ch05-outliers/Recipe-4-Removing-outliers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Removing outliers - outlier trimming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from feature_engine.outliers import OutlierTrimmer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((14448, 8), (6192, 8))"
      ]
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the California House price data from Scikit-learn\n",
    "X, y = fetch_california_housing(return_X_y=True, as_frame=True)\n",
    "\n",
    "# let's separate the data into training and testing sets\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X,\n",
    "    y,\n",
    "    test_size=0.3,\n",
    "    random_state=0,\n",
    ")\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "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>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>1.9750</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>193.0</td>\n",
       "      <td>4.825000</td>\n",
       "      <td>36.73</td>\n",
       "      <td>-119.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>2.2604</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3.671480</td>\n",
       "      <td>1.184116</td>\n",
       "      <td>836.0</td>\n",
       "      <td>3.018051</td>\n",
       "      <td>37.77</td>\n",
       "      <td>-122.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7887</th>\n",
       "      <td>6.2990</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.478022</td>\n",
       "      <td>1.087912</td>\n",
       "      <td>1387.0</td>\n",
       "      <td>3.810440</td>\n",
       "      <td>33.87</td>\n",
       "      <td>-118.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4581</th>\n",
       "      <td>1.7199</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.518000</td>\n",
       "      <td>1.196000</td>\n",
       "      <td>3051.0</td>\n",
       "      <td>3.051000</td>\n",
       "      <td>34.06</td>\n",
       "      <td>-118.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>2.2206</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4.622754</td>\n",
       "      <td>1.161677</td>\n",
       "      <td>606.0</td>\n",
       "      <td>3.628743</td>\n",
       "      <td>36.73</td>\n",
       "      <td>-119.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "1989  1.9750      52.0  2.800000   0.700000       193.0  4.825000     36.73   \n",
       "256   2.2604      43.0  3.671480   1.184116       836.0  3.018051     37.77   \n",
       "7887  6.2990      17.0  6.478022   1.087912      1387.0  3.810440     33.87   \n",
       "4581  1.7199      17.0  2.518000   1.196000      3051.0  3.051000     34.06   \n",
       "1993  2.2206      50.0  4.622754   1.161677       606.0  3.628743     36.73   \n",
       "\n",
       "      Longitude  \n",
       "1989    -119.79  \n",
       "256     -122.21  \n",
       "7887    -118.04  \n",
       "4581    -118.28  \n",
       "1993    -119.81  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_limits(df, variable, fold):\n",
    "\n",
    "    IQR = df[variable].quantile(0.75) - df[variable].quantile(0.25)\n",
    "\n",
    "    lower_limit = df[variable].quantile(0.25) - (IQR * fold)\n",
    "    upper_limit = df[variable].quantile(0.75) + (IQR * fold)\n",
    "\n",
    "    return lower_limit, upper_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-3.925900000000002, 11.232600000000001)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we find the limits\n",
    "\n",
    "lower_limit, upper_limit = find_limits(X_train, \"MedInc\", 3)\n",
    "lower_limit, upper_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove outliers on the right\n",
    "\n",
    "inliers = X_train[\"MedInc\"].ge(lower_limit)\n",
    "X_train = X_train.loc[inliers]\n",
    "\n",
    "inliers = X_test[\"MedInc\"].ge(lower_limit)\n",
    "X_test = X_test.loc[inliers]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove outliers on the left\n",
    "\n",
    "inliers = X_train[\"MedInc\"].le(upper_limit)\n",
    "X_train = X_train.loc[inliers]\n",
    "\n",
    "inliers = X_test[\"MedInc\"].le(upper_limit)\n",
    "X_test = X_test.loc[inliers]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature-engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((14448, 8), (6192, 8))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's separate the data into training and testing sets\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X,\n",
    "    y,\n",
    "    test_size=0.3,\n",
    "    random_state=0,\n",
    ")\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "               variables=[&#x27;MedInc&#x27;, &#x27;HouseAge&#x27;, &#x27;Population&#x27;])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OutlierTrimmer</label><div class=\"sk-toggleable__content\"><pre>OutlierTrimmer(capping_method=&#x27;iqr&#x27;, fold=1.5, tail=&#x27;both&#x27;,\n",
       "               variables=[&#x27;MedInc&#x27;, &#x27;HouseAge&#x27;, &#x27;Population&#x27;])</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "OutlierTrimmer(capping_method='iqr', fold=1.5, tail='both',\n",
       "               variables=['MedInc', 'HouseAge', 'Population'])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trimmer = OutlierTrimmer(\n",
    "    variables=[\"MedInc\", \"HouseAge\", \"Population\"],\n",
    "    capping_method=\"iqr\",\n",
    "    tail=\"both\",\n",
    "    fold=1.5,\n",
    ")\n",
    "\n",
    "trimmer.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'MedInc': -0.6776500000000012, 'HouseAge': -10.5, 'Population': -626.0}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trimmer.left_tail_caps_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'MedInc': 7.984350000000001, 'HouseAge': 65.5, 'Population': 3134.0}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trimmer.right_tail_caps_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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     "output_type": "stream",
     "text": [
      "(14448, 8) (6192, 8)\n",
      "(13165, 8) (5619, 8)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, X_test.shape)\n",
    "\n",
    "X_train = trimmer.transform(X_train)\n",
    "X_test = trimmer.transform(X_test)\n",
    "\n",
    "print(X_train.shape, X_test.shape)"
   ]
  }
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