python讲稿7 Bernoulli naive bayes

from numpy import *
import jieba
import string
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 trainNB0(trainMatrix,trainCategory):
    numTrainDocs = len(trainMatrix)
    numWords = len(trainMatrix[0])
    pAbusive = sum(trainCategory)/float(numTrainDocs)
    p0Num = np.ones(numWords); p1Num = np.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 += 1
        else:
            p0Num += trainMatrix[i]
            p0Denom += 1
    p1Vect = np.log(p1Num/p1Denom)          #change to log()
    p0Vect = np.log(p0Num/p0Denom)          #change to log()
    return p0Vect,p1Vect,pAbusive

def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):
    p1 = sum(vec2Classify * p1Vec) + np.log(pClass1)    #element-wise mult
    p0 = sum(vec2Classify * p0Vec) + np.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 returnVec

def testingNB():
    listOPosts,listClasses = loadDataSet()
    myVocabList = createVocabList(listOPosts)
    trainMat=[]
    for postinDoc in listOPosts:
        trainMat.append(setOfWords2Vec(myVocabList, postinDoc))
    p0V,p1V,pAb = trainNB0(np.array(trainMat),np.array(listClasses))
    testEntry = ['love', 'my', 'dalmation']
    thisDoc = np.array(setOfWords2Vec(myVocabList, testEntry))
    print(testEntry,'classified as: ',classifyNB(thisDoc,p0V,p1V,pAb))
    testEntry = ['stupid', 'garbage']
    thisDoc = np.array(setOfWords2Vec(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 textParse2(bigString):
    stop_f=open('d:/email/stopwords.txt',encoding='utf8')
    stopwords=list()
    for line in stop_f.readlines():
        line=line.strip()
        stopwords.append(line)
    stop_f.close()
    seg_list=jieba.lcut(bigString,cut_all=False)
    outstr=[]
    for i in seg_list:
        if i not in stopwords and i not in string.punctuation:
            outstr.append(i)
    return outstr  
def spamTest():
	docList=[]; classList = []; fullText =[]
	for i in range(1,26):
	    wordList = textParse2(open('d:/email/spam/%d.txt' % i).read())
	    docList.append(wordList)
	    fullText.extend(wordList)
	    classList.append(1)
	    wordList = textParse2(open('d:/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(np.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(setOfWords2Vec(vocabList, docList[docIndex]))
	    trainClasses.append(classList[docIndex])
	p0V,p1V,pSpam = trainNB0(np.array(trainMat),np.array(trainClasses))
	errorCount = 0
	for docIndex in testSet:        #classify the remaining items
	    wordVector = setOfWords2Vec(vocabList, docList[docIndex])
	    if classifyNB(np.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
	return float(errorCount)/len(testSet)

运行100次取平均值:

re=0
for i in range(100):
    re+=spamTest()
re/100.0

结果为:
0.017000000000000005