《机器学习实战》中贝叶斯分类中导入RSS源例子

'''Created on Oct 19, 2010@author: Peter'''from numpy import *def loadDataSet():    postingList=[['my', 'dog', 'has', 'flea', 'problems', 'help','my','dog', 'please'],                 ['maybe', 'not', 'take', 'him', 'to', 'dog', 'park', 'stupid'],                 ['my', 'dalmation', 'is', 'so', 'cute', 'I', 'love', 'him'],                 ['stop', 'posting', 'stupid', 'worthless', 'garbage'],                 ['mr', 'licks', 'ate', 'my', 'steak', 'how', 'to', 'stop', 'him'],                 ['quit', 'buying', 'worthless', 'dog', 'food', 'stupid']]    classVec = [0,1,0,1,0,1]    #1 is abusive, 0 not    return postingList,classVec                 def createVocabList(dataSet):    vocabSet = set([])  #create empty set    for document in dataSet:        vocabSet = vocabSet | set(document) #union of the two sets    return list(vocabSet)def bagOfWords2Vec(vocabList, inputSet):    returnVec = [0]*len(vocabList)    for word in inputSet:        if word in vocabList:            returnVec[vocabList.index(word)] += 1        else: print "the word: %s is not in my Vocabulary!" % word    return returnVecdef trainNB0(trainMatrix,trainCategory):    numTrainDocs = len(trainMatrix)    numWords = len(trainMatrix[0])    pAbusive = sum(trainCategory)/float(numTrainDocs)    p0Num = ones(numWords); p1Num = ones(numWords)      #change to ones()     p0Denom = 2.0; p1Denom = 2.0                        #change to 2.0    for i in range(numTrainDocs):        if trainCategory[i] == 1:            p1Num += trainMatrix[i]            p1Denom += sum(trainMatrix[i])        else:            p0Num += trainMatrix[i]            p0Denom += sum(trainMatrix[i])    p1Vect = log(p1Num/p1Denom)          #change to log()    p0Vect = log(p0Num/p0Denom)          #change to log()    return p0Vect,p1Vect,pAbusivedef classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):    p1 = sum(vec2Classify * p1Vec) + log(pClass1)    #element-wise mult    p0 = sum(vec2Classify * p0Vec) + log(1.0 - pClass1)    if p1 > p0:        return 1    else:         return 0    def bagOfWords2VecMN(vocabList, inputSet):    returnVec = [0]*len(vocabList)    for word in inputSet:        if word in vocabList:            returnVec[vocabList.index(word)] += 1    return returnVecdef testingNB():    print '*** load dataset for training ***'    listOPosts,listClasses = loadDataSet()    print 'listOPost:/n',listOPosts    print 'listClasses:/n',listClasses    print '/n*** create Vocab List ***'    myVocabList = createVocabList(listOPosts)    print 'myVocabList:/n',myVocabList    print '/n*** Vocab show in post Vector Matrix ***'    trainMat=[]    for postinDoc in listOPosts:        trainMat.append(bagOfWords2Vec(myVocabList, postinDoc))    print 'train matrix:',trainMat    print '/n*** train P0V p1V pAb ***'    p0V,p1V,pAb = trainNB0(array(trainMat),array(listClasses))    print 'p0V:/n',p0V    print 'p1V:/n',p1V    print 'pAb:/n',pAb    print '/n*** classify ***'    testEntry = ['love', 'my', 'dalmation']    thisDoc = array(bagOfWords2Vec(myVocabList, testEntry))    print testEntry,'classified as: ',classifyNB(thisDoc,p0V,p1V,pAb)    testEntry = ['stupid', 'garbage']    thisDoc = array(bagOfWords2Vec(myVocabList, testEntry))    print testEntry,'classified as: ',classifyNB(thisDoc,p0V,p1V,pAb)def textParse(bigString):    #input is big string, #output is word list    import re    listOfTokens = re.split(r'/W*', bigString)    return [tok.lower() for tok in listOfTokens if len(tok) > 2]     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)#create vocabulary    trainingSet = range(50); testSet=[]           #create test set    for i in range(10):        randIndex = int(random.uniform(0,len(trainingSet)))        testSet.append(trainingSet[randIndex])        del(trainingSet[randIndex])      trainMat=[]; trainClasses = []    for docIndex in trainingSet:#train the classifier (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])        if classifyNB(array(wordVector),p0V,p1V,pSpam) != classList[docIndex]:            errorCount += 1            print "classification error",docList[docIndex]    print 'the error rate is: ',float(errorCount)/len(testSet)    #return vocabList,fullTextdef calcMostFreq(vocabList,fullText):    import Operator    freqDict = {}    for token in vocabList:        freqDict[token]=fullText.count(token)    sortedFreq = sorted(freqDict.iteritems(), key=operator.itemgetter(1), reverse=True)     return sortedFreq[:30]       def stopWords():    import re    wordList =  open('stopword.txt').read() # see http://www.ranks.nl/stopwords    listOfTokens = re.split(r'/W*', wordList)    return [tok.lower() for tok in listOfTokens]     print 'read stop word from /'stopword.txt/':',listOfTokens    return listOfTokensdef localWords(feed1,feed0):    import feedparser    docList=[]; classList = []; fullText =[]    print 'feed1 entries length: ', len(feed1['entries']), '/nfeed0 entries length: ', len(feed0['entries'])    minLen = min(len(feed1['entries']),len(feed0['entries']))    print '/nmin Length: ', minLen    for i in range(minLen):        wordList = textParse(feed1['entries'][i]['summary'])        print '/nfeed1/'s entries[',i,']/'s summary - ','parse text:/n',wordList        docList.append(wordList)        fullText.extend(wordList)        classList.append(1) #NY is class 1        wordList = textParse(feed0['entries'][i]['summary'])        print '/nfeed0/'s entries[',i,']/'s summary - ','parse text:/n',wordList        docList.append(wordList)        fullText.extend(wordList)        classList.append(0)    vocabList = createVocabList(docList)#create vocabulary    print '/nVocabList is ',vocabList    print '/nRemove Stop Word:'    stopWordList = stopWords()    for stopWord in stopWordList:        if stopWord in vocabList:            vocabList.remove(stopWord)            print 'Removed: ',stopWord##    top30Words = calcMostFreq(vocabList,fullText)   #remove top 30 words##    print '/nTop 30 words: ', top30Words##    for pairW in top30Words:##        if pairW[0] in vocabList:##            vocabList.remove(pairW[0])##            print '/nRemoved: ',pairW[0]    trainingSet = range(2*minLen); testSet=[]           #create test set    print '/n/nBegin to create a test set: /ntrainingSet:',trainingSet,'/ntestSet',testSet    for i in range(5):        randIndex = int(random.uniform(0,len(trainingSet)))        testSet.append(trainingSet[randIndex])        del(trainingSet[randIndex])    print 'random select 5 sets as the testSet:/ntrainingSet:',trainingSet,'/ntestSet',testSet    trainMat=[]; trainClasses = []    for docIndex in trainingSet:#train the classifier (get probs) trainNB0        trainMat.append(bagOfWords2VecMN(vocabList, docList[docIndex]))        trainClasses.append(classList[docIndex])    print '/ntrainMat length:',len(trainMat)    print '/ntrainClasses',trainClasses    print '/n/ntrainNB0:'    p0V,p1V,pSpam = trainNB0(array(trainMat),array(trainClasses))    #print '/np0V:',p0V,'/np1V',p1V,'/npSpam',pSpam    errorCount = 0    for docIndex in testSet:        #classify the remaining items        wordVector = bagOfWords2VecMN(vocabList, docList[docIndex])        classifiedClass = classifyNB(array(wordVector),p0V,p1V,pSpam)        originalClass = classList[docIndex]        result =  classifiedClass != originalClass        if result:            errorCount += 1        print '/n',docList[docIndex],'/nis classified as: ',classifiedClass,', while the original class is: ',originalClass,'. --',not result    print '/nthe error rate is: ',float(errorCount)/len(testSet)    return vocabList,p0V,p1Vdef testRSS():    import feedparser    ny=feedparser.parse('http://www.nasa.gov/rss/dyn/image_of_the_day.rss')    sf=feedparser.parse('http://sports.yahoo.com/nba/teams/hou/rss.xml')    vocabList,pSF,pNY = localWords(ny,sf)def testTopWords():    import feedparser    ny=feedparser.parse('http://www.nasa.gov/rss/dyn/image_of_the_day.rss')    sf=feedparser.parse('http://sports.yahoo.com/nba/teams/hou/rss.xml')    getTopWords(ny,sf)def getTopWords(ny,sf):    import operator    vocabList,p0V,p1V=localWords(ny,sf)    topNY=[]; topSF=[]    for i in range(len(p0V)):        if p0V[i] > -6.0 : topSF.append((vocabList[i],p0V[i]))        if p1V[i] > -6.0 : topNY.append((vocabList[i],p1V[i]))    sortedSF = sorted(topSF, key=lambda pair: pair[1], reverse=True)    print "SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**"    for item in sortedSF:        print item[0]    sortedNY = sorted(topNY, key=lambda pair: pair[1], reverse=True)    print "NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**"    for item in sortedNY:        print item[0]def test42():    print '/n*** Load DataSet ***'    listOPosts,listClasses = loadDataSet()    print 'List of posts:/n', listOPosts    print 'List of Classes:/n', listClasses    print '/n*** Create Vocab List ***'    myVocabList = createVocabList(listOPosts)    print 'Vocab List from posts:/n', myVocabList    print '/n*** Vocab show in post Vector Matrix ***'    trainMat=[]    for postinDoc in listOPosts:        trainMat.append(bagOfWords2Vec(myVocabList,postinDoc))    print 'Train Matrix:/n', trainMat    print '/n*** Train ***'    p0V,p1V,pAb = trainNB0(trainMat,listClasses)    print 'p0V:/n',p0V    print 'p1V:/n',p1V    print 'pAb:/n',pAb