336 lines
11 KiB
Plaintext
336 lines
11 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 机器学习100天——第1天：数据预处理（Data Preprocessing）"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 第一步：导入需要的库\n",
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"这两个是我们每次都需要导入的库。NumPy包含数学计算函数。Pandas用于导入和管理数据集。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 第二步：导入数据集\n",
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"数据集通常是.csv格式。CSV文件以文本形式保存表格数据。文件的每一行是一条数据记录。我们使用Pandas的read_csv方法读取本地csv文件为一个数据帧。然后，从数据帧中制作自变量和因变量的矩阵和向量。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Step 2: Importing dataset\n",
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"X\n",
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"[['France' 44.0 72000.0]\n",
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" ['Spain' 27.0 48000.0]\n",
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" ['Germany' 30.0 54000.0]\n",
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" ['Spain' 38.0 61000.0]\n",
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" ['Germany' 40.0 nan]\n",
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" ['France' 35.0 58000.0]\n",
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" ['Spain' nan 52000.0]\n",
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" ['France' 48.0 79000.0]\n",
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" ['Germany' 50.0 83000.0]\n",
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" ['France' 37.0 67000.0]]\n",
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"Y\n",
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"['No' 'Yes' 'No' 'No' 'Yes' 'Yes' 'No' 'Yes' 'No' 'Yes']\n"
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]
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}
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],
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"source": [
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"dataset = pd.read_csv('../datasets/Data.csv')\n",
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"X = dataset.iloc[ : , :-1].values\n",
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"Y = dataset.iloc[ : , 3].values\n",
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"print(\"Step 2: Importing dataset\")\n",
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"print(\"X\")\n",
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"print(X)\n",
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"print(\"Y\")\n",
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"print(Y)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 第三步：处理丢失数据\n",
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"我们得到的数据很少是完整的。数据可能因为各种原因丢失，为了不降低机器学习模型的性能，需要处理数据。我们可以用整列的平均值或中间值替换丢失的数据。我们用sklearn.preprocessing库中的Imputer类完成这项任务。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"---------------------\n",
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"Step 3: Handling the missing data\n",
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"step2\n",
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"X\n",
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"[['France' 44.0 72000.0]\n",
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" ['Spain' 27.0 48000.0]\n",
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" ['Germany' 30.0 54000.0]\n",
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" ['Spain' 38.0 61000.0]\n",
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" ['Germany' 40.0 63777.77777777778]\n",
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" ['France' 35.0 58000.0]\n",
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" ['Spain' 38.77777777777778 52000.0]\n",
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" ['France' 48.0 79000.0]\n",
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" ['Germany' 50.0 83000.0]\n",
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" ['France' 37.0 67000.0]]\n"
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]
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}
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],
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"source": [
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"from sklearn.preprocessing import Imputer\n",
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"imputer = Imputer(missing_values = \"NaN\", strategy = \"mean\", axis = 0)\n",
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"imputer = imputer.fit(X[ : , 1:3])\n",
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"X[ : , 1:3] = imputer.transform(X[ : , 1:3])\n",
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"print(\"---------------------\")\n",
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"print(\"Step 3: Handling the missing data\")\n",
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"print(\"step2\")\n",
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"print(\"X\")\n",
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"print(X)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 第四步：解析分类数据\n",
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"分类数据指的是含有标签值而不是数字值的变量。取值范围通常是固定的。例如\"Yes\"和\"No\"不能用于模型的数学计算，所以需要解析成数字。为实现这一功能，我们从sklearn.preprocessing库导入LabelEncoder类。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"---------------------\n",
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"Step 4: Encoding categorical data\n",
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"X\n",
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"[[ 1.00000000e+00 0.00000000e+00 0.00000000e+00 4.40000000e+01\n",
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" 7.20000000e+04]\n",
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" [ 0.00000000e+00 0.00000000e+00 1.00000000e+00 2.70000000e+01\n",
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" 4.80000000e+04]\n",
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" [ 0.00000000e+00 1.00000000e+00 0.00000000e+00 3.00000000e+01\n",
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" 5.40000000e+04]\n",
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" [ 0.00000000e+00 0.00000000e+00 1.00000000e+00 3.80000000e+01\n",
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" 6.10000000e+04]\n",
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" [ 0.00000000e+00 1.00000000e+00 0.00000000e+00 4.00000000e+01\n",
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" 6.37777778e+04]\n",
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" [ 1.00000000e+00 0.00000000e+00 0.00000000e+00 3.50000000e+01\n",
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" 5.80000000e+04]\n",
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" [ 0.00000000e+00 0.00000000e+00 1.00000000e+00 3.87777778e+01\n",
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" 5.20000000e+04]\n",
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" [ 1.00000000e+00 0.00000000e+00 0.00000000e+00 4.80000000e+01\n",
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" 7.90000000e+04]\n",
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" [ 0.00000000e+00 1.00000000e+00 0.00000000e+00 5.00000000e+01\n",
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" 8.30000000e+04]\n",
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" [ 1.00000000e+00 0.00000000e+00 0.00000000e+00 3.70000000e+01\n",
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" 6.70000000e+04]]\n",
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"Y\n",
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"[0 1 0 0 1 1 0 1 0 1]\n"
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]
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}
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],
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"source": [
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"from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
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"labelencoder_X = LabelEncoder()\n",
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"X[ : , 0] = labelencoder_X.fit_transform(X[ : , 0])\n",
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"#Creating a dummy variable\n",
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"onehotencoder = OneHotEncoder(categorical_features = [0])\n",
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"X = onehotencoder.fit_transform(X).toarray()\n",
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"labelencoder_Y = LabelEncoder()\n",
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"Y = labelencoder_Y.fit_transform(Y)\n",
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"print(\"---------------------\")\n",
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"print(\"Step 4: Encoding categorical data\")\n",
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"print(\"X\")\n",
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"print(X)\n",
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"print(\"Y\")\n",
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"print(Y)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 第五步：拆分数据集为测试集合和训练集合\n",
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"把数据集拆分成两个：一个是用来训练模型的训练集合，另一个是用来验证模型的测试集合。两者比例一般是80:20。我们导入sklearn.model_selection库中的train_test_split()方法。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"---------------------\n",
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"Step 5: Splitting the datasets into training sets and Test sets\n",
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"X_train\n",
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"[[ 0.00000000e+00 1.00000000e+00 0.00000000e+00 4.00000000e+01\n",
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" 6.37777778e+04]\n",
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" [ 1.00000000e+00 0.00000000e+00 0.00000000e+00 3.70000000e+01\n",
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" 6.70000000e+04]\n",
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" [ 0.00000000e+00 0.00000000e+00 1.00000000e+00 2.70000000e+01\n",
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" 4.80000000e+04]\n",
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" [ 0.00000000e+00 0.00000000e+00 1.00000000e+00 3.87777778e+01\n",
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" 5.20000000e+04]\n",
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" [ 1.00000000e+00 0.00000000e+00 0.00000000e+00 4.80000000e+01\n",
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" 7.90000000e+04]\n",
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" [ 0.00000000e+00 0.00000000e+00 1.00000000e+00 3.80000000e+01\n",
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" 6.10000000e+04]\n",
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" [ 1.00000000e+00 0.00000000e+00 0.00000000e+00 4.40000000e+01\n",
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" 7.20000000e+04]\n",
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" [ 1.00000000e+00 0.00000000e+00 0.00000000e+00 3.50000000e+01\n",
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" 5.80000000e+04]]\n",
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"X_test\n",
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"[[ 0.00000000e+00 1.00000000e+00 0.00000000e+00 3.00000000e+01\n",
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" 5.40000000e+04]\n",
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" [ 0.00000000e+00 1.00000000e+00 0.00000000e+00 5.00000000e+01\n",
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" 8.30000000e+04]]\n",
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"Y_train\n",
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"[1 1 1 0 1 0 0 1]\n",
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"Y_test\n",
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"[0 0]\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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}
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],
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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"X_train, X_test, Y_train, Y_test = train_test_split( X , Y , test_size = 0.2, random_state = 0)\n",
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"print(\"---------------------\")\n",
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"print(\"Step 5: Splitting the datasets into training sets and Test sets\")\n",
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"print(\"X_train\")\n",
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"print(X_train)\n",
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"print(\"X_test\")\n",
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"print(X_test)\n",
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"print(\"Y_train\")\n",
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"print(Y_train)\n",
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"print(\"Y_test\")\n",
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"print(Y_test)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 第六步：特征量化\n",
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"大部分模型算法使用两点间的欧氏距离表示，但此特征在幅度、单位和范围姿态问题上变化很大。在距离计算中，高幅度的特征比低幅度特征权重更大。可用特征标准化或Z值归一化解决。导入sklearn.preprocessing库的StandardScalar类。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"---------------------\n",
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"Step 6: Feature Scaling\n",
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"X_train\n",
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"[[-1. 2.64575131 -0.77459667 0.26306757 0.12381479]\n",
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" [ 1. -0.37796447 -0.77459667 -0.25350148 0.46175632]\n",
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" [-1. -0.37796447 1.29099445 -1.97539832 -1.53093341]\n",
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" [-1. -0.37796447 1.29099445 0.05261351 -1.11141978]\n",
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" [ 1. -0.37796447 -0.77459667 1.64058505 1.7202972 ]\n",
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" [-1. -0.37796447 1.29099445 -0.0813118 -0.16751412]\n",
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" [ 1. -0.37796447 -0.77459667 0.95182631 0.98614835]\n",
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" [ 1. -0.37796447 -0.77459667 -0.59788085 -0.48214934]]\n",
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"X_test\n",
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"[[ 0. 0. 0. -1. -1.]\n",
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" [ 0. 0. 0. 1. 1.]]\n"
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]
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}
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],
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"source": [
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"from sklearn.preprocessing import StandardScaler\n",
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"sc_X = StandardScaler()\n",
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"X_train = sc_X.fit_transform(X_train)\n",
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"X_test = sc_X.fit_transform(X_test)\n",
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"print(\"---------------------\")\n",
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"print(\"Step 6: Feature Scaling\")\n",
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"print(\"X_train\")\n",
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"print(X_train)\n",
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"print(\"X_test\")\n",
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"print(X_test)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<b>完整的项目请前往Github项目<a href=\"https://github.com/MachineLearning100/100-Days-Of-ML-Code\">100-Days-Of-ML-Code</a>查看。有任何的建议或者意见欢迎在issue中提出~</b>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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