06.08-Weight-of-Evidence.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weight  of evidence\n",
    "\n",
    "Weight of Evidence (WoE) was developed primarily for the credit and financial industries to help build more predictive models to evaluate the risk of loan default. That is, to predict how likely the money lent to a person or institution is to be lost. Thus, Weight of Evidence is a measure of the \"strength” of a grouping technique to separate good and bad risk (default). \n",
    "\n",
    "- WoE will be 0 if the P(Goods) / P(Bads) = 1, that is, if the outcome is random for that group.\n",
    "- If P(Bads) > P(Goods) the odds ratio will be < 1 and,\n",
    "- WoE will be < 0 if,  P(Goods) > P(Bads).\n",
    "\n",
    "WoE is well suited for Logistic Regression, because the Logit transformation is simply the log of the odds, i.e., ln(P(Goods)/P(Bads)). Therefore, by using WoE-coded predictors in logistic regression, the predictors are all prepared and coded to the same scale, and the parameters in the linear logistic regression equation can be directly compared.\n",
    "\n",
    "The WoE transformation has three advantages:\n",
    "\n",
    "- It creates a monotonic relationship between the target and the independent variables.\n",
    "- It orders the categories on a \"logistic\" scale which is natural for logistic regression\n",
    "- The transformed variables can then be compared because they are on the same scale. Therefore, it is possible to determine which one is more predictive.\n",
    "\n",
    "The WoE also has a limitation:\n",
    "\n",
    "- Prone to cause over-fitting\n",
    "\n",
    "\n",
    "For more details follow this [link](http://documentation.statsoft.com/StatisticaHelp.aspx?path=WeightofEvidence/WeightofEvidenceWoEIntroductoryOverview)\n",
    "\n",
    "\n",
    "## In this demo:\n",
    "\n",
    "We will see how to perform one hot encoding with:\n",
    "- pandas\n",
    "- Feature-Engine\n",
    "\n",
    "And the advantages and limitations of each implementation using the Titanic dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# to split the datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# for encoding using feature-engine\n",
    "from feature_engine.categorical_encoders import WoERatioCategoricalEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>C22</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>C22</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>C22</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>C22</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived     sex cabin embarked\n",
       "0         1  female    B5        S\n",
       "1         1    male   C22        S\n",
       "2         0  female   C22        S\n",
       "3         0    male   C22        S\n",
       "4         0  female   C22        S"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load dataset\n",
    "\n",
    "data = pd.read_csv(\n",
    "    '../titanic.csv',\n",
    "    usecols=['cabin', 'sex', 'embarked', 'survived'])\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1307, 4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's remove observations with na in embarked\n",
    "\n",
    "data.dropna(subset=['embarked'], inplace=True)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we extract the first letter of the cabin\n",
    "# to create a simpler variable for the demo\n",
    "\n",
    "data['cabin'] = data['cabin'].astype(str).str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1306, 4)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and we remove the observations where cabin = T\n",
    "# because only few observations show T in the dataset\n",
    "\n",
    "data = data[data['cabin']!= 'T']\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survived :  2  labels\n",
      "sex :  2  labels\n",
      "cabin :  8  labels\n",
      "embarked :  3  labels\n"
     ]
    }
   ],
   "source": [
    "# let's have a look at how many labels each variable has\n",
    "\n",
    "for col in data.columns:\n",
    "    print(col, ': ', len(data[col].unique()), ' labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['B', 'C', 'E', 'D', 'A', 'n', 'F', 'G'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's explore the unique categories\n",
    "data['cabin'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['female', 'male'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['sex'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['S', 'C', 'Q'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['embarked'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoding important\n",
    "\n",
    "We select calculate the woe using the train set, and then use those mappings in the test set.\n",
    "\n",
    "Note that in the pandas implementation, we need to keep the target in the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((914, 4), (392, 4))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's separate into training and testing set\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data[['cabin', 'sex', 'embarked', 'survived']],  # this time we keep the target!!\n",
    "    data['survived'],  # target\n",
    "    test_size=0.3,  # percentage of obs in test set\n",
    "    random_state=0)  # seed to ensure reproducibility\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore original relationship between categorical variables and target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's explore the relationship of the categories with the target\n",
    "\n",
    "for var in ['cabin', 'sex', 'embarked']:\n",
    "    \n",
    "    fig = plt.figure()\n",
    "    fig = X_train.groupby([var])['survived'].mean().plot()\n",
    "    fig.set_title('Relationship between {} and Survival'.format(var))\n",
    "    fig.set_ylabel('Mean Survival')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that the relationship between the target and cabin and embarked goes up and down, depending on the category.\n",
    "\n",
    "\n",
    "## Weight of Evidence with pandas\n",
    "\n",
    "\n",
    "### Advantages\n",
    "\n",
    "- quick\n",
    "- returns pandas dataframe\n",
    "\n",
    "### Limitations of pandas:\n",
    "\n",
    "- it does not preserve information from train data to propagate to test data\n",
    "\n",
    "We need to store the mappings separately if planning to use the model in production."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cabin</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.411765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.738095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.696970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>0.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>0.292199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       survived\n",
       "cabin          \n",
       "A      0.411765\n",
       "B      0.738095\n",
       "C      0.600000\n",
       "D      0.696970\n",
       "E      0.700000\n",
       "F      0.769231\n",
       "G      0.750000\n",
       "n      0.292199"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's calculate the probability of survived = 1 per category\n",
    "# (probability of events or p(1))\n",
    "\n",
    "prob_df = X_train.groupby(['cabin'])['survived'].mean()\n",
    "\n",
    "# and capture it into a dataframe\n",
    "prob_df = pd.DataFrame(prob_df)\n",
    "prob_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>survived</th>\n",
       "      <th>died</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cabin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.411765</td>\n",
       "      <td>0.588235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.738095</td>\n",
       "      <td>0.261905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.696970</td>\n",
       "      <td>0.303030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>0.292199</td>\n",
       "      <td>0.707801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       survived      died\n",
       "cabin                    \n",
       "A      0.411765  0.588235\n",
       "B      0.738095  0.261905\n",
       "C      0.600000  0.400000\n",
       "D      0.696970  0.303030\n",
       "E      0.700000  0.300000\n",
       "F      0.769231  0.230769\n",
       "G      0.750000  0.250000\n",
       "n      0.292199  0.707801"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and now the probability of survived = 0\n",
    "# (probability of non-events or p(0))\n",
    "\n",
    "prob_df['died'] = 1-prob_df['survived']\n",
    "\n",
    "prob_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>survived</th>\n",
       "      <th>died</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cabin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.411765</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>-0.356675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.738095</td>\n",
       "      <td>0.261905</td>\n",
       "      <td>1.036092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.405465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.696970</td>\n",
       "      <td>0.303030</td>\n",
       "      <td>0.832909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.847298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>1.203973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>0.292199</td>\n",
       "      <td>0.707801</td>\n",
       "      <td>-0.884730</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       survived      died     ratio\n",
       "cabin                              \n",
       "A      0.411765  0.588235 -0.356675\n",
       "B      0.738095  0.261905  1.036092\n",
       "C      0.600000  0.400000  0.405465\n",
       "D      0.696970  0.303030  0.832909\n",
       "E      0.700000  0.300000  0.847298\n",
       "F      0.769231  0.230769  1.203973\n",
       "G      0.750000  0.250000  1.098612\n",
       "n      0.292199  0.707801 -0.884730"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  and now the woe\n",
    "\n",
    "prob_df['ratio'] = np.log( prob_df['survived'] / prob_df['died'] )\n",
    "\n",
    "prob_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': -0.35667494393873245,\n",
       " 'B': 1.0360919316867758,\n",
       " 'C': 0.4054651081081642,\n",
       " 'D': 0.8329091229351043,\n",
       " 'E': 0.8472978603872034,\n",
       " 'F': 1.2039728043259361,\n",
       " 'G': 1.0986122886681098,\n",
       " 'n': -0.8847299269619376}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and now let's capture the woe in a dictionary\n",
    "\n",
    "ordered_labels = prob_df['ratio'].to_dict()\n",
    "\n",
    "ordered_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now, we replace the labels with the woe\n",
    "\n",
    "X_train['cabin'] = X_train['cabin'].map(ordered_labels)\n",
    "X_test['cabin'] = X_test['cabin'].map(ordered_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "843    -0.884730\n",
       "869    -0.884730\n",
       "430    -0.884730\n",
       "481    -0.884730\n",
       "1308   -0.884730\n",
       "456    -0.884730\n",
       "118     0.832909\n",
       "485     0.832909\n",
       "297    -0.884730\n",
       "263     0.847298\n",
       "Name: cabin, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's explore the result\n",
    "\n",
    "X_train['cabin'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can turn the previous commands into 2 functions\n",
    "\n",
    "\n",
    "def find_category_mappings(df, variable, target):\n",
    "\n",
    "    tmp = pd.DataFrame(df.groupby([variable])[target].mean())\n",
    "    \n",
    "    tmp['non-target'] = 1 - tmp[target]\n",
    "    \n",
    "    tmp['ratio'] = np.log( tmp[target] / tmp['non-target'] )\n",
    "\n",
    "    return tmp['ratio'].to_dict()\n",
    "\n",
    "\n",
    "def integer_encode(train, test, variable, ordinal_mapping):\n",
    "\n",
    "    X_train[variable] = X_train[variable].map(ordinal_mapping)\n",
    "    \n",
    "    X_test[variable] = X_test[variable].map(ordinal_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# and now we run a loop over the remaining categorical variables\n",
    "\n",
    "for variable in ['sex', 'embarked']:\n",
    "    \n",
    "    mappings = find_category_mappings(X_train, variable, 'survived')\n",
    "    \n",
    "    integer_encode(X_train, X_test, variable, mappings)"
   ]
  },
  {
   "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>cabin</th>\n",
       "      <th>sex</th>\n",
       "      <th>embarked</th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>843</th>\n",
       "      <td>-0.88473</td>\n",
       "      <td>-1.522770</td>\n",
       "      <td>-0.674455</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>-0.88473</td>\n",
       "      <td>-1.522770</td>\n",
       "      <td>-0.674455</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>-0.88473</td>\n",
       "      <td>1.025409</td>\n",
       "      <td>-0.674455</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>-0.88473</td>\n",
       "      <td>1.025409</td>\n",
       "      <td>0.149212</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>-0.88473</td>\n",
       "      <td>-1.522770</td>\n",
       "      <td>-0.674455</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        cabin       sex  embarked  survived\n",
       "843  -0.88473 -1.522770 -0.674455         0\n",
       "869  -0.88473 -1.522770 -0.674455         0\n",
       "430  -0.88473  1.025409 -0.674455         1\n",
       "481  -0.88473  1.025409  0.149212         1\n",
       "1308 -0.88473 -1.522770 -0.674455         0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's see the result\n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's inspect the newly created monotonic relationship\n",
    "# between the categorical variables and the target\n",
    "\n",
    "for var in ['cabin', 'sex', 'embarked']:\n",
    "    \n",
    "    fig = plt.figure()\n",
    "    fig = X_train.groupby([var])['survived'].mean().plot()\n",
    "    fig.set_title('Monotonic relationship between {} and Survival'.format(var))\n",
    "    fig.set_ylabel('Mean Survived')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the monotonic relationships between the mean target and the categories.\n",
    "\n",
    "### Note\n",
    "\n",
    "Replacing categorical labels with this code and method will generate missing values for categories present in the test set that were not seen in the training set. Therefore it is extremely important to handle rare labels before-hand. I will explain how to do this, in a later notebook.\n",
    "\n",
    "**In addition, it will create NA or Inf if the probability of target = 0 is zero, as the division by zero is not defined.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weight of Evidence with Feature-Engine\n",
    "\n",
    "If using Feature-Engine, instead of pandas, we do not need to keep the target variable in the training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((914, 3), (392, 3))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's separate into training and testing set\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data[['cabin', 'sex', 'embarked']],  # predictors\n",
    "    data['survived'],  # target\n",
    "    test_size=0.3,  # percentage of obs in test set\n",
    "    random_state=0)  # seed to ensure reproducibility\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "woe_enc = WoERatioCategoricalEncoder(\n",
    "    encoding_method = 'woe',\n",
    "    variables=['cabin', 'sex', 'embarked'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WoERatioCategoricalEncoder(encoding_method='woe',\n",
       "                           variables=['cabin', 'sex', 'embarked'])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# when fitting the transformer, we need to pass the target as well\n",
    "# just like with any Scikit-learn predictor class\n",
    "\n",
    "woe_enc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cabin': {'A': -0.35667494393873245,\n",
       "  'B': 1.0360919316867758,\n",
       "  'C': 0.4054651081081642,\n",
       "  'D': 0.8329091229351043,\n",
       "  'E': 0.8472978603872034,\n",
       "  'F': 1.2039728043259361,\n",
       "  'G': 1.0986122886681098,\n",
       "  'n': -0.8847299269619376},\n",
       " 'sex': {'female': 1.0254088846448146, 'male': -1.5227695297884265},\n",
       " 'embarked': {'C': 0.14921239818667575,\n",
       "  'Q': -0.8362480242006187,\n",
       "  'S': -0.6744550475477927}}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# in the encoder dict we see the woe for each category\n",
    "# for each of the selected variables\n",
    "\n",
    "woe_enc.encoder_dict_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cabin', 'sex', 'embarked']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is the list of variables that the encoder will transform\n",
    "\n",
    "woe_enc.variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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",
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       "    }\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cabin</th>\n",
       "      <th>sex</th>\n",
       "      <th>embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>843</th>\n",
       "      <td>-0.88473</td>\n",
       "      <td>-1.522770</td>\n",
       "      <td>-0.674455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>-0.88473</td>\n",
       "      <td>-1.522770</td>\n",
       "      <td>-0.674455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>-0.88473</td>\n",
       "      <td>1.025409</td>\n",
       "      <td>-0.674455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>-0.88473</td>\n",
       "      <td>1.025409</td>\n",
       "      <td>0.149212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>-0.88473</td>\n",
       "      <td>-1.522770</td>\n",
       "      <td>-0.674455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        cabin       sex  embarked\n",
       "843  -0.88473 -1.522770 -0.674455\n",
       "869  -0.88473 -1.522770 -0.674455\n",
       "430  -0.88473  1.025409 -0.674455\n",
       "481  -0.88473  1.025409  0.149212\n",
       "1308 -0.88473 -1.522770 -0.674455"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = woe_enc.transform(X_train)\n",
    "X_test = woe_enc.transform(X_test)\n",
    "\n",
    "# let's explore the result\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**\n",
    "\n",
    "If the argument variables is left to None, then the encoder will automatically identify all categorical variables. Is that not sweet?\n",
    "\n",
    "The encoder will not encode numerical variables. So if some of your numerical variables are in fact categories, you will need to re-cast them as object before using the encoder.\n",
    "\n",
    "If there is a label in the test set that was not present in the train set, the encoder will through and error, to alert you of this behaviour.\n",
    "\n",
    "Finally, if the probability of target = 0 is zero for any category, the encoder will raise an error as the division by zero is not defined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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