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mylamour/feature_engine.ipynb Secret

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[特征工程教程] #python #jupynotebook
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feature_engine.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Learn from https://www.cnblogs.com/jasonfreak/p/5448385.html\n",
"# Note And Tutorial\n",
"\n",
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.datasets import load_iris"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 5.1 3.5 1.4 0.2]\n",
" [ 4.9 3. 1.4 0.2]\n",
" [ 4.7 3.2 1.3 0.2]\n",
" [ 4.6 3.1 1.5 0.2]\n",
" [ 5. 3.6 1.4 0.2]\n",
" [ 5.4 3.9 1.7 0.4]\n",
" [ 4.6 3.4 1.4 0.3]\n",
" [ 5. 3.4 1.5 0.2]\n",
" [ 4.4 2.9 1.4 0.2]\n",
" [ 4.9 3.1 1.5 0.1]\n",
" [ 5.4 3.7 1.5 0.2]\n",
" [ 4.8 3.4 1.6 0.2]\n",
" [ 4.8 3. 1.4 0.1]\n",
" [ 4.3 3. 1.1 0.1]\n",
" [ 5.8 4. 1.2 0.2]\n",
" [ 5.7 4.4 1.5 0.4]\n",
" [ 5.4 3.9 1.3 0.4]\n",
" [ 5.1 3.5 1.4 0.3]\n",
" [ 5.7 3.8 1.7 0.3]\n",
" [ 5.1 3.8 1.5 0.3]\n",
" [ 5.4 3.4 1.7 0.2]\n",
" [ 5.1 3.7 1.5 0.4]\n",
" [ 4.6 3.6 1. 0.2]\n",
" [ 5.1 3.3 1.7 0.5]\n",
" [ 4.8 3.4 1.9 0.2]\n",
" [ 5. 3. 1.6 0.2]\n",
" [ 5. 3.4 1.6 0.4]\n",
" [ 5.2 3.5 1.5 0.2]\n",
" [ 5.2 3.4 1.4 0.2]\n",
" [ 4.7 3.2 1.6 0.2]\n",
" [ 4.8 3.1 1.6 0.2]\n",
" [ 5.4 3.4 1.5 0.4]\n",
" [ 5.2 4.1 1.5 0.1]\n",
" [ 5.5 4.2 1.4 0.2]\n",
" [ 4.9 3.1 1.5 0.1]\n",
" [ 5. 3.2 1.2 0.2]\n",
" [ 5.5 3.5 1.3 0.2]\n",
" [ 4.9 3.1 1.5 0.1]\n",
" [ 4.4 3. 1.3 0.2]\n",
" [ 5.1 3.4 1.5 0.2]\n",
" [ 5. 3.5 1.3 0.3]\n",
" [ 4.5 2.3 1.3 0.3]\n",
" [ 4.4 3.2 1.3 0.2]\n",
" [ 5. 3.5 1.6 0.6]\n",
" [ 5.1 3.8 1.9 0.4]\n",
" [ 4.8 3. 1.4 0.3]\n",
" [ 5.1 3.8 1.6 0.2]\n",
" [ 4.6 3.2 1.4 0.2]\n",
" [ 5.3 3.7 1.5 0.2]\n",
" [ 5. 3.3 1.4 0.2]\n",
" [ 7. 3.2 4.7 1.4]\n",
" [ 6.4 3.2 4.5 1.5]\n",
" [ 6.9 3.1 4.9 1.5]\n",
" [ 5.5 2.3 4. 1.3]\n",
" [ 6.5 2.8 4.6 1.5]\n",
" [ 5.7 2.8 4.5 1.3]\n",
" [ 6.3 3.3 4.7 1.6]\n",
" [ 4.9 2.4 3.3 1. ]\n",
" [ 6.6 2.9 4.6 1.3]\n",
" [ 5.2 2.7 3.9 1.4]\n",
" [ 5. 2. 3.5 1. ]\n",
" [ 5.9 3. 4.2 1.5]\n",
" [ 6. 2.2 4. 1. ]\n",
" [ 6.1 2.9 4.7 1.4]\n",
" [ 5.6 2.9 3.6 1.3]\n",
" [ 6.7 3.1 4.4 1.4]\n",
" [ 5.6 3. 4.5 1.5]\n",
" [ 5.8 2.7 4.1 1. ]\n",
" [ 6.2 2.2 4.5 1.5]\n",
" [ 5.6 2.5 3.9 1.1]\n",
" [ 5.9 3.2 4.8 1.8]\n",
" [ 6.1 2.8 4. 1.3]\n",
" [ 6.3 2.5 4.9 1.5]\n",
" [ 6.1 2.8 4.7 1.2]\n",
" [ 6.4 2.9 4.3 1.3]\n",
" [ 6.6 3. 4.4 1.4]\n",
" [ 6.8 2.8 4.8 1.4]\n",
" [ 6.7 3. 5. 1.7]\n",
" [ 6. 2.9 4.5 1.5]\n",
" [ 5.7 2.6 3.5 1. ]\n",
" [ 5.5 2.4 3.8 1.1]\n",
" [ 5.5 2.4 3.7 1. ]\n",
" [ 5.8 2.7 3.9 1.2]\n",
" [ 6. 2.7 5.1 1.6]\n",
" [ 5.4 3. 4.5 1.5]\n",
" [ 6. 3.4 4.5 1.6]\n",
" [ 6.7 3.1 4.7 1.5]\n",
" [ 6.3 2.3 4.4 1.3]\n",
" [ 5.6 3. 4.1 1.3]\n",
" [ 5.5 2.5 4. 1.3]\n",
" [ 5.5 2.6 4.4 1.2]\n",
" [ 6.1 3. 4.6 1.4]\n",
" [ 5.8 2.6 4. 1.2]\n",
" [ 5. 2.3 3.3 1. ]\n",
" [ 5.6 2.7 4.2 1.3]\n",
" [ 5.7 3. 4.2 1.2]\n",
" [ 5.7 2.9 4.2 1.3]\n",
" [ 6.2 2.9 4.3 1.3]\n",
" [ 5.1 2.5 3. 1.1]\n",
" [ 5.7 2.8 4.1 1.3]\n",
" [ 6.3 3.3 6. 2.5]\n",
" [ 5.8 2.7 5.1 1.9]\n",
" [ 7.1 3. 5.9 2.1]\n",
" [ 6.3 2.9 5.6 1.8]\n",
" [ 6.5 3. 5.8 2.2]\n",
" [ 7.6 3. 6.6 2.1]\n",
" [ 4.9 2.5 4.5 1.7]\n",
" [ 7.3 2.9 6.3 1.8]\n",
" [ 6.7 2.5 5.8 1.8]\n",
" [ 7.2 3.6 6.1 2.5]\n",
" [ 6.5 3.2 5.1 2. ]\n",
" [ 6.4 2.7 5.3 1.9]\n",
" [ 6.8 3. 5.5 2.1]\n",
" [ 5.7 2.5 5. 2. ]\n",
" [ 5.8 2.8 5.1 2.4]\n",
" [ 6.4 3.2 5.3 2.3]\n",
" [ 6.5 3. 5.5 1.8]\n",
" [ 7.7 3.8 6.7 2.2]\n",
" [ 7.7 2.6 6.9 2.3]\n",
" [ 6. 2.2 5. 1.5]\n",
" [ 6.9 3.2 5.7 2.3]\n",
" [ 5.6 2.8 4.9 2. ]\n",
" [ 7.7 2.8 6.7 2. ]\n",
" [ 6.3 2.7 4.9 1.8]\n",
" [ 6.7 3.3 5.7 2.1]\n",
" [ 7.2 3.2 6. 1.8]\n",
" [ 6.2 2.8 4.8 1.8]\n",
" [ 6.1 3. 4.9 1.8]\n",
" [ 6.4 2.8 5.6 2.1]\n",
" [ 7.2 3. 5.8 1.6]\n",
" [ 7.4 2.8 6.1 1.9]\n",
" [ 7.9 3.8 6.4 2. ]\n",
" [ 6.4 2.8 5.6 2.2]\n",
" [ 6.3 2.8 5.1 1.5]\n",
" [ 6.1 2.6 5.6 1.4]\n",
" [ 7.7 3. 6.1 2.3]\n",
" [ 6.3 3.4 5.6 2.4]\n",
" [ 6.4 3.1 5.5 1.8]\n",
" [ 6. 3. 4.8 1.8]\n",
" [ 6.9 3.1 5.4 2.1]\n",
" [ 6.7 3.1 5.6 2.4]\n",
" [ 6.9 3.1 5.1 2.3]\n",
" [ 5.8 2.7 5.1 1.9]\n",
" [ 6.8 3.2 5.9 2.3]\n",
" [ 6.7 3.3 5.7 2.5]\n",
" [ 6.7 3. 5.2 2.3]\n",
" [ 6.3 2.5 5. 1.9]\n",
" [ 6.5 3. 5.2 2. ]\n",
" [ 6.2 3.4 5.4 2.3]\n",
" [ 5.9 3. 5.1 1.8]]\n",
"[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
" 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
" 2 2]\n"
]
}
],
"source": [
"iris = load_iris()\n",
"print(iris.data)\n",
"print(iris.target)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 数据预处理"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
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" -1.31297673e+00],\n",
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" -1.31297673e+00],\n",
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" -1.31297673e+00],\n",
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" -1.31297673e+00],\n",
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" -1.31297673e+00],\n",
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" -1.44444970e+00],\n",
" [ -1.74885626e+00, -1.24957601e-01, -1.39813811e+00,\n",
" -1.31297673e+00],\n",
" [ -9.00681170e-01, 8.00654259e-01, -1.28440670e+00,\n",
" -1.31297673e+00],\n",
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" -1.05003079e+00],\n",
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" -1.18150376e+00],\n",
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" -1.31297673e+00],\n",
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" -1.31297673e+00],\n",
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" -1.31297673e+00],\n",
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" -1.31297673e+00],\n",
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" 3.96171883e-01],\n",
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" 3.96171883e-01],\n",
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" 1.33225943e-01],\n",
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" 3.96171883e-01],\n",
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" 1.33225943e-01],\n",
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" 5.27644853e-01],\n",
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" 1.33225943e-01],\n",
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" 2.64698913e-01],\n",
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" -2.61192967e-01],\n",
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" 3.96171883e-01],\n",
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" -2.61192967e-01],\n",
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" 2.64698913e-01],\n",
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" 2.64698913e-01],\n",
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" 3.96171883e-01],\n",
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" 1.75297293e-03],\n",
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" 1.33225943e-01],\n",
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" 2.64698913e-01],\n",
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" 6.59117823e-01],\n",
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" 3.96171883e-01],\n",
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" -2.61192967e-01],\n",
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" -1.29719997e-01],\n",
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" -2.61192967e-01],\n",
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" 1.75297293e-03],\n",
" [ 1.89829664e-01, -8.19166497e-01, 7.62758643e-01,\n",
" 5.27644853e-01],\n",
" [ -5.37177559e-01, -1.24957601e-01, 4.21564419e-01,\n",
" 3.96171883e-01],\n",
" [ 1.89829664e-01, 8.00654259e-01, 4.21564419e-01,\n",
" 5.27644853e-01],\n",
" [ 1.03800476e+00, 1.06445364e-01, 5.35295827e-01,\n",
" 3.96171883e-01],\n",
" [ 5.53333275e-01, -1.74477836e+00, 3.64698715e-01,\n",
" 1.33225943e-01],\n",
" [ -2.94841818e-01, -1.24957601e-01, 1.94101603e-01,\n",
" 1.33225943e-01],\n",
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" 1.33225943e-01],\n",
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" 2.64698913e-01],\n",
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" -2.61192967e-01],\n",
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" -1.29719997e-01],\n",
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" 1.33225943e-01],\n",
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" 1.71090158e+00],\n",
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" 1.18500970e+00],\n",
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" 6.59117823e-01],\n",
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" 7.90590793e-01],\n",
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" 1.05353673e+00],\n",
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" 7.90590793e-01],\n",
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" 1.31648267e+00],\n",
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" 1.44795564e+00],\n",
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" 1.05353673e+00],\n",
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" 7.90590793e-01],\n",
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" 1.18500970e+00],\n",
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" 7.90590793e-01],\n",
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" 7.90590793e-01],\n",
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" 7.90590793e-01],\n",
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" 1.18500970e+00],\n",
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" 5.27644853e-01],\n",
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" 9.22063763e-01],\n",
" [ 2.49201920e+00, 1.72626612e+00, 1.50201279e+00,\n",
" 1.05353673e+00],\n",
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" 1.31648267e+00],\n",
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" 3.96171883e-01],\n",
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" 2.64698913e-01],\n",
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" 1.44795564e+00],\n",
" [ 5.53333275e-01, 8.00654259e-01, 1.04708716e+00,\n",
" 1.57942861e+00],\n",
" [ 6.74501145e-01, 1.06445364e-01, 9.90221459e-01,\n",
" 7.90590793e-01],\n",
" [ 1.89829664e-01, -1.24957601e-01, 5.92161531e-01,\n",
" 7.90590793e-01],\n",
" [ 1.28034050e+00, 1.06445364e-01, 9.33355755e-01,\n",
" 1.18500970e+00],\n",
" [ 1.03800476e+00, 1.06445364e-01, 1.04708716e+00,\n",
" 1.57942861e+00],\n",
" [ 1.28034050e+00, 1.06445364e-01, 7.62758643e-01,\n",
" 1.44795564e+00],\n",
" [ -5.25060772e-02, -8.19166497e-01, 7.62758643e-01,\n",
" 9.22063763e-01],\n",
" [ 1.15917263e+00, 3.37848329e-01, 1.21768427e+00,\n",
" 1.44795564e+00],\n",
" [ 1.03800476e+00, 5.69251294e-01, 1.10395287e+00,\n",
" 1.71090158e+00],\n",
" [ 1.03800476e+00, -1.24957601e-01, 8.19624347e-01,\n",
" 1.44795564e+00],\n",
" [ 5.53333275e-01, -1.28197243e+00, 7.05892939e-01,\n",
" 9.22063763e-01],\n",
" [ 7.95669016e-01, -1.24957601e-01, 8.19624347e-01,\n",
" 1.05353673e+00],\n",
" [ 4.32165405e-01, 8.00654259e-01, 9.33355755e-01,\n",
" 1.44795564e+00],\n",
" [ 6.86617933e-02, -1.24957601e-01, 7.62758643e-01,\n",
" 7.90590793e-01]])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 标准化需要计算特征的均值和方差, 返回值为标准化后的数据\n",
"# x' = (x - mean(x)) / S\n",
"from sklearn.preprocessing import StandardScaler\n",
"StandardScaler().fit_transform(iris.data)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" [ 0.73154399, 0.28501714, 0.57953485, 0.21851314],\n",
" [ 0.67017484, 0.36168166, 0.59571097, 0.2553047 ],\n",
" [ 0.69804799, 0.338117 , 0.59988499, 0.196326 ],\n",
" [ 0.71066905, 0.35533453, 0.56853524, 0.21320072],\n",
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" [ 0.69997037, 0.32386689, 0.58504986, 0.25073566],\n",
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" [ 0.69052512, 0.32145135, 0.60718588, 0.22620651],\n",
" [ 0.69193502, 0.32561648, 0.60035539, 0.23403685],\n",
" [ 0.68914871, 0.33943145, 0.58629069, 0.25714504],\n",
" [ 0.72155725, 0.32308533, 0.56001458, 0.24769876],\n",
" [ 0.72965359, 0.28954508, 0.57909015, 0.22005426],\n",
" [ 0.71653899, 0.3307103 , 0.57323119, 0.22047353],\n",
" [ 0.67467072, 0.36998072, 0.58761643, 0.25028107],\n",
" [ 0.69025916, 0.35097923, 0.5966647 , 0.21058754]])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 归一化\n",
"from sklearn.preprocessing import Normalizer\n",
"Normalizer().fit_transform(iris.data)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" [ 0.41666667, 0.29166667, 0.69491525, 0.75 ],\n",
" [ 0.69444444, 0.5 , 0.83050847, 0.91666667],\n",
" [ 0.66666667, 0.54166667, 0.79661017, 1. ],\n",
" [ 0.66666667, 0.41666667, 0.71186441, 0.91666667],\n",
" [ 0.55555556, 0.20833333, 0.6779661 , 0.75 ],\n",
" [ 0.61111111, 0.41666667, 0.71186441, 0.79166667],\n",
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" [ 0.44444444, 0.41666667, 0.69491525, 0.70833333]])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 区间缩放法,最常见的是最值缩放\n",
"# x' = (x- Min)/(Max - Min)\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"MinMaxScaler().fit_transform(iris.data)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 标准化与归一化的区别\n",
"标准化是依照特征矩阵的列处理数据，其通过求z-score的方法，将样本的特征值转换到同一量纲下。\n",
"\n",
"归一化是依照特征矩阵的行处理数据，其目的在于样本向量在点乘运算或其他核函数计算相似性时，拥有统一的标准，也就是说都转化为“单位向量。"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" [ 1., 0., 1., 0.],\n",
" [ 1., 1., 1., 0.],\n",
" [ 1., 0., 1., 0.]])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 二値化\n",
"from sklearn.preprocessing import Binarizer\n",
"Binarizer(threshold=3).fit_transform(iris.data)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0, 0)\t1.0\n",
" (1, 0)\t1.0\n",
" (2, 0)\t1.0\n",
" (3, 0)\t1.0\n",
" (4, 0)\t1.0\n",
" (5, 0)\t1.0\n",
" (6, 0)\t1.0\n",
" (7, 0)\t1.0\n",
" (8, 0)\t1.0\n",
" (9, 0)\t1.0\n",
" (10, 0)\t1.0\n",
" (11, 0)\t1.0\n",
" (12, 0)\t1.0\n",
" (13, 0)\t1.0\n",
" (14, 0)\t1.0\n",
" (15, 0)\t1.0\n",
" (16, 0)\t1.0\n",
" (17, 0)\t1.0\n",
" (18, 0)\t1.0\n",
" (19, 0)\t1.0\n",
" (20, 0)\t1.0\n",
" (21, 0)\t1.0\n",
" (22, 0)\t1.0\n",
" (23, 0)\t1.0\n",
" (24, 0)\t1.0\n",
" :\t:\n",
" (125, 2)\t1.0\n",
" (126, 2)\t1.0\n",
" (127, 2)\t1.0\n",
" (128, 2)\t1.0\n",
" (129, 2)\t1.0\n",
" (130, 2)\t1.0\n",
" (131, 2)\t1.0\n",
" (132, 2)\t1.0\n",
" (133, 2)\t1.0\n",
" (134, 2)\t1.0\n",
" (135, 2)\t1.0\n",
" (136, 2)\t1.0\n",
" (137, 2)\t1.0\n",
" (138, 2)\t1.0\n",
" (139, 2)\t1.0\n",
" (140, 2)\t1.0\n",
" (141, 2)\t1.0\n",
" (142, 2)\t1.0\n",
" (143, 2)\t1.0\n",
" (144, 2)\t1.0\n",
" (145, 2)\t1.0\n",
" (146, 2)\t1.0\n",
" (147, 2)\t1.0\n",
" (148, 2)\t1.0\n",
" (149, 2)\t1.0\n"
]
}
],
"source": [
"# 定性特征One-Hot\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"print(OneHotEncoder().fit_transform(iris.target.reshape((-1,1))))\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([], shape=(4, 0), dtype=float64)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 缺失值计算\n",
"# 返回值为计算缺失值后的数据\n",
"\n",
"from sklearn.preprocessing import Imputer\n",
"Imputer().fit_transform(np.vstack(np.asarray([np.nan for i in range(4)])), iris.data)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1. , 5.1 , 3.5 , ..., 1.96, 0.28, 0.04],\n",
" [ 1. , 4.9 , 3. , ..., 1.96, 0.28, 0.04],\n",
" [ 1. , 4.7 , 3.2 , ..., 1.69, 0.26, 0.04],\n",
" ..., \n",
" [ 1. , 6.5 , 3. , ..., 27.04, 10.4 , 4. ],\n",
" [ 1. , 6.2 , 3.4 , ..., 29.16, 12.42, 5.29],\n",
" [ 1. , 5.9 , 3. , ..., 26.01, 9.18, 3.24]])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 数据变换\n",
"# 有基于多项式的,基于指数的,基于对数函数的\n",
"from sklearn.preprocessing import PolynomialFeatures\n",
"PolynomialFeatures().fit_transform(iris.data)\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" [ 1.96009478, 1.36097655, 1.74046617, 0.87546874],\n",
" [ 1.88706965, 1.36097655, 1.5260563 , 0.83290912],\n",
" [ 2.04122033, 1.41098697, 1.68639895, 0.87546874],\n",
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" [ 1.88706965, 1.25276297, 1.58923521, 0.74193734],\n",
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" [ 2.12823171, 1.33500107, 1.96009478, 1.06471074],\n",
" [ 2.18605128, 1.56861592, 2.00148 , 1.09861229],\n",
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" [ 1.98787435, 1.33500107, 1.80828877, 0.91629073],\n",
" [ 1.96009478, 1.28093385, 1.88706965, 0.87546874],\n",
" [ 2.16332303, 1.38629436, 1.96009478, 1.19392247],\n",
" [ 1.98787435, 1.48160454, 1.88706965, 1.22377543],\n",
" [ 2.00148 , 1.41098697, 1.87180218, 1.02961942],\n",
" [ 1.94591015, 1.38629436, 1.75785792, 1.02961942],\n",
" [ 2.06686276, 1.41098697, 1.85629799, 1.13140211],\n",
" [ 2.04122033, 1.41098697, 1.88706965, 1.22377543],\n",
" [ 2.06686276, 1.41098697, 1.80828877, 1.19392247],\n",
" [ 1.91692261, 1.30833282, 1.80828877, 1.06471074],\n",
" [ 2.05412373, 1.43508453, 1.93152141, 1.19392247],\n",
" [ 2.04122033, 1.45861502, 1.90210753, 1.25276297],\n",
" [ 2.04122033, 1.38629436, 1.82454929, 1.19392247],\n",
" [ 1.98787435, 1.25276297, 1.79175947, 1.06471074],\n",
" [ 2.01490302, 1.38629436, 1.82454929, 1.09861229],\n",
" [ 1.97408103, 1.48160454, 1.85629799, 1.19392247],\n",
" [ 1.93152141, 1.38629436, 1.80828877, 1.02961942]])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.preprocessing import FunctionTransformer\n",
"FunctionTransformer(np.log1p).fit_transform(iris.data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 特征选择\n",
"\n",
"* 特征是否发散：\n",
"\n",
"如果一个特征不发散，例如方差接近于0，也就是说样本在这个特征上基本上没有差异，这个特征对于样本的区分并没有什么用。\n",
"\n",
"* 特征与目标的相关性：\n",
"\n",
"这点比较显见，与目标相关性高的特征，应当优选选择。除方差法外，本文介绍的其他方法均从相关性考虑。\n",
"\n",
"根据特征选择的形式又可以将特征选择方法分为3种：\n",
"* Filter：过滤法，\n",
"按照发散性或者相关性对各个特征进行评分，设定阈值或者待选择阈值的个数，选择特征。\n",
"* Wrapper：包装法，\n",
"根据目标函数（通常是预测效果评分），每次选择若干特征，或者排除若干特征。\n",
"* Embedded：嵌入法，\n",
"先使用某些机器学习的算法和模型进行训练，得到各个特征的权值系数，根据系数从大到小选择特征。类似于Filter方法，但是是通过训练来确定特征的优劣。\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Filter"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" [ 5.8],\n",
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" [ 5. ],\n",
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" [ 6. ],\n",
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" [ 4.8],\n",
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" [ 5.1],\n",
" [ 5.1],\n",
" [ 5.9],\n",
" [ 5.7],\n",
" [ 5.2],\n",
" [ 5. ],\n",
" [ 5.2],\n",
" [ 5.4],\n",
" [ 5.1]])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Filter: 方差选择法\n",
"from sklearn.feature_selection import VarianceThreshold\n",
"VarianceThreshold(threshold=3).fit_transform(iris.data)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" [ 5.2, 2. ],\n",
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" [ 5.1, 1.8]])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Filter: 相关系数法\n",
"from sklearn.feature_selection import SelectKBest\n",
"from scipy.stats import pearsonr\n",
"SelectKBest(lambda X, Y: \\\n",
" tuple(map(tuple,np.array(list(map(lambda x:\\\n",
" pearsonr(x, Y), X.T))).T)), k=2)\\\n",
" .fit_transform(iris.data, iris.target)\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" [ 6.4, 2. ],\n",
" [ 5.6, 2.2],\n",
" [ 5.1, 1.5],\n",
" [ 5.6, 1.4],\n",
" [ 6.1, 2.3],\n",
" [ 5.6, 2.4],\n",
" [ 5.5, 1.8],\n",
" [ 4.8, 1.8],\n",
" [ 5.4, 2.1],\n",
" [ 5.6, 2.4],\n",
" [ 5.1, 2.3],\n",
" [ 5.1, 1.9],\n",
" [ 5.9, 2.3],\n",
" [ 5.7, 2.5],\n",
" [ 5.2, 2.3],\n",
" [ 5. , 1.9],\n",
" [ 5.2, 2. ],\n",
" [ 5.4, 2.3],\n",
" [ 5.1, 1.8]])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Filter: 卡方检验\n",
"from sklearn.feature_selection import SelectKBest\n",
"from sklearn.feature_selection import chi2\n",
"\n",
"SelectKBest(chi2, k=2).fit_transform(iris.data, iris.target)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Filter: 互信息法\n",
"# from sklearn.feature_selection import SelectKBest\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" [ 3. , 1.8],\n",
" [ 2.8, 2.1],\n",
" [ 3. , 1.6],\n",
" [ 2.8, 1.9],\n",
" [ 3.8, 2. ],\n",
" [ 2.8, 2.2],\n",
" [ 2.8, 1.5],\n",
" [ 2.6, 1.4],\n",
" [ 3. , 2.3],\n",
" [ 3.4, 2.4],\n",
" [ 3.1, 1.8],\n",
" [ 3. , 1.8],\n",
" [ 3.1, 2.1],\n",
" [ 3.1, 2.4],\n",
" [ 3.1, 2.3],\n",
" [ 2.7, 1.9],\n",
" [ 3.2, 2.3],\n",
" [ 3.3, 2.5],\n",
" [ 3. , 2.3],\n",
" [ 2.5, 1.9],\n",
" [ 3. , 2. ],\n",
" [ 3.4, 2.3],\n",
" [ 3. , 1.8]])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Warpper: 递归特征消除\n",
"from sklearn.feature_selection import RFE\n",
"from sklearn.linear_model import LogisticRegression\n",
"\n",
"RFE(estimator=LogisticRegression(), n_features_to_select=2)\\\n",
" .fit_transform(iris.data,iris.target)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Embedded\n",
"使用带惩罚项的基模型，除了筛选出特征外，同时也进行了降维。\n",
"\n",
"基于L1惩罚项,L1惩罚项降维的原理在于保留多个对目标值具有同等相关性的特征中的一个,所以没选到的特征不代表不重要。\n",
"\n",
"结合L2惩罚项来优化,具体操作为：\n",
"若一个特征在L1中的权值为1，选择在L2中权值差别不大且在L1中权值为0的特征构成同类集合，将这一集合中的特征平分L1中的权值.\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
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" [ 5.9, 3. , 5.1]])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Embedded: 基于惩罚模型特征选择法\n",
"from sklearn.feature_selection import SelectFromModel\n",
"from sklearn.linear_model import LogisticRegression\n",
"\n",
"SelectFromModel(LogisticRegression(penalty='l1', C=0.1))\\\n",
" .fit_transform(iris.data,iris.target)\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" [ 5.5, 1.8],\n",
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" [ 5.1, 2.3],\n",
" [ 5.1, 1.9],\n",
" [ 5.9, 2.3],\n",
" [ 5.7, 2.5],\n",
" [ 5.2, 2.3],\n",
" [ 5. , 1.9],\n",
" [ 5.2, 2. ],\n",
" [ 5.4, 2.3],\n",
" [ 5.1, 1.8]])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Embedded: 基于树模型的特征选择\n",
"from sklearn.feature_selection import SelectFromModel\n",
"from sklearn.ensemble import GradientBoostingClassifier\n",
"\n",
"SelectFromModel(GradientBoostingClassifier())\\\n",
" .fit_transform(iris.data, iris.target)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.07817179 0.03503159 0.38008987 0.50670675]\n"
]
},
{
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" [ 4.8, 1.8],\n",
" [ 5.4, 2.1],\n",
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" [ 5.1, 1.8]])"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 基于树模型\n",
"from sklearn.ensemble import ExtraTreesClassifier\n",
"clf = ExtraTreesClassifier()\n",
"clf.fit(iris.data,iris.target)\n",
"print(clf.feature_importances_)\n",
"# OR Just use Select From Model\n",
"SelectFromModel(clf, prefit=True).transform(iris.data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 降维\n",
"\n",
"当特征选择完成后，可以直接训练模型了，但是可能由于特征矩阵过大，导致计算量大，训练时间长的问题，因此降低特征矩阵维度也是必不可少的。\n",
"\n",
"常见的降维方法除了以上提到的基于L1惩罚项的模型以外，另外还有主成分分析法（PCA）和线性判别分析（LDA），线性判别分析本身也是一个分类模型。\n",
"\n",
"PCA和LDA有很多的相似点，其本质是要将原始的样本映射到维度更低的样本空间中，但是PCA和LDA的映射目标不一样：\n",
"* PCA是为了让映射后的样本具有最大的发散性；\n",
"* 而LDA是为了让映射后的样本有最好的分类性能。\n",
"\n",
"所以说PCA是一种无监督的降维方法，而LDA是一种有监督的降维方法。\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
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" [ 0.81286779, -0.370679 ],\n",
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" [ 0.89016045, -0.03381244],\n",
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" [-0.70708128, -1.00842476],\n",
" [ 0.35553304, -0.50321849],\n",
" [ 0.33112695, -0.21118014],\n",
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" [ 0.64169028, 0.01907118],\n",
" [-0.90846333, -0.75156873],\n",
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" [ 2.53172698, -0.01184224],\n",
" [ 1.41407223, -0.57492506],\n",
" [ 2.61648461, 0.34193529],\n",
" [ 1.97081495, -0.18112569],\n",
" [ 2.34975798, -0.04188255],\n",
" [ 3.39687992, 0.54716805],\n",
" [ 0.51938325, -1.19135169],\n",
" [ 2.9320051 , 0.35237701],\n",
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" [ 1.66193495, 0.2420384 ],\n",
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" [ 1.5852673 , -0.53930705],\n",
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" [ 3.79468686, 0.25326557],\n",
" [ 1.29832982, -0.76101394],\n",
" [ 2.42816726, 0.37678197],\n",
" [ 1.19809737, -0.60557896],\n",
" [ 3.49926548, 0.45677347],\n",
" [ 1.38766825, -0.20403099],\n",
" [ 2.27585365, 0.33338653],\n",
" [ 2.61419383, 0.55836695],\n",
" [ 1.25762518, -0.179137 ],\n",
" [ 1.29066965, -0.11642525],\n",
" [ 2.12285398, -0.21085488],\n",
" [ 2.3875644 , 0.46251925],\n",
" [ 2.84096093, 0.37274259],\n",
" [ 3.2323429 , 1.37052404],\n",
" [ 2.15873837, -0.21832553],\n",
" [ 1.4431026 , -0.14380129],\n",
" [ 1.77964011, -0.50146479],\n",
" [ 3.07652162, 0.68576444],\n",
" [ 2.14498686, 0.13890661],\n",
" [ 1.90486293, 0.04804751],\n",
" [ 1.16885347, -0.1645025 ],\n",
" [ 2.10765373, 0.37148225],\n",
" [ 2.31430339, 0.18260885],\n",
" [ 1.92245088, 0.40927118],\n",
" [ 1.41407223, -0.57492506],\n",
" [ 2.56332271, 0.2759745 ],\n",
" [ 2.41939122, 0.30350394],\n",
" [ 1.94401705, 0.18741522],\n",
" [ 1.52566363, -0.37502085],\n",
" [ 1.76404594, 0.07851919],\n",
" [ 1.90162908, 0.11587675],\n",
" [ 1.38966613, -0.28288671]])"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 降维: PCA\n",
"from sklearn.decomposition import PCA\n",
"PCA(n_components=2).fit_transform(iris.data)\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/mour/anaconda3/lib/python3.6/site-packages/sklearn/lda.py:6: DeprecationWarning: lda.LDA has been moved to discriminant_analysis.LinearDiscriminantAnalysis in 0.17 and will be removed in 0.19\n",
" \"in 0.17 and will be removed in 0.19\", DeprecationWarning)\n"
]
},
{
"data": {
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" [ 4.68400868, 0.32508073]])"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 降维: LDA\n",
"from sklearn.lda import LDA\n",
"LDA(n_components=2).fit_transform(iris.data, iris.target)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 总结\n",
"\n",
"## 数据预处理\n",
"\n",
"| 类 | 功能 | 说明 |\n",
"|---------------------|--------------------|----------------------------------------------------------|\n",
"| StandardScaler | 无量纲化 | 标准化，基于特征矩阵的列，将特征值转换至服从标准正态分布 |\n",
"| MinMaxScaler | 无量纲化 | 区间缩放，基于最大最小值，将特征值转换到[0, 1]区间上 |\n",
"| Normalizer | 归一化 | 基于特征矩阵的行，将样本向量转换为“单位向量” |\n",
"| Binarizer | 二值化 | 基于给定阈值，将定量特征按阈值划分 |\n",
"| OneHotEncoder | 哑编码 | 将定性数据编码为定量数据 |\n",
"| Imputer | 缺失值计算 | 计算缺失值，缺失值可填充为均值等 |\n",
"| PolynomialFeatures | 多项式数据转换 | 多项式数据转换 |\n",
"| FunctionTransformer | 自定义单元数据转换 | 使用单变元的函数来转换数据 |\n",
"\n",
"\n",
"## 特征选择\n",
"\n",
"| 类 | 所属方式 | 说明 |\n",
"|-------------------|----------|--------------------------------------------------------|\n",
"| VarianceThreshold | Filter | 方差选择法 |\n",
"| SelectKBest | Filter | 可选关联系数、卡方校验、最大信息系数作为得分计算的方法 |\n",
"| RFE | Wrapper | 递归地训练基模型，将权值系数较小的特征从特征集合中消除 |\n",
"| SelectFromModel | Embedded | 训练基模型，选择权值系数较高的特征 |\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# References\n",
"\n",
"* [机器学习中，有哪些特征选择的工程方法？](https://www.zhihu.com/question/28641663/answer/41653367)"
]
}
],
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"file_extension": ".py",
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"name": "python",
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"pygments_lexer": "ipython3",
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