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朴素贝叶斯算法的python实现

高洛峰

发布： 2016-10-18 09:11:08

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算法优缺点

优点：在数据较少的情况下依然有效，可以处理多类别问题

缺点：对输入数据的准备方式敏感

适用数据类型：标称型数据

算法思想：

朴素贝叶斯

比如我们想判断一个邮件是不是垃圾邮件，那么我们知道的是这个邮件中的词的分布，那么我们还要知道：垃圾邮件中某些词的出现是多少，就可以利用贝叶斯定理得到。

朴素贝叶斯分类器中的一个假设是：每个特征同等重要

贝叶斯分类是一类分类算法的总称，这类算法均以贝叶斯定理为基础，故统称为贝叶斯分类。

函数

loadDataSet()

创建数据集，这里的数据集是已经拆分好的单词组成的句子，表示的是某论坛的用户评论，标签1表示这个是骂人的

createVocabList(dataSet)

找出这些句子中总共有多少单词，以确定我们词向量的大小

setOfWords2Vec(vocabList, inputSet)

将句子根据其中的单词转成向量，这里用的是伯努利模型，即只考虑这个单词是否存在

bagOfWords2VecMN(vocabList, inputSet)

这个是将句子转成向量的另一种模型，多项式模型，考虑某个词的出现次数

trainNB0(trainMatrix,trainCatergory)

计算P(i)和P(w[i]|C[1])和P(w[i]|C[0])，这里有两个技巧，一个是开始的分子分母没有全部初始化为0是为了防止其中一个的概率为0导致整体为0，另一个是后面乘用对数防止因为精度问题结果为0

classifyNB(vec2Classify, p0Vec, p1Vec, pClass1)

根据贝叶斯公式计算这个向量属于两个集合中哪个的概率高

#coding=utf-8
from numpy import *
def loadDataSet():
    postingList=[[&#39;my&#39;, &#39;dog&#39;, &#39;has&#39;, &#39;flea&#39;, &#39;problems&#39;, &#39;help&#39;, &#39;please&#39;],
                 [&#39;maybe&#39;, &#39;not&#39;, &#39;take&#39;, &#39;him&#39;, &#39;to&#39;, &#39;dog&#39;, &#39;park&#39;, &#39;stupid&#39;],
                 [&#39;my&#39;, &#39;dalmation&#39;, &#39;is&#39;, &#39;so&#39;, &#39;cute&#39;, &#39;I&#39;, &#39;love&#39;, &#39;him&#39;],
                 [&#39;stop&#39;, &#39;posting&#39;, &#39;stupid&#39;, &#39;worthless&#39;, &#39;garbage&#39;],
                 [&#39;mr&#39;, &#39;licks&#39;, &#39;ate&#39;, &#39;my&#39;, &#39;steak&#39;, &#39;how&#39;, &#39;to&#39;, &#39;stop&#39;, &#39;him&#39;],
                 [&#39;quit&#39;, &#39;buying&#39;, &#39;worthless&#39;, &#39;dog&#39;, &#39;food&#39;, &#39;stupid&#39;]]
    classVec = [0,1,0,1,0,1]    #1 is abusive, 0 not
    return postingList,classVec
#创建一个带有所有单词的列表
def createVocabList(dataSet):
    vocabSet = set([])
    for document in dataSet:
        vocabSet = vocabSet | set(document)
    return list(vocabSet)
     
def setOfWords2Vec(vocabList, inputSet):
    retVocabList = [0] * len(vocabList)
    for word in inputSet:
        if word in vocabList:
            retVocabList[vocabList.index(word)] = 1
        else:
            print &#39;word &#39;,word ,&#39;not in dict&#39;
    return retVocabList
#另一种模型    
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,trainCatergory):
    numTrainDoc = len(trainMatrix)
    numWords = len(trainMatrix[0])
    pAbusive = sum(trainCatergory)/float(numTrainDoc)
    #防止多个概率的成绩当中的一个为0
    p0Num = ones(numWords)
    p1Num = ones(numWords)
    p0Denom = 2.0
    p1Denom = 2.0
    for i in range(numTrainDoc):
        if trainCatergory[i] == 1:
            p1Num +=trainMatrix[i]
            p1Denom += sum(trainMatrix[i])
        else:
            p0Num +=trainMatrix[i]
            p0Denom += sum(trainMatrix[i])
    p1Vect = log(p1Num/p1Denom)#处于精度的考虑，否则很可能到限归零
    p0Vect = log(p0Num/p0Denom)
    return p0Vect,p1Vect,pAbusive
     
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 0
         
def 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 = [&#39;love&#39;, &#39;my&#39;, &#39;dalmation&#39;]
    thisDoc = array(setOfWords2Vec(myVocabList, testEntry))
    print testEntry,&#39;classified as: &#39;,classifyNB(thisDoc,p0V,p1V,pAb)
    testEntry = [&#39;stupid&#39;, &#39;garbage&#39;]
    thisDoc = array(setOfWords2Vec(myVocabList, testEntry))
    print testEntry,&#39;classified as: &#39;,classifyNB(thisDoc,p0V,p1V,pAb)
     
     
def main():
    testingNB()
     
if __name__ == &#39;__main__&#39;:
    main()

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