfQs33XQT1q1bl1UhqqpmZXX/kcLh2VP+3ZOnO/C7N87C5xMonxXAQNTA7944i/LyEG79XO2UHzffplPnUsU6e4NT65zPrMhHnTMGe11dHU6ePJm8HYlEUFNTk3Kfzs5OPPTQQ1iyZAl27tyZdSEuXhyAlCrr3wuHZyMS6c/69xIOtL0PCEDXNJiWsr8LCwfa3se86tCUHzefplvnUsQ6e4OT65yvrJhqnTVNTNggztjHvnTpUpw4cQLd3d0YGhpCW1sbli1bltxuWRa2b9+Ou+66C83NzWlb807llgV/iCi/Si0rMrbYa2trsWPHDmzZsgWGYWDjxo1oaGjAtm3b0NTUhAsXLuDvf/87LMvC0aNHAQCf//znsWfPnrwXfrqqy8vQOxhD0K8nf1aKC/4QUX6VWlYIpVT2fSA5VqyumJGL6gd8GmKmhGVJbF5+o2NPijj542q+sM7e4OQ65ysr8tUV4+m1Ytyy4A8R5VepZYWngx1wx4I/RJR/pZQVXCuGiMhlGOxERC7DYCcichkGOxGRy3jm5GkprfNARKXDidniiWAfOQY1VOZD72AM+147AwBFfwGIqHQ5NVs80RXT+tZ56LqGoF+HEAJBvw5d19D61vliF42ISphTs8UTwV5q6zwQUWlwarZ4Itiry8sQM2XKz5y8zgMRlQanZosngn3l4rmwLIlhw4JSCsOGBcuSWLl4brGLRkQlzKnZ4omTp6W2zgMRlQanZosngh0orXUeiKh0ODFbPNEVQ0TkJQx2IiKXYbATEbkMg52IyGUY7ERELuO6UTFOXJCHiLynmFnkqmB36oI8ROQtxc4iV3XFOHVBHiLylmJnkauC3akL8hCRtxQ7i1wV7E5dkIeIvKXYWeSqYHfqgjxE5C3FziJXnTx16oI8ROQtxc4iVwU74MwFeYjIe4qZRa7qiiEiIgY7EZHrMNiJiFyGwU5E5DKTOnl65MgRvPDCCzBNE1u3bsXmzZtTtp8+fRrNzc0YHBzErbfeih/+8Ifw+fJ3XjaxBkP3QAxzZgU48oWISkq+Myxji72jowN79+7F/v378eqrr+LAgQM4e/Zsyn0ee+wxPPHEEzh69CiUUjh48GDOCjhaYg2G3sEYZs+4sgZD+wddefubRES5UogMyxjsx48fx5IlS1BRUYFQKITGxka0trYmt3/yySeIRqNYtGgRAGD9+vUp23Ot2GswEBFNRyEyLGN/SWdnJ8LhcPJ2TU0N2tvbx90eDofR0dGRVSGqqmZN+r7dA/a7nBACAOD3afDpAj0DMYTDs7P6u6XKK/UciXX2Bi/UuRAZljHYpZTJAgCAUirldqbtk3Hx4gCkVJO675xZAfQOxhD06/D7NBimPW23clYAkUh/Vn+3FIXDsz1Rz5FYZ2/wSp1zkWGaJiZsEGfsiqmrq0MkEknejkQiqKmpGXd7V1dXyvZcK/YaDERE01GIDMsY7EuXLsWJEyfQ3d2NoaEhtLW1YdmyZcnt1157LYLBIN555x0AwKFDh1K251rD9dXYvPxGVMwMYGDIRMXMADYvv5GjYoioJBQiwzJ2xdTW1mLHjh3YsmULDMPAxo0b0dDQgG3btqGpqQn19fV49tlnsWvXLgwMDGDBggXYsmVLzgqYTmINBq98dCMid8l3hgml1OQ6t/Momz72kbwY7KyzN7DO3jDVOk+7j52IiEoLg52IyGUY7ERELuOIC21oWnbj3nP1u6WKdfYG1tkbplLnTL/jiJOnRESUO+yKISJyGQY7EZHLMNiJiFyGwU5E5DIMdiIil2GwExG5DIOdiMhlGOxERC7DYCcicpmSCPYjR45g1apVWLFiBfbt2zdm++nTp7F+/Xo0NjaiubkZpmkWoZS5lanOr7/+Ou655x6sXbsWjzzyCPr6+opQytzKVOeEY8eO4c477yxgyfInU53PnTuHBx54AGvXrsVDDz3kidf51KlT2LBhA9auXYtvfOMbuHTpUhFKmVsDAwNYvXo1Pv744zHb8pJfyuEuXLig7rjjDtXT06MGBwfVmjVr1D//+c+U+9x9993qr3/9q1JKqe9///tq3759RShp7mSqc39/v7rtttvUhQsXlFJKPf/88+rJJ58sVnFzYjKvs1JKRSIRtXLlSnXHHXcUoZS5lanOUkq1YsUK9cc//lEppdQzzzyjnn766WIVNycm8zrff//96tixY0oppX7yk5+o5557rhhFzZl3331XrV69Wi1YsEB99NFHY7bnI78c32I/fvw4lixZgoqKCoRCITQ2NqK1tTW5/ZNPPkE0GsWiRYsAAOvXr0/ZXooy1dkwDOzevRu1tbUAgPnz5+PTTz8tVnFzIlOdE3bt2oVHH320CCXMvUx1PnXqFEKhUPJSk9u3b8fmzZuLVdycmMzrLKXE4OAgAGBoaAhlZWXFKGrOHDx4ELt37057Leh85Zfjg72zsxPhcDh5u6amBh0dHeNuD4fDKdtLUaY6V1ZWYvny5QCAaDSKlpYWfPWrXy14OXMpU50B4OWXX8bNN9+MhQsXFrp4eZGpzufPn0d1dTV27tyJdevWYffu3QiFQsUoas5M5nX+3ve+h127duHLX/4yjh8/jvvuu6/QxcypPXv24NZbb027LV/55fhgl1JCiCtLVCqlUm5n2l6KJlun/v5+PPzww7jpppuwbt26QhYx5zLV+cyZM2hra8MjjzxSjOLlRaY6m6aJt99+G/fffz/+8Ic/4LOf/SyeeuqpYhQ1ZzLVORqNorm5GS+99BL+/Oc/Y9OmTXj88ceLUdSCyFd+OT7Y6+rqEIlEkrcjkUjKR5rR27u6utJ+5CklmeoM2O/0mzZtwvz587Fnz55CFzHnMtW5tbUVkUgEGzZswMMPP5ysfynLVOdwOIx58+ahvr4eALB69Wq0t7cXvJy5lKnOZ86cQTAYRENDAwDg3nvvxdtvv13wchZKvvLL8cG+dOlSnDhxAt3d3RgaGkJbW1uyzxEArr32WgSDQbzzzjsAgEOHDqVsL0WZ6mxZFrZv34677roLzc3NJf8JBchc56amJhw9ehSHDh1CS0sLampqsH///iKWePoy1fmWW25Bd3c33n//fQDAm2++iQULFhSruDmRqc7z5s3DhQsXcO7cOQDAG2+8kXxjc6O85de0T78WwOHDh9Xdd9+tVqxYoVpaWpRSSn39619X7e3tSimlTp8+rTZs2KAaGxvVd77zHTU8PFzM4ubERHVua2tT8+fPV2vXrk1+7dy5s8glnr5Mr3PCRx995IpRMUplrvO7776rNmzYoFatWqUefPBB1dXVVczi5kSmOh87dkytWbNGrV69Wm3dulWdP3++mMXNmTvuuCM5Kibf+cUrKBERuYzju2KIiCg7DHYiIpdhsBMRuQyDnYjIZRjsREQuw2AnInIZBjsRkcsw2ImIXOb/A48oNAT906IVAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 1, 50)\n",
    "y = np.sin(2 * np.pi * 0.5 * x)\n",
    "\n",
    "sns.regplot(x=x, y=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MI score: 1.17 | Cor index: 0.00\n"
     ]
    }
   ],
   "source": [
    "mis = mutual_info_regression(x.reshape(-1, 1), y)[0]\n",
    "cor = np.corrcoef(x, y)[0, 1]\n",
    "\n",
    "print(f'MI score: {mis:.2f} | Cor index: {cor:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = autos.copy()\n",
    "y = X.pop('price')\n",
    "\n",
    "cat_features = X.select_dtypes('object').columns\n",
    "\n",
    "for colname in cat_features:\n",
    "    X[colname], _ = X[colname].factorize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\install\\miniconda3\\envs\\env_mlf\\Lib\\site-packages\\joblib\\externals\\loky\\backend\\context.py:136: UserWarning: Could not find the number of physical cores for the following reason:\n",
      "found 0 physical cores < 1\n",
      "Returning the number of logical cores instead. You can silence this warning by setting LOKY_MAX_CPU_COUNT to the number of cores you want to use.\n",
      "  warnings.warn(\n",
      "  File \"d:\\install\\miniconda3\\envs\\env_mlf\\Lib\\site-packages\\joblib\\externals\\loky\\backend\\context.py\", line 282, in _count_physical_cores\n",
      "    raise ValueError(f\"found {cpu_count_physical} physical cores < 1\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "length            0.616900\n",
       "drive_wheels      0.332490\n",
       "make_and_style    0.510382\n",
       "body_style        0.080485\n",
       "wheel_base        0.578663\n",
       "Name: MI Scores, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mi_scores = mutual_info_regression(\n",
    "    X, y,\n",
    "    discrete_features=X.columns.isin(\n",
    "        cat_features.to_list() +\n",
    "        ['num_of_doors',\n",
    "         'num_of_cylinders']),\n",
    "    random_state=42)\n",
    "\n",
    "mi_scores = (pd.Series(\n",
    "    mi_scores,\n",
    "    name='MI Scores',\n",
    "    index=X.columns)\n",
    "    .sort_values())\n",
    "\n",
    "mi_scores.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Mutual Information Scores')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 8))\n",
    "plt.barh(np.arange(len(mi_scores)), mi_scores)\n",
    "plt.yticks(np.arange(len(mi_scores)), mi_scores.index)\n",
    "plt.title('Mutual Information Scores')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='curb_weight', ylabel='price'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(data=autos, x='curb_weight', y='price', order=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2c22011de10>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(data=autos,\n",
    "           x='horsepower',\n",
    "           y='price',\n",
    "           hue='fuel_type',\n",
    "           facet_kws={'legend_out': False})"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (Spyder)",
   "language": "python3",
   "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",
   "version": "3.11.9"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}