使用贝叶斯进行文档分类

准备数据：从文本中构建词汇量

def loadDataSet():
'''函数loadDataSet:导入数据集和分类

 parameters:无

 return:postingList:数据集例子
 classVec:对应数据集的分类
 '''
    postingList=[['my','dog','has','flea','promblems','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]
return postingList,classVec

def createVocabList(dataSet):
'''函数createVocabList建立一个存在现有单词的总集

 parameters:dataSet:之前函数中的数据集

 return:vocabList:现有单词的总集
 '''
    vocabList=set([])
for i in dataSet:
        vocabList = vocabList | set(i)
    vocabList = list(vocabList)
return vocabList

def setOfWords2Vec(vocabList,inputSet):
'''函数setOfWords2Vec输出文档0-1向量

 parameters:vocabList:单词总集
 inputSet:某个单词list

 return:returnVec:文档list对应的0-1向量
 '''
    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

训练算法：从词汇两计算概率

def trainNB0(trainMatrix,trainCategory):
'''函数trainNB0是朴素贝叶斯分类器训练函数

 parameters:trainMatrix：数据集0-1文档矩阵
 trainCategory:文档类别标签0-1向量

 return:p0Vect,p1Vect:分别是在分类0,1情况下的条件概况list
 pAbusive:分类为1的概率
 '''
    numTrainDocs = len(trainMatrix)
    numWords = len(trainMatrix[0])
    pAbusive = np.sum(trainCategory)/numTrainDocs
    p0Num = np.ones(numWords)
    p1Num = np.ones(numWords)
#`ones`是为了防止概率值为0的情况
    p0Denom = 2
    p1Denom = 2
#`=2`是为了防止概率值为0的情况
for i in range(numTrainDocs):
if trainCategory[i] == 1:
            p1Num += trainMatrix[i]
            p1Denom += np.sum(trainMatrix[i])
else:
            p0Num += trainMatrix[i]
            p0Denom += np.sum(trainMatrix[i])
    p1Vect = np.log(p1Num/p1Denom)
    p0Vect = np.log(p0Num/p0Denom)
#`log`防止乘法遇到特别小的数，用log转化为加法
return p0Vect,p1Vect,pAbusive

测试算法：根据现实情况修改分类器

def classifyNB(vec2Classify,p0Vect,p1Vect,pClass1):
'''函数classifyNB是贝叶斯分类函数

 parameters:vec2Classify:想测试的词汇list
 p0Vect,p1Vect:分别是在分类0,1情况下的条件概况list
 pClass1:分类为1的概率

 return:最后分类
 '''
    p1 = np.sum(vec2Classify*p1Vect)+np.log(pClass1)
    p0 = np.sum(vec2Classify*p0Vect)+np.log(1-pClass1)
if p1>p0:
return 1
else:
return 0

def testingNB():
'''函数testingNB利用之前的所有函数对新的词汇list进行分类

 parameters: 无

 return:print输出
 '''
import numpy as np
    dataSet,classVec = loadDataSet()
    myvocabList = createVocabList(dataSet)
    trainMat=[]
for postinDoc in dataSet:
        trainMat.append(setOfWords2Vec(myvocabList,postinDoc))
    p0V,p1V,pAb = trainNB0(np.array(trainMat),np.array(classVec))
    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))

最后的测试

if __name__ == "__main__":
    testingNB()

基于朴素贝叶斯算法——进行文档分类

测试算法：使用朴素贝叶斯进行交叉验证

import bayes
import numpy as np
from imp import reload 

reload(bayes)

def textParse(bigString):
'''函数textPares对文本字符串切分

 parameters:bigString:输入的txt文件

 return：被分割的字符串列表（小写，长度大于2）
 '''
import re
    listOfTokens = re.split(r'\W*',bigString)
return [tok.lower() for tok in listOfTokens if len(tok) > 2]

def spamTest():
'''函数spamTest使用朴素贝叶斯进行交叉验证

 parameters:无

 return：错误率
 '''
    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 = bayes.createVocabList(docList)
    trainingSet = list(range(50))
    testSet=[]
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:
        trainMat.append(bayes.setOfWords2Vec(vocabList,docList[docIndex]))
        trainClasses.append(classList[docIndex])
    p0V,p1V,pSpam = bayes.trainNB0(np.array(trainMat),np.array(trainClasses))
    errorCount=0
for docIndex in testSet:
        wordVector = bayes.setOfWords2Vec(vocabList,docList[docIndex])
if bayes.classifyNB(np.array(wordVector),p0V,p1V,pSpam) != classList[docIndex]:
            errorCount += 1
    print("the error rate is:",errorCount/len(testSet))

spamTest()

输出：

基于朴素贝叶斯算法——进行文档分类

以上过程重复多次，比如说10次，然后求平均值，获取平均错误率

秒客网

基于朴素贝叶斯算法——进行文档分类

使用贝叶斯进行文档分类

准备数据：从文本中构建词汇量

训练算法：从词汇两计算概率

测试算法：根据现实情况修改分类器

最后的测试

测试算法：使用朴素贝叶斯进行交叉验证

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