机器学习实战

• 朴素贝叶斯
  • 一个特征或者单词出现的可能性与它和其他单词相邻没有关系
  • 每个特征同等重要
• 朴素贝叶斯分类器通常有两种实现方式
  • 基于贝努利模型实现

不考虑词在文档中出现的次数，只考虑出现与否，在这个意义上相当于假设词是等权重的

• 基于多项式模型实现

考虑词在文档中出现的次数

• Page61 在转换成矩阵的时候，可不可以在每个行向量后面再加一位，表明这个整体是1还是0？ （ 是坏句子还是好句子）

>>> for postinDoc inlistOPosts:

...    trainMat.append(bayes.setOfWords2Vec(myVocabList, postinDoc))

...

>>> p0V, p1V, pAb =bayes.trainNB0(trainMat, listClasses)

>>> pAb

0.5

>>> p0V

array([ 0.04166667, 0.08333333,  0.04166667,  0.04166667, 0.        ,

        0.        , 0.04166667,  0.04166667,  0.04166667, 0.125     ,

        0.04166667,  0.04166667, 0.04166667,  0.04166667,  0.04166667,

        0.        , 0.        ,  0.       ,  0.        , 0.04166667,

        0.04166667,  0.       ,  0.        , 0.04166667,  0.        ,

        0.04166667,  0.04166667, 0.04166667,  0.04166667,  0.       ,

        0.        , 0.04166667])

>>> p1V

array([ 0.        ,  0.05263158, 0.10526316,  0.        , 0.05263158,

        0.10526316,  0.05263158, 0.05263158,  0.        , 0.        ,

        0.        , 0.        ,  0.       ,  0.        , 0.        ,

        0.15789474,  0.05263158, 0.05263158,  0.05263158,  0.       ,

        0.        , 0.05263158,  0.05263158,  0.       ,  0.05263158,

        0.        , 0.        ,  0.       ,  0.        , 0.05263158,

        0.05263158,  0.       ])

>>> myVocabList

['is', 'him', 'dog', 'streak', 'food', 'worthless', 'stop', 'to','ate', 'my', 'so', 'dalmation', 'flea', 'I', 'licks', 'stupid', 'buying', 'quit', 'take', 'help', 'has','posting', 'garbage', 'please', 'not', 'problems', 'cute', 'mr', 'how', 'park','maybe', 'love']

这个p0V, p1V求出的是广泛的每个词在字典里，被划分到“好人”和“坏人”的概率。 （基于我们人工标注的那个list （1,0,1,0,1,0）， 比如看 p1V中概率最高的那个是0.157， 通过字典list找到对应索引位置的词语，是stupid）

>>> reload(bayes)

<module 'bayes' from 'G:\\ML\\bayes.py'>

>>> bayes.testingNB()

['love', 'my', 'dalmation'] classified as:  0

['stupid', 'garbage'] classified as: 1

['stupid', 'love'] classified as: 1

>>> mySent = 'This book isthe best book on python or M.L. I have ever laid eyes upon'

>>> mySent.split()

['This', 'book', 'is', 'the', 'best', 'book', 'on', 'python', 'or','M.L.', 'I', 'have', 'ever', 'laid', 'eyes', 'upon']

>>> mySent = 'This book isthe best book on python or M.L. I have ever laid eyes upon.'

>>> mySent.split()

['This', 'book', 'is', 'the', 'best', 'book', 'on', 'python', 'or','M.L.', 'I', 'have', 'ever', 'laid', 'eyes', 'upon.']

>>> import re

>>> regEx =re.compile('\\W*')

>>> listOfTokens =regEx.split(mySent)

__main__:1: FutureWarning: split() requires a non-empty patternmatch.

>>> listOfTokens

['This', 'book', 'is', 'the', 'best', 'book', 'on', 'python', 'or','M', 'L', 'I', 'have', 'ever', 'laid', 'eyes', 'upon', '']

>>> [tok for tok inlistOfTokens if len(tok) >0]

['This', 'book', 'is', 'the', 'best', 'book', 'on', 'python', 'or','M', 'L', 'I', 'have', 'ever', 'laid', 'eyes', 'upon']

>>> [tok.lower() for tokin listOfTokens if len(tok) >0]

['this', 'book', 'is', 'the', 'best', 'book', 'on', 'python', 'or','m', 'l', 'i', 'have', 'ever', 'laid', 'eyes', 'upon']

>>>

 

from numpy import *def  loadDataSet():    postingList=[ ['my', 'dog', 'has', 'flea', 'problems', 'help', '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', 'streak', 'how', 'to', 'stop', 'him'],                              ['quit', 'buying', 'worthless', 'dog', 'food', 'stupid']  ]    classVec = [0,1,0,1,0,1] #  1  is  bad words,   0  is  normal words    return postingList, classVec    def createVocabList(dataSet):    vocabSet = set([])    for document in dataSet:        vocabSet = vocabSet | set(document)    return list(vocabSet)    def setOfWords2Vec(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 returnVec    #到此为止，目前还没有明确接下来要怎么利用这个词向量，是将每一个句子转换成词向量，然后跟例子中的标记为1的句子求余弦相似度？那标记为0的句子有没有用呢，需要在接下来学习过程中明确    def bagOfWords2VecMN(vocabList, inputSet):    returnVec = [0]*len(vocabList)    for word in inputSet:        if word in inputSet:            returnVec[vocabList.index(word)] += 1  #本函数是词袋模型，统计了词出现的次数    return returnVec    def trainNB0(trainMatrix, trainCategory):    numTrainDocs = len(trainMatrix)    numWords = len(trainMatrix[0])    pAbusive = sum(trainCategory)/float(numTrainDocs)    #p0Num = zeros(numWords);  p1Num = zeros(numWords)    #p0Denom =0.0;  p1Denom = 0.0    #因为在利用贝叶斯分类器对文档进行分类时，要计算多个概率的乘积来获得文档属于某个类别的概率，而为了避免某个概率为0导致乘积为0，改进如下：    p0Num = ones(numWords);  p1Num = ones(numWords)    p0Denom = 2.0;  p1Denom = 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, pAbusive    def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):    p1 = sum(vec2Classify * p1Vec) + log(pClass1)    # log(pClass1)  这个值加上会效果更好是吗， 一开始觉得只要前面的乘积就好了    p0 = sum(vec2Classify * p0Vec) + log(1.0 - pClass1)    if p1 > p0:        return 1    else:        return 0    def testingNB():    listOPosts, listClasses = loadDataSet()    myVocabList = createVocabList(listOPosts)    trainMat = []    for postinDoc in listOPosts:        trainMat.append(setOfWords2Vec(myVocabList, postinDoc))    p0V, p1V, pAb = trainNB0(array(trainMat), array(listClasses))    testEntry=['love', 'my', 'dalmation']    thisDoc = array(setOfWords2Vec(myVocabList, testEntry))    print ( testEntry, 'classified as: ', classifyNB(thisDoc, p0V, p1V, pAb))    testEntry = ['stupid', 'garbage']    thisDoc = array(setOfWords2Vec(myVocabList, testEntry))    print (testEntry, 'classified as:', classifyNB(thisDoc, p0V, p1V, pAb))    testEntry = ['stupid', 'love']    thisDoc = array(setOfWords2Vec(myVocabList, testEntry))    print (testEntry, 'classified as:', classifyNB(thisDoc, p0V, p1V, pAb))    def textParse(bigString):    import re    listOfTokens = re.split(r'\W*', bigString)   #注意这里唷！ 开始写成了小写的w结果出来的全是符号， classification error的词语没有正常输出    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)    trainingSet = list(range(50));  testSet=[]   #总共有25+25=50个数据集    for i in range(10):    #选取了10个作为test集，其余40个作为训练集        randIndex = int(random.uniform(0, len(trainingSet)))        testSet.append(trainingSet[randIndex])        del(trainingSet[randIndex])  #这种随机选择数据一部分作为训练集，剩余部分作为测试机的过程叫做【留存交叉验证】hold-out cross validation    trainMat = [];  trainClasses = []    for docIndex in trainingSet:        trainMat.append(setOfWords2Vec(vocabList, docList[docIndex]))        trainClasses.append(classList[randIndex])  #这句话作用是什么?    p0V,p1V,pSpam = trainNB0(array(trainMat),  array(trainClasses)) #作用就在这里呀hhh    errorCount = 0    for docIndex in testSet:        #wordVector = setOfWords2Vec(vocabList, docList[docIndex])        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))    #用spamTest进行了下命令行测试，为什么我的错误率好高呢，试验多次的概率大部分都是大于0.5的    #待进一步分析找原因        #接下来使用朴素贝叶斯发现地域相关的用词.  此功能没有成功运行， 显示报错ValueError: 'for' is not in list，不知道是不是RSS源的问题，待解决
def calcMostFreq(vocabList, fullText):    import operator    freqDict = {}    for token in vocabList:        freqDict[token] = fullText.count(token)    sortedFreq = sorted(freqDict.items(), key=operator.itemgetter(1), reverse=True)    return sortedFreq[:30]    def localWords(feed1,feed0):    import feedparser    docList=[];  classList = [];  fullText = []    minLen = min( len(feed1['entries']), len(feed0['entries']))    for i in range(minLen):        wordList = textParse(feed1['entries'][i]['summary'])        docList.append(wordList)        fullText.extend(wordList)        classList.append(1)        wordList = textParse(feed0['entries'][i]['summary'])        docList.append(wordList)        fullText.extend(wordList)        classList.append(0)    vocabList = createVocabList(docList)  #vocabList 去重后的字典    top30Words = calcMostFreq(vocabList, fullText)    for pairW in top30Words:        if pairW[0] in vocabList: vocabList.remove(pairW[0]) #这里的[0]是为啥咧。 噢，好像懂了，因为calcMostFreq 返回的是一个dict， 每一个item都包含key和value，所以取[0]是取的key值    trainingSet = list(range(2*minLen));  testSet = []    for i in range(2):        randIndex = int(random.uniform(0, len(trainingSet)))        testSet.append(trainingSet[randIndex])        del(trainingSet[randIndex])  #从trainingSet中取出来20个当test，原书中是有100以上的，但是那个RSS源无法打开了，只能用这个新的RSS源    trainMat=[];   trainClasses = []    for docIndex in trainingSet:        trainMat.append(bagOfWords2VecMN(vocabList, docList[docIndex]))  #docList 此时包含了feed1和feed2的所有的，所以在长度 minLen*2 完全够用        trainClasses.append(classList[docIndex])    p0V, p1V,pSpam = trainNB0(array(trainMat),array(trainClasses))    errorCount = 0    for docIndex in testSet:        wordVector = bagOfWords2VecMN(vocabList, docList[docIndex])        if classifyNB(array(wordVector), p0V, p1V, pSpam) != classList[docIndex]:            errorCount += 1        print ('the error rate is: ', float(errorCount)/len(testSet))        return vocabList, p0V, p1V         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: topNF.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**")    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**")    for item in sortedNY:        print (item[0])