机器学习实战:基于概率论的分类方法:朴素贝叶斯(源码解析,错误分析)-阿里云开发者社区

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机器学习实战:基于概率论的分类方法:朴素贝叶斯(源码解析,错误分析)

简介: 按照惯例,先把代码粘到这里 from numpy import * def LoadDataSet(): postingList = [['my', 'dog', 'has', 'flea', 'proble...

按照惯例,先把代码粘到这里

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', '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 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


def BagOfWords2VecMN(vocabList, inputSet):
    returnVec = [0] * len(vocabList)
    for word in inputSet:
        if word in vocabList:
            returnVec[vocabList.index(word)] += 1
    return returnVec


def trainNB0(trainMatrix, trainCategory):
    numTrainDocs = len(trainMatrix)
    numWords = len(trainMatrix[0])
    pAbusive = sum(trainCategory) / float(numTrainDocs)
    p0Num = ones(numWords)
    p1Num = ones(numWords)            #change to ones() to avoid product to be zero
    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()  to avoid down-overflow
    p0Vect = log(p0Num/p0Denom)          #change to log()
    return p0Vect, p1Vect, pAbusive


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


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('machinelearninginaction\Ch04\email\spam\%d.txt' % i).read())
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(1)
        wordList = TextParse(open('machinelearninginaction\Ch04\email\ham\%d.txt' % i).read())
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(0)
    vocabList = CreateVocabList(docList)  # create vocabulary
    trainingSet = list(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,fullText


# 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)  # NY is class 1
#         wordList = TextParse(feed0['entries'][i]['summary'])
#         docList.append(wordList)
#         fullText.extend(wordList)
#         classList.append(0)
#     vocabList = CreateVocabList(docList)  # create vocabulary
#     top30Words = calcMostFreq(vocabList, fullText)  # remove top 30 words
#     for pairW in top30Words:
#         if pairW[0] in vocabList: vocabList.remove(pairW[0])
#     trainingSet = range(2 * minLen);
#     testSet = []  # create test set
#     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:  # 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
#     '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: 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]

编译过程中遇到的错误

错误1:

UnicodeDecodeError: 'gbk' codec can't decode byte 0xae in position 199: illegal multibyte sequence

这个错误意思是python无法读取某个txt文件,实为编码问题,解决方法是按照错误跳转到相应程序行,在spam或者ham中查找有问题的txt,看看是否编码错误。如果编码错误,可以手动修改编码,或者你的编译器可以自动选择编码模式,修改一下就可以了。


错误2:

TypeError: 'range' object doesn't support item deletion

trainingSet = range(50) 

改为

trainingSet = list(range(50))

即可


关于机器学习的源代码以及数据集在这里:
http://blog.csdn.net/iamoldpan/article/details/78010329

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