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Amazing-Feature-Engineering/3.3_Demo_Feature_Encoding.ipynb
DESKTOP-SAT83DL\yimeng.zhang 149c80cedf 2018.12.2 First commit.
2018-12-02 21:11:32 +08:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"# import seaborn as sns\n",
"# import matplotlib.pyplot as plt\n",
"import os\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"import category_encoders as ce\n",
"from feature_engineering import encoding\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Dataset"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"\n",
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" 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>Pclass</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Fare</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>7.2500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>71.2833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>7.9250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>53.1000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>8.0500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Survived Pclass Sex Age SibSp Fare\n",
"0 0 3 male 22.0 1 7.2500\n",
"1 1 1 female 38.0 1 71.2833\n",
"2 1 3 female 26.0 0 7.9250\n",
"3 1 1 female 35.0 1 53.1000\n",
"4 0 3 male 35.0 0 8.0500"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"use_cols = [\n",
" 'Pclass', 'Sex', 'Age', 'Fare', 'SibSp',\n",
" 'Survived'\n",
"]\n",
"\n",
"data = pd.read_csv('./data/titanic.csv', usecols=use_cols)\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((623, 6), (268, 6))"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(data, data.Survived, test_size=0.3,\n",
" random_state=0)\n",
"X_train.shape, X_test.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## One-hot encoding\n",
"replace the categorical variable by different boolean variables (0/1) to indicate whether or not certain label is true for that observation"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data1 = pd.get_dummies(data,drop_first=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"\n",
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" 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>Pclass</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Fare</th>\n",
" <th>Sex_male</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>7.2500</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>71.2833</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>7.9250</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>53.1000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>8.0500</td>\n",
" <td>1</td>\n",
" </tr>\n",
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"text/plain": [
" Survived Pclass Age SibSp Fare Sex_male\n",
"0 0 3 22.0 1 7.2500 1\n",
"1 1 1 38.0 1 71.2833 0\n",
"2 1 3 26.0 0 7.9250 0\n",
"3 1 1 35.0 1 53.1000 0\n",
"4 0 3 35.0 0 8.0500 1"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data1.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Ordinal-encoding\n",
"replace the labels by some ordinal number if ordinal is meaningful"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ord_enc = ce.OrdinalEncoder(cols=['Sex']).fit(X_train,y_train)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Survived Pclass Sex Age SibSp Fare\n",
"0 0 3 1 22.0 1 7.2500\n",
"1 1 1 2 38.0 1 71.2833\n",
"2 1 3 2 26.0 0 7.9250\n",
"3 1 1 2 35.0 1 53.1000\n",
"4 0 3 1 35.0 0 8.0500\n"
]
}
],
"source": [
"data4 = ord_enc.transform(data)\n",
"print(data4.head(5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Mean encoding\n",
"replace the label by the mean of the target for that label. \n",
"(the target must be 0/1 valued or continuous)\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Sex\n",
"female 0.753488\n",
"male 0.196078\n",
"Name: Survived, dtype: float64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# cross check-- the mean of target group by Sex\n",
"X_train['Survived'].groupby(data['Sex']).mean()\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"mean_enc = encoding.MeanEncoding(cols=['Sex']).fit(X_train,y_train)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Survived Pclass Sex Age SibSp Fare\n",
"0 0 3 0.196078 22.0 1 7.2500\n",
"1 1 1 0.753488 38.0 1 71.2833\n",
"2 1 3 0.753488 26.0 0 7.9250\n",
"3 1 1 0.753488 35.0 1 53.1000\n",
"4 0 3 0.196078 35.0 0 8.0500\n"
]
}
],
"source": [
"data6 = mean_enc.transform(data)\n",
"print(data6.head(5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Target-encoding\n",
"Similar to mean encoding, but use both posterior probability and prior probability of the target"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# create the encoder and fit with our data\n",
"target_enc = ce.TargetEncoder(cols=['Sex']).fit(X_train,y_train)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# perform transformation\n",
"# data.Survived.groupby(data['Sex']).agg(['mean'])\n",
"data2 = target_enc.transform(data)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Fare</th>\n",
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" </thead>\n",
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" <th>0</th>\n",
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" <td>1</td>\n",
" <td>7.2500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.753488</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>71.2833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0.753488</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>7.9250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.753488</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>53.1000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0.196078</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>8.0500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text/plain": [
" Survived Pclass Sex Age SibSp Fare\n",
"0 0 3 0.196078 22.0 1 7.2500\n",
"1 1 1 0.753488 38.0 1 71.2833\n",
"2 1 3 0.753488 26.0 0 7.9250\n",
"3 1 1 0.753488 35.0 1 53.1000\n",
"4 0 3 0.196078 35.0 0 8.0500"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# check the result\n",
"data2.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## WOE-encoding\n",
"replace the label with Weight of Evidence of each label. WOE is computed from the basic odds ratio: \n",
"\n",
"ln( (Proportion of Good Outcomes) / (Proportion of Bad Outcomes))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"woe_enc = ce.WOEEncoder(cols=['Sex']).fit(X_train,y_train)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data3 = woe_enc.transform(data)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
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" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>71.2833</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
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" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>7.9250</td>\n",
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" <td>1</td>\n",
" <td>1</td>\n",
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" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>53.1000</td>\n",
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" <td>3</td>\n",
" <td>-0.950742</td>\n",
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" Survived Pclass Sex Age SibSp Fare\n",
"0 0 3 -0.950742 22.0 1 7.2500\n",
"1 1 1 1.555633 38.0 1 71.2833\n",
"2 1 3 1.555633 26.0 0 7.9250\n",
"3 1 1 1.555633 35.0 1 53.1000\n",
"4 0 3 -0.950742 35.0 0 8.0500"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data3.head(5)"
]
},
{
"cell_type": "code",
"execution_count": null,
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"source": []
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