宜配屋

源起：

1.我要做交叉验证，需要每个训练集和测试集都保持相同的样本分布比例，直接用sklearn提供的KFold并不能满足这个需求。

2.将生成的交叉验证数据集保存成CSV文件，而不是直接用sklearn训练分类模型。

3.在编码过程中有一的误区需要注意：

这个sklearn官方给出的文档

>>> import numpy as np
>>> from sklearn.model_selection import KFold
 
>>> X = ["a", "b", "c", "d"]
>>> kf = KFold(n_splits=2)
>>> for train, test in kf.split(X):
...  print("%s %s" % (train, test))
[2 3] [0 1]
[0 1] [2 3]

我之前犯的一个错误是将train，test理解成原数据集分割成子数据集之后的子数据集索引。而实际上，它就是原始数据集本身的样本索引。

源码：

# -*- coding:utf-8 -*-
# 得到交叉验证数据集，保存成CSV文件
# 输入是一个包含正常恶意标签的完整数据集，在读数据的时候分开保存到datasetBenign，datasetMalicious
# 分别对两个数据集进行KFold，最后合并保存
 
from sklearn.model_selection import KFold
import csv
 
def writeInFile(benignKFTrain, benignKFTest, maliciousKFTrain, maliciousKFTest, i, datasetBenign, datasetMalicious):
 newTrainFilePath = "E:\\hadoopExperimentResult\\5KFold\\AllDataSetIIR10\\dataset\\ImbalancedAllTraffic-train-%s.csv" % i
 newTestFilePath = "E:\\hadoopExperimentResult\\5KFold\\AllDataSetIIR10\\dataset\\IImbalancedAllTraffic-test-%s.csv" % i
 newTrainFile = open(newTrainFilePath, "wb")# wb 为防止空行
 newTestFile = open(newTestFilePath, "wb")
 writerTrain = csv.writer(newTrainFile)
 writerTest = csv.writer(newTestFile)
 for index in benignKFTrain:
  writerTrain.writerow(datasetBenign[index])
 for index in benignKFTest:
  writerTest.writerow(datasetBenign[index])
 for index in maliciousKFTrain:
  writerTrain.writerow(datasetMalicious[index])
 for index in maliciousKFTest:
  writerTest.writerow(datasetMalicious[index])
 newTrainFile.close()
 newTestFile.close()
 
 
def getKFoldDataSet(datasetPath):
 # CSV读取文件
 # 开始从文件中读取全部的数据集
 datasetFile = file(datasetPath, 'rb')
 datasetBenign = []
 datasetMalicious = []
 readerDataset = csv.reader(datasetFile)
 for line in readerDataset:
  if len(line) > 1:
   curLine = []
   curLine.append(float(line[0]))
   curLine.append(float(line[1]))
   curLine.append(float(line[2]))
   curLine.append(float(line[3]))
   curLine.append(float(line[4]))
   curLine.append(float(line[5]))
   curLine.append(float(line[6]))
   curLine.append(line[7])
   if line[7] == "benign":
    datasetBenign.append(curLine)
   else:
    datasetMalicious.append(curLine)
 
 # 交叉验证分割数据集
 K = 5
 kf = KFold(n_splits=K)
 benignKFTrain = []; benignKFTest = []
 for train,test in kf.split(datasetBenign):
  benignKFTrain.append(train)
  benignKFTest.append(test)
 maliciousKFTrain=[]; maliciousKFTest=[]
 for train,test in kf.split(datasetMalicious):
  maliciousKFTrain.append(train)
  maliciousKFTest.append(test)
 for i in range(K):
  print "======================== "+ str(i)+ " ========================"
  print benignKFTrain[i], benignKFTest[i]
  print maliciousKFTrain[i],maliciousKFTest[i]
  writeInFile(benignKFTrain[i], benignKFTest[i], maliciousKFTrain[i], maliciousKFTest[i], i, datasetBenign,
     datasetMalicious)
 
 datasetFile.close()
 
 
if __name__ == "__main__":
 
 getKFoldDataSet(r"E:\hadoopExperimentResult\5KFold\AllDataSetIIR10\dataset\ImbalancedAllTraffic-10.csv")

以上这篇Python sklearn KFold 生成交叉验证数据集的方法就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持【听图阁-专注于Python设计】。