k近邻算法-5.数据归一化

数据归一化（Feature Scaling）

多个特征值时，其中某个特征数量级比较大，其他特征较小时，分类结果会被特征值所主导，而弱化了其他特征的影响，这是各个特征值的量纲不同所致，需要将数据归一化处理

如上图所示，样本间的距离，被发现时间所主导
解决办法：将所有的数据映射到同一尺度

方法一：最值归一化

把所有的数据映射到0-1之间，适用于有明显的边界，受outlier极值影响较大，比如收入的分布

import numpy 
import matplotlib.pyplot as plt
x = numpy.random.randint(0,100,(50,4))
x = np.array(x,dtype=float)
for i in range(x.shape[1]):
    x[:,i] = (x[:,i] - np.min(x[:,i])) / (np.max(x[:,i]) - np.min(x[:,i]))

plt.scatter(x[:,0],x[:,1])
plt.show()

归一化后的平均值和方差：

方法二：均值方差归一化

把所有数据归一化到均值为0，方差为1的分布中，适用于数据分布没有明显的边界，存在极端数据值的数据集

import numpy 
import matplotlib.pyplot as plt
x = numpy.random.randint(0,100,(50,4))
x = numpy.array(x,dtype=float)
for i in range(x.shape[1]):
    x[:,i] = (x[:,i] - numpy.mean(x[:,i])) / numpy.std(x[:,i])

plt.scatter(x[:,0],x[:,1])
plt.show()

归一化后的平均值和方差：

scikit-learn 中的 StandardScaler

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

iris = load_iris()
x = iris.data
y = iris.target
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2)

从预处理模块导入标准 标量器

from sklearn.preprocessing import StandardScaler
standardScaler = StandardScaler()
standardScaler.fit(x_train)

x_train = standardScaler.transform(x_train)
x_test = standardScaler.transform(x_test)

from sklearn.neighbors import KNeighborsClassifier
knn_clf = KNeighborsClassifier(n_neighbors=3)
knn_clf.fit(x_train,y_train)
knn_clf.score(x_test,y_test)

自己封装的StandardScaler

预处理文件

import numpy as np

class StandardScaler:
	def __init__(self):
		self.mean_ = None
		self.scale_ = None

	#求出传入的x数据集的每一列的均值和标准差
	def fit(self, x):
		assert x.ndim == 2, "the dimension of x must be 2"

		self.mean_ = np.array([np.mean(x[:,i]) for i in range(x.shape[1])])
		self.scale_ = np.array([np.std(x[:,i]) for i in range(x.shape[1])])
		return self
		
	#对x进行数据归一化
	def transform(self, x):
		assert x.ndim == 2, "the dimension of x must be 2"
		assert self.mean_ is not None and self.scale_ is not None,
			"must fit before transform!"
		assert x.shape[1] == len(self.mean_), 
			"the feature number os x must be equal to mean_ and std_"

		res_x = np.empty(shape=x.shape, dtype=float)
		for col in range(x.shape[1]):
			res_x[:,col] = (x[:,col] - self.mean_[col]) / self.scale_[col]
		return res_x

调用封装的预处理库

from mylib import preprocessing
standScaler = preprocessing.StandardScaler() #创建一个均值方差归一化的类
standScaler.fit(x_train)  #找出样本的均值和标准差

x_train = standScaler.transform(x_train)  #根据均值，标准差，求出归一化的值
x_test = standScaler.transform(x_test)    #针对测试数据，也是一样用训练集的均值，标准差
knn = KNeighborsClassifier(n_neighbors=3)  #knn算法分类器
knn.fit(x_train,y_train)    
knn.score(x_test,y_test)