Naive Bayes python implementation, Bayesian python

Naive Bayes python implementation, Bayesian python

Probability Theory is the basis of many machine learning algorithms. Naive Bayes classifier is called naive because only original and simple assumptions are made throughout the formal process. (This assumption: There are many features in the problem. We simply assume that each feature is independent. This assumption is called conditional independence. In fact, it is often not completely independent in actual problems, in this case, another method called Bayesian network is needed. We will use the naive Bayes method later to solve the spam filtering problem.

Classification of Bayesian decision theory:

Advantage: less data is still valid and can handle multi-category issues.

Disadvantage: The preparation method for input data is more sensitive. I understand that it is necessary to prepare each classified sample set.

Data Type: nominal data (nominal values provide only enough information to distiguish one object from another = or | =)

The theoretical basis is the Bayesian formula we learned in the mathematical statistics course. We will not repeat it here. We will classify it by calculating the conditional probability.

Text Classification Using Python:

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

The first function is loadDataSet () to create sample data. Each sample data is classified. 1 indicates that the negative comments 0 indicates normal comments.

The next function is to create a list of non-repeated words in all documents.

The third function is set to convert to a vector. If the word appears, the vector is 1 and the value is not 0.

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()     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,pAbusive

Two optimizations are made in this function. Note that the initialization matrix is 1, so that the overall probability is 0 if the probability of a feature is 0, another reason is that after the probability multiplication, the value will be relatively small, so it is easier to get the log.

Test code function:

def 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 0def 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)

Application Section: Use Naive Bayes to filter spam and use cross-validation.

There will be all marked spam mails under the spam folder for data preparation, while normal mails under ham.

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,fullText

TextParse parses strings into a string list.

SpamTest randomly selects 10 of the 50 emails as the test set for cross verification.

All code is summarized in a bayes. py file:

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 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 0def 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)def bagOfWords2VecMN(vocabList, inputSet):    returnVec = [0]*len(vocabList)    for word in inputSet:        if word in vocabList:            returnVec[vocabList.index(word)] += 1    return returnVecdef 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,fullText    if __name__ == "__main__":    listOPosts,listClasses = loadDataSet()    myVocabList = createVocabList(listOPosts)    print myVocabList    trainMat = []    for postinDoc in listOPosts:        trainMat.append(setOfWords2Vec(myVocabList, postinDoc))    p0V,p1V,pAb = trainNB0(trainMat, listClasses)    testingNB()    spamTest()

 

 

Naive Bayes algorithm problems

Use this to compile the software? The tips I gave you are also my graduation project. You can use excel to implement your computing. This is more convenient than software, then you are using VB to interact with your excel file. There are not many specific applications in life

Based on weka, the results of different algorithms in short Chinese text are significantly different. Naive Bayes is much better than other causes.

Hello!
For text classification, the effects of Naive Bayes or Bayesian belief networks are the best, which is different from general clustering classification.
For more information, see the document about bug triage.