python实现求特征选择的信息增益

使用python语言，实现求特征选择的信息增益，可以同时满足特征中有连续型和二值离散型属

使用python语言，实现求特征选择的信息增益，可以同时满足特征中有连续型和二值离散型属性的情况。

师兄让我做一个特征选择的代码，我在网上找了一下，大部分都是用来求离散型属性的信息益益，但是我的数据是同时包含二值离散型和连续型属性的，所以这里实现了一下。

代码块

import numpy as np
import math

class IG():
 def __init__(self,X,y):

 X = np.array(X)
 n_feature = np.shape(X)[1]
 n_y = len(y)

 orig_H = 0
 for i in set(y):
 orig_H += -(y.count(i)/n_y)*math.log(y.count(i)/n_y)

 condi_H_list = []
 for i in range(n_feature):
 feature = X[:,i]
 sourted_feature = sorted(feature)
 threshold = [(sourted_feature[inde-1]+sourted_feature[inde])/2 for inde in range(len(feature)) if inde != 0 ]

 thre_set = set(threshold)
 if float(max(feature)) in thre_set:
 thre_set.remove(float(max(feature)))
 if min(feature) in thre_set:
 thre_set.remove(min(feature))
 pre_H = 0
 for thre in thre_set:
 lower = [y[s] for s in range(len(feature)) if feature[s] < thre]
 highter = [y[s] for s in range(len(feature)) if feature[s] > thre]
 H_l = 0
 for l in set(lower):
 H_l += -(lower.count(l) / len(lower))*math.log(lower.count(l) / len(lower))
 H_h = 0
 for h in set(highter):
 H_h += -(highter.count(h) / len(highter))*math.log(highter.count(h) / len(highter))
 temp_condi_H = len(lower)/n_y *H_l+ len(highter)/n_y * H_h
 condi_H = orig_H - temp_condi_H
 pre_H = max(pre_H,condi_H)
 condi_H_list.append(pre_H)

 self.IG = condi_H_list


 def getIG(self):
 return self.IG

if __name__ == "__main__":


 X = [[1, 0, 0, 1],
 [0, 1, 1, 1],
 [0, 0, 1, 0]]
 y = [0, 0, 1]


 print(IG(X,y).getIG())

输出结果为：

[0.17441604792151594, 0.17441604792151594, 0.17441604792151594, 0.6365141682948128]

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python 特征选择 信息增益