源起：

1.我要做交叉驗證，需要每個訓練集和測試集都保持相同的樣本分布比例，直接用sklearn提供的KFold并不能滿足這個需求。

2.將生成的交叉驗證數據集保存成CSV文件，而不是直接用sklearn訓練分類模型。

3.在編碼過程中有一的誤區需要注意：

這個sklearn官方給出的文檔

>>> 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]

我之前犯的一個錯誤是將train，test理解成原數據集分割成子數據集之后的子數據集索引。而實際上，它就是原始數據集本身的樣本索引。

源碼：

# -*- coding:utf-8 -*-# 得到交叉驗證數據集，保存成CSV文件# 輸入是一個包含正常惡意標簽的完整數據集，在讀數據的時候分開保存到datasetBenign，datasetMalicious# 分別對兩個數據集進行KFold，最后合并保存 from sklearn.model_selection import KFoldimport 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 為防止空行 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讀取文件 # 開始從文件中讀取全部的數據集 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)  # 交叉驗證分割數據集 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")