代碼註釋：機器學習實戰第4章 基於概率論的分類方法：樸素貝葉斯

寫在開頭的話：在學習《機器學習實戰》的過程中發現書中很多代碼並沒有註釋，這對新入門的同學是一個挑戰，特此貼出我對代碼做出的註釋，僅供參考，歡迎指正。

1、進行文本分類

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', 'steak', 'how', 'to', 'stop', 'him'],
                   ['quit', 'buying', 'worthless', 'dog', 'food', 'stupid']]
    classVec = [0, 1, 0, 1, 0, 1]  #ddf
    return postingList, classVec

#功能：創建不重複詞的列表
#輸入：數據集
#輸出：不重複詞的列表
def createVocabList(dataSet):
    vocabSet = set([])#創建空集合
    for document in dataSet:
        vocabSet = vocabSet | set(document)#或操作表示創建並集
    return list(vocabSet)

#功能：輸入文檔中的單詞在詞彙表中是否出現
#輸入：詞彙表，輸入文檔
#輸出：文檔向量
def setOfWords2Vec(vocabList, inputSet):
    returnVec = [0] * len(vocabList)#創建和詞彙表等長的向量
    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

#功能：樸素貝葉斯分類器訓練函數
#輸入：文檔向量，標籤向量
#輸出：任意文檔屬於侮辱性文檔的概率，詞彙在侮辱性文檔中出現概率，詞彙在非侮辱性文檔中出現概率
def trainNB0(trainMatrix, trainCategory):
    numTrainDocs = len(trainMatrix)#trainMatrix行數
    numWords = len(trainMatrix[0])#trainMatrix列數
    pAbusive = sum(trainCategory) / float(numTrainDocs)#任意文檔屬於侮辱性文檔的概率
    p0Num = ones(numWords)#拉普拉斯修正
    p1Num = ones(numWords)#拉普拉斯修正
    p0Denom = 2.0#拉普拉斯修正，只有兩個標籤
    p1Denom = 2.0#拉普拉斯修正，只有兩個標籤
    for i in range(numTrainDocs):
        if trainCategory[i] == 1:#屬於侮辱性文檔
            p1Num += trainMatrix[1]#侮辱性文檔數量
            p1Denom += sum(trainMatrix[i])
        else:#不屬於侮辱性文檔
            p0Num += trainMatrix[i]#非侮辱性文檔數量
            p0Denom += sum(trainMatrix[i])
    p1Vect = log(p1Num / p1Denom)#防止下溢出，自然對數處理
    p0Vect = log(p0Num / p0Denom)#防止下溢出，自然對數處理
    return p0Vect, p1Vect, pAbusive

#功能：判斷屬於哪一類
#輸入：要判斷向量，詞彙在侮辱性文檔中出現概率，詞彙在非侮辱性文檔中出現概率，任意文檔屬於侮辱性文檔的概率
#輸出：判斷值
def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):
    p1 = sum(vec2Classify * p1Vec) + log(pClass1)
    p0 = sum(vec2Classify * p0Vec) + log(1.0 - pClass1)
    if p1 > p0:
        return  1
    else:
        return  0

#功能：輸入文檔中的單詞在詞彙表中是否出現
#輸入：詞彙表，輸入文檔
#輸出：文檔向量
def setOfWords2Vec(vocabList, inputSet):
    returnVec = [0] * len(vocabList)  # 創建和詞彙表等長的向量
    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

def bagOfWords2VecMN(vocabList, inputSet):
    returnVec = [0]*len(vocabList)
    for word in inputSet:
        if word in vocabList:
            returnVec[vocabList.index(word)] += 1
    return returnVec

#功能：便利函數
#輸入：無
#輸出：無
def testingNB():
    label = ['ham', 'spam']#將結果以文本形式呈現
    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: ', label[classifyNB(thisDoc, p0V, p1V, pAb)]
    testEntry = ['stupid', 'garbage']
    thisDoc = array(setOfWords2Vec(myVocabList, testEntry))
    print testEntry, 'classified as: ', label[classifyNB(thisDoc, p0V, p1V, pAb)]

2、過濾垃圾郵件

#功能：切分文本
#輸入：需要切分文本
#輸出：切分好的文本
def textParse(bigString):
    import re
    listOfTokens = re.split(r'\W*', bigString)#\W表示非單詞字符，*表示匹配前一個字符0次或無限次
    #參考鏈接：http://www.cnblogs.com/huxi/archive/2010/07/04/1771073.html
    return [tok.lower() for tok in listOfTokens if len(tok) > 2]#列表推導式

#功能：測試算法
#輸入：無
#輸出：測試正確率
def spamTest():
    docList = []#文檔列表
    classList = []#標籤列表
    fullText = []#單詞列表
    for i in range(1, 26):
        wordList = textParse(open('email/spam/%d.txt' % i).read())#打開spam文檔，切分文本
        docList.append(wordList)#append向列表尾部添加一個新的元素，將整個wordList添加進去，即文本爲基本單位
        fullText.extend(wordList)#extend向列表尾部添加wordList的所有元素，即單詞爲基本單位
        classList.append(1)#標籤表示爲spam
        wordList = textParse(open('email/ham/%d.txt' % i).read())  # 打開ham文檔，切分文本
        docList.append(wordList)  # append向列表尾部添加一個新的元素，將整個wordList添加進去，即文本爲基本單位
        fullText.extend(wordList)  # extend向列表尾部添加wordList的所有元素，即單詞爲基本單位
        classList.append(0)#標籤表示爲ham
    vocabList = createVocabList(docList)#創建不重複詞的列表
    trainingSet = range(50)
    testSet = []
    for i in range(10):#在trainingSet中隨機刪除十個數，刪除的作爲測試集，沒有刪除的作爲訓練集
        randIndex = int(random.uniform(0, len(trainingSet)))
        testSet.append(trainingSet[randIndex])
        del(trainingSet[randIndex])
    trainMat = []
    trainClasses = []
    for docIndex in trainingSet:#得測試集和測試標籤
        trainMat.append(bagOfWords2VecMN(vocabList, docList[docIndex]))
        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]:#判斷測試是否正確
            errorCount += 1
            print "classifiction error", docList[docIndex]
    print 'the error rate is: ', float(errorCount) / len(testSet)

3、RSS應用

#功能：得出現頻率最高的30個詞
#輸入：詞彙表，文本
#輸出：頻率最高的30個詞出現的個數和詞
def calcMostFreq(vocabList, fullText):
    import operator
    freqDict = {}#建立字典
    for token in vocabList:
        freqDict[token] = fullText.count(token)
    sortedFreq = sorted(freqDict.iteritems(), key = operator.itemgetter(1), reverse = True)#按值從小到大的順序排序
    return sortedFreq[:30]#返回前30 單詞出現個數和詞

#功能：rss功能
#輸入：
#輸出：
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)
    top30Words = calcMostFreq(vocabList, fullText)
    for pairW in top30Words:
        if pairW[0] in vocabList:
            vocabList.remove(pairW[0])
    trainingSet = range(2 * minLen)
    testSet = []
    for i in range(20):
        randIndex = int(random.uniform(0, len(trainingSet)))
        testSet.append(trainingSet[randIndex])
        del(trainingSet[randIndex])
    trainMat = []
    trainClasses = []
    for docIndex in trainingSet:
        trainMat.append(bagOfWords2VecMN(vocabList, docList[docIndex]))
        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(nf, sf):
    import operator
    vocabList, p0V, p1V = localWords(nf, 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]