机器学习之过滤式特征选择

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• 机器学习之过滤式特征选择

# -*- coding: utf-8 -*-
"""
Created on Sat Dec  8 16:58:09 2018

@author: muli
"""

from sklearn.feature_selection import  VarianceThreshold,SelectKBest,f_classif,SelectPercentile

def test_VarianceThreshold():
    '''
    测试 VarianceThreshold  的用法

    :return:  None
    '''
    X=[[100,1,2,3],
       [100,4,5,6],
       [100,7,8,9],
       [101,11,12,13]]
    selector=VarianceThreshold(1)
    selector.fit(X)
    print("Variances is %s"%selector.variances_)
    print("After transform is %s"%selector.transform(X))
    print("The surport is %s"%selector.get_support(True))
    print("After reverse transform is %s"%
            selector.inverse_transform(selector.transform(X)))
    

def test_SelectKBest():
    '''
    测试 SelectKBest  的用法，其中考察的特征指标是 f_classif

    :return:  None
    '''
    X=[   [1,2,3,4,5],
          [5,4,3,2,1],
          [3,3,3,3,3,],
          [1,1,1,1,1] ]
    y=[0,1,0,1]
    print("before transform:\n",X)
    selector=SelectKBest(score_func=f_classif,k=3)
    selector.fit(X,y)
    print("scores_:",selector.scores_)
    print("pvalues_:",selector.pvalues_)
    print("selected index:",selector.get_support(True))
    print("after transform:",selector.transform(X))
    

def test_SelectPercentile():
    '''
    测试 SelectPercentile  的用法，其中考察的特征指标是 f_classif

    :retu
    '''
    X=[   [1,2,3,4,5],
          [5,4,3,2,1],
          [3,3,3,3,3,],
          [1,1,1,1,1] ]
    y=[0,1,0,1]
    print("before transform:\n",X)
    selector=SelectPercentile(score_func=f_classif,percentile=60)
    selector.fit(X,y)
    print("scores_:",selector.scores_)
    print("pvalues_:",selector.pvalues_)
    print("selected index:",selector.get_support(True))
    print("after transform:",selector.transform(X))
    
    
    
if __name__=='__main__':
#    test_VarianceThreshold() # 调用 test_VarianceThreshold
#    test_SelectKBest() # 调用 test_SelectKBest
    test_SelectPercentile()