1.      实验要求：

对垃圾邮件分类算法(书上P66)改进：

1、采用词袋模型

2、随机选择15个测试样本

3、去除长度小于3的字符

2.      垃圾邮件分类算法改进点

defbagOfWords2VecMN(vocabList, inputSet):

returnVec = [0]*len(vocabList)

for word in inputSet:

if word in vocabList:

returnVec[vocabList.index(word)] += 1

return returnVec

deftextParse(bigString):    #input is bigstring, #output is word list

import re

listOfTokens = re.split(r'\W*', bigString)

return [tok.lower() for tok in listOfTokensif len(tok) > 3]

def spamTest():

docList=[]; classList = []; fullText =[]

for i in range(1,26):

wordList =textParse(open('email/spam/%d.txt' % i).read())

docList.append(wordList)

fullText.extend(wordList)

classList.append(1)

wordList =textParse(open('email/ham/%d.txt' % i).read())

docList.append(wordList)

fullText.extend(wordList)

classList.append(0)

vocabList = createVocabList(docList)#createvocabulary

trainingSet = range(50); testSet=[]           #create test set

for i in range(15):

randIndex =int(random.uniform(0,len(trainingSet)))

testSet.append(trainingSet[randIndex])

del(trainingSet[randIndex])

trainMat=[]; trainClasses = []

for docIndex in trainingSet:#train theclassifier (get probs) trainNB0

trainMat.append(bagOfWords2VecMN(vocabList,docList[docIndex]))

trainClasses.append(classList[docIndex])

p0V,p1V,pSpam =trainNB0(array(trainMat),array(trainClasses))

errorCount = 0

for docIndex in testSet:        #classify the remaining items

wordVector =bagOfWords2VecMN(vocabList, docList[docIndex])

ifclassifyNB(array(wordVector),p0V,p1V,pSpam) != classList[docIndex]:

errorCount += 1

print "classificationerror",docList[docIndex]

print 'the error rate is:',float(errorCount)/len(testSet)

#return vocabList,fullText

3.      改进点说明

上述代码中将改进点高亮标注。

词袋模型算法说明

文档词袋模型是词集模型的改进算法。

词集模型仅仅将每个词出现与否作为一个特征；词袋模型考虑到如果一个词出现多次，其中包含的信息。

bagOfWords2VecMN函数中，针对属于inputSet的word没出现一次进行计数，函数返回计数值。

4.      测试分类器

>>>import os

>>>os.getcwd()

'C:\\Python27\\MLIA\\Ch04'

>>>import bayes

>>>bayes.spamTest()

classificationerror ['adobe', 'microsoft', 'softwares', 'fast', 'order', 'download','microsoft', 'office', 'professional', 'plus', '2007', '2010', 'microsoft','windows', 'ultimate', 'adobe', 'photoshop', 'extended', 'adobe', 'acrobat','extended', 'windows', 'professional', 'thousand', 'more', 'titles']

the error rateis:  0.0666666666667

>>>bayes.spamTest()

classificationerror ['yeah', 'ready', 'here', 'because', 'plane', 'tickets', 'germany']

classificationerror ['benoit', 'mandelbrot', '1924', '2010', 'benoit', 'mandelbrot', '1924','2010', 'wilmott', 'team', 'benoit', 'mandelbrot', 'mathematician', 'father','fractal', 'mathematics', 'advocate', 'more', 'sophisticated', 'modelling','quantitative', 'finance', 'died', '14th', 'october', '2010', 'aged','wilmott', 'magazine', 'often', 'featured', 'mandelbrot', 'ideas', 'work','others', 'inspired', 'fundamental', 'insights', 'must', 'logged', 'view','these', 'articles', 'from', 'past', 'issues', 'wilmott', 'magazine']

the error rateis:  0.133333333333