python的数据处理_基于python的数据处理

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”)

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