下面代码修改自： vbaymax-特征择算法之ReliefF算法python实现
太多人私信我要这份python 3.x的代码了。
所以干脆发一篇博客，需要的请自取。
只需要代码的直接复制下面代码
需要 数据和代码 的请到
链接：https://share.weiyun.com/7sdVMZab
密码：
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#!/usr/bin/env python
# -*- coding:utf-8 -*-
#@Time : 2019/10/29 0029 9:12
#@Author : tb_youth
#@FileName: RTest.py
#@SoftWare: PyCharm
#@Blog : https://blog.csdn.net/tb_youth
import pandas as pd
import numpy as np
import numpy.linalg as la
import random
import csv
'''
适用于多分类问题
'''
class Relief:
def __init__(self, data_df, sample_rate, t, k):
"""
#
:param data_df: 数据框（字段为特征，行为样本）
:param sample_rate: 抽样比例
:param t: 统计量分量阈值
:param k: k近邻的个数
"""
self.__data = data_df
self.__feature = data_df.columns
self.__sample_num = int(round(len(data_df) * sample_rate))
self.__t = t
self.__k = k
# 数据处理（将离散型数据处理成连续型数据，比如字符到数值）
def get_data(self):
new_data = pd.DataFrame()
for one in self.__feature[:-1]:
col = self.__data[one]
if (str(list(col)[0]).split(".")[0]).isdigit() or str(list(col)[0]).isdigit() or (str(list(col)[0]).split('-')[-1]).split(".")[-1].isdigit():
new_data[one] = self.__data[one]
# print('%s 是数值型' % one)
else:
# print('%s 是离散型' % one)
keys = list(set(list(col)))
values = list(range(len(keys)))
new = dict(zip(keys, values))
new_data[one] = self.__data[one].map(new)
new_data[self.__feature[-1]] = self.__data[self.__feature[-1]]
return new_data
# 返回一个样本的k个猜中近邻和其他类的k个猜错近邻
def get_neighbors(self, row):
df = self.get_data()
row_type = row[df.columns[-1]]
right_df = df[df[df.columns[-1]] == row_type].drop(columns=[df.columns[-1]])
aim = row.drop(df.columns[-1])
f = lambda x: eulidSim(np.mat(x), np.mat(aim))
right_sim = right_df.apply(f, axis=1)
right_sim_two = right_sim.drop(right_sim.idxmin())
right = dict()
right[row_type] = list(right_sim_two.sort_values().index[0:self.__k])
# print list(right_sim_two.sort_values().index[0:self.__k])
lst = [row_type]
types = list(set(df[df.columns[-1]]) - set(lst))
wrong = dict()
for one in types:
wrong_df = df[df[df.columns[-1]] == one].drop(columns=[df.columns[-1]])
wrong_sim = wrong_df.apply(f, axis=1)
wrong[one] = list(wrong_sim.sort_values().index[0:self.__k])
print(right, wrong)
return right, wrong
# 计算特征权重
def get_weight(self, feature, index, NearHit, NearMiss):
# data = self.__data.drop(self.__feature[-1], axis=1)
data = self.__data
row = data.iloc[index]
right = 0
print('####:',NearHit.values())
for one in list(NearHit.values())[0]:
nearhit = data.iloc[one]
if (str(row[feature]).split(".")[0]).isdigit() or str(row[feature]).isdigit() or (str(row[feature]).split('-')[-1]).split(".")[-1].isdigit():
max_feature = data[feature].max()
min_feature = data[feature].min()
right_one = pow(round(abs(row[feature] - nearhit[feature]) / (max_feature - min_feature), 2), 2)
else:
print('@@:',row[feature])
print('$$:',nearhit[feature])
print('-'*100)
right_one = 0 if row[feature] == nearhit[feature] else 1
right += right_one
right_w = round(right / self.__k, 2)
wrong_w = 0
# 样本row所在的种类占样本集的比例
p_row = round(float(list(data[data.columns[-1]]).count(row[data.columns[-1]])) / len(data), 2)
for one in NearMiss.keys():
# 种类one在样本集中所占的比例
p_one = round(float(list(data[data.columns[-1]]).count(one)) / len(data), 2)
wrong_one = 0
for i in NearMiss[one]:
nearmiss = data.iloc[i]
if (str(row[feature]).split(".")[0]).isdigit() or str(row[feature]).isdigit() or (str(row[feature]).split('-')[-1]).split(".")[-1].isdigit():
max_feature = data[feature].max()
min_feature = data[feature].min()
wrong_one_one = pow(round(abs(row[feature] - nearmiss[feature]) / (max_feature - min_feature), 2), 2)
else:
wrong_one_one = 0 if row[feature] == nearmiss[feature] else 1
wrong_one += wrong_one_one
wrong = round(p_one / (1 - p_row) * wrong_one / self.__k, 2)
wrong_w += wrong
w = wrong_w - right_w
return w
# 过滤式特征选择
def reliefF(self):
sample = self.get_data()
# print sample
m, n = np.shape(self.__data) # m为行数，n为列数
score = []
sample_index = random.sample(range(0, m), self.__sample_num)
print('采样样本索引为 %s ' % sample_index)
num = 1
for i in sample_index: # 采样次数
one_score = dict()
row = sample.iloc[i]
NearHit, NearMiss = self.get_neighbors(row)
print('第 %s 次采样，样本index为 %s，其NearHit k近邻行索引为 %s ，NearMiss k近邻行索引为 %s' % (num, i, NearHit, NearMiss))
for f in self.__feature[0:-1]:
print('***:',f,i,NearHit,NearMiss)
w = self.get_weight(f, i, NearHit, NearMiss)
one_score[f] = w
print('特征 %s 的权重为 %s.' % (f, w))
score.append(one_score)
num += 1
f_w = pd.DataFrame(score)
print('采样各样本特征权重如下：')
print( f_w)
print('平均特征权重如下：')
print(f_w.mean())
return f_w.mean()
# 返回最终选取的特征
def get_final(self):
f_w = pd.DataFrame(self.reliefF(), columns=['weight'])
final_feature_t = f_w[f_w['weight'] > self.__t]
print('*'*100)
print(final_feature_t)
# final_feature_k = f_w.sort_values('weight').head(self.__k)
# print final_feature_k
return final_feature_t
# 几种距离求解
#欧氏距离(Euclidean Distance)
def eulidSim(vecA, vecB):
return la.norm(vecA - vecB)
#余弦相似度
def cosSim(vecA, vecB):
"""
:param vecA: 行向量
:param vecB: 行向量
:return: 返回余弦相似度（范围在0-1之间）
"""
num = float(vecA * vecB.T)
denom = la.norm(vecA) * la.norm(vecB)
cosSim = 0.5 + 0.5 * (num / denom)
return cosSim
#皮尔逊(皮尔森)相关系数
'''
皮尔森相关系数也称皮尔森积矩相关系数(Pearson product-moment correlation coefficient) ，
是一种线性相关系数，
是最常用的一种相关系数。
记为r，用来反映两个变量X和Y的线性相关程度，
r值介于-1到1之间，绝对值越大表明相关性越强。
'''
def pearsSim(vecA, vecB):
if len(vecA) < 3:
return 1.0
else:
return 0.5 + 0.5 * np.corrcoef(vecA, vecB,rowvar=0)[0][1]
if __name__ == '__main__':
with open('./西瓜数据集30.csv','r',encoding= 'gbk') as f:
data = pd.read_csv(f)[['色泽', '根蒂', '敲声', '纹理', '脐部', '触感', '密度', '含糖率', '好瓜']]
#print(type(data))
# print(data)
# f_csv = csv.reader(f)
# for row in f_csv:
# print(row)
f = Relief(data, 1, 0.2, 2)
# df = f.get_data()
# print(type(df.iloc[0]))
# f.get_neighbors(df.iloc[0])
f.reliefF()
f.get_final()
本文地址：https://blog.csdn.net/tb_youth/article/details/107450065
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