用python实现求信息增益，进行特征选择。（可以同时适用于二值离散型和连续型的属性）

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]