数据预处理是机器学习中最基础也最麻烦的一部分内容
在我们把精力扑倒各种算法的推导之前，最应该做的就是把数据预处理先搞定
在之后的每个算法实现和案例练手过程中，这一步都必不可少
同学们也不要嫌麻烦，动起手来吧
基础比较好的同学也可以温故知新，再练习一下哈
闲言少叙，下面我们六步完成数据预处理
其实我感觉这里少了一步：观察数据
![此处输入图片的描述][1]
这是十组国籍、年龄、收入、是否已购买的数据
有分类数据，有数值型数据，还有一些缺失值
看起来是一个分类预测问题
根据国籍、年龄、收入来预测是够会购买
OK，有了大体的认识，开始表演。
Step 1：导入库
import numpy as np
import pandas as pd
Step 2：导入数据集
dataset = pd.read_csv('Data.csv')
X = dataset.iloc[ : , :-1].values
Y = dataset.iloc[ : , 3].values
print("X")
print(X)
print("Y")
print(Y)
这一步的目的是将自变量和因变量拆成一个矩阵和一个向量。
结果如下
X
[['France' 44.0 72000.0]
['Spain' 27.0 48000.0]
['Germany' 30.0 54000.0]
['Spain' 38.0 61000.0]
['Germany' 40.0 nan]
['France' 35.0 58000.0]
['Spain' nan 52000.0]
['France' 48.0 79000.0]
['Germany' 50.0 83000.0]
['France' 37.0 67000.0]]
Y
['No' 'Yes' 'No' 'No' 'Yes' 'Yes' 'No' 'Yes' 'No' 'Yes']
Step 3：处理缺失数据
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values = "NaN", strategy = "mean", axis = 0)
imputer = imputer.fit(X[ : , 1:3])
X[ : , 1:3] = imputer.transform(X[ : , 1:3])
Imputer类具体用法移步
http://scikit-learn.org/stable/modules/preprocessing.html#preprocessing
本例中我们用的是均值替代法填充缺失值
运行结果如下
Step 3: Handling the missing data
step2
X
[['France' 44.0 72000.0]
['Spain' 27.0 48000.0]
['Germany' 30.0 54000.0]
['Spain' 38.0 61000.0]
['Germany' 40.0 63777.77777777778]
['France' 35.0 58000.0]
['Spain' 38.77777777777778 52000.0]
['France' 48.0 79000.0]
['Germany' 50.0 83000.0]
['France' 37.0 67000.0]]
Step 4：把分类数据转换为数字
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[ : , 0] = labelencoder_X.fit_transform(X[ : , 0])
onehotencoder = OneHotEncoder(categorical_features = [0])
X = onehotencoder.fit_transform(X).toarray()
labelencoder_Y = LabelEncoder()
Y = labelencoder_Y.fit_transform(Y)
print("X")
print(X)
print("Y")
print(Y)
LabelEncoder用法请移步
http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html
X
[[1.00000000e+00 0.00000000e+00 0.00000000e+00 4.40000000e+01
7.20000000e+04]
[0.00000000e+00 0.00000000e+00 1.00000000e+00 2.70000000e+01
4.80000000e+04]
[0.00000000e+00 1.00000000e+00 0.00000000e+00 3.00000000e+01
5.40000000e+04]
[0.00000000e+00 0.00000000e+00 1.00000000e+00 3.80000000e+01
6.10000000e+04]
[0.00000000e+00 1.00000000e+00 0.00000000e+00 4.00000000e+01
6.37777778e+04]
[1.00000000e+00 0.00000000e+00 0.00000000e+00 3.50000000e+01
5.80000000e+04]
[0.00000000e+00 0.00000000e+00 1.00000000e+00 3.87777778e+01
5.20000000e+04]
[1.00000000e+00 0.00000000e+00 0.00000000e+00 4.80000000e+01
7.90000000e+04]
[0.00000000e+00 1.00000000e+00 0.00000000e+00 5.00000000e+01
8.30000000e+04]
[1.00000000e+00 0.00000000e+00 0.00000000e+00 3.70000000e+01
6.70000000e+04]]
Y
[0 1 0 0 1 1 0 1 0 1]
Step 5：将数据集分为训练集和测试集
from sklearn.cross_validation import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split( X , Y , test_size = 0.2, random_state = 0)
X_train
[[0.00000000e+00 1.00000000e+00 0.00000000e+00 4.00000000e+01
6.37777778e+04]
[1.00000000e+00 0.00000000e+00 0.00000000e+00 3.70000000e+01
6.70000000e+04]
[0.00000000e+00 0.00000000e+00 1.00000000e+00 2.70000000e+01
4.80000000e+04]
[0.00000000e+00 0.00000000e+00 1.00000000e+00 3.87777778e+01
5.20000000e+04]
[1.00000000e+00 0.00000000e+00 0.00000000e+00 4.80000000e+01
7.90000000e+04]
[0.00000000e+00 0.00000000e+00 1.00000000e+00 3.80000000e+01
6.10000000e+04]
[1.00000000e+00 0.00000000e+00 0.00000000e+00 4.40000000e+01
7.20000000e+04]
[1.00000000e+00 0.00000000e+00 0.00000000e+00 3.50000000e+01
5.80000000e+04]]
X_test
[[0.0e+00 1.0e+00 0.0e+00 3.0e+01 5.4e+04]
[0.0e+00 1.0e+00 0.0e+00 5.0e+01 8.3e+04]]
step2
Y_train
[1 1 1 0 1 0 0 1]
Y_test
[0 0]
Step 6：特征缩放
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
大多数机器学习算法在计算中使用两个数据点之间的欧氏距离
特征在幅度、单位和范围上很大的变化,这引起了问题
高数值特征在距离计算中的权重大于低数值特征
通过特征标准化或Z分数归一化来完成
导入sklearn.preprocessing 库中的StandardScala
用法：http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
X_train
[[-1. 2.64575131 -0.77459667 0.26306757 0.12381479]
[ 1. -0.37796447 -0.77459667 -0.25350148 0.46175632]
[-1. -0.37796447 1.29099445 -1.97539832 -1.53093341]
[-1. -0.37796447 1.29099445 0.05261351 -1.11141978]
[ 1. -0.37796447 -0.77459667 1.64058505 1.7202972 ]
[-1. -0.37796447 1.29099445 -0.0813118 -0.16751412]
[ 1. -0.37796447 -0.77459667 0.95182631 0.98614835]
[ 1. -0.37796447 -0.77459667 -0.59788085 -0.48214934]]
X_test
[[-1. 2.64575131 -0.77459667 -1.45882927 -0.90166297]
[-1. 2.64575131 -0.77459667 1.98496442 2.13981082]]
