【机器学习算法-python实现】扫黄神器-朴素贝叶斯分类器的实现

时间：2014-04-29 13:15:21 收藏：0 阅读：297

(转载请注明出处：http://blog.csdn.net/buptgshengod)

1.背景

以前我在外面公司实习的时候，一个大神跟我说过，学计算机就是要一个一个贝叶斯公式的套用来套用去。嗯，现在终于用到了。朴素贝叶斯分类器据说是好多扫黄软件使用的算法，贝叶斯公式也比较简单，大学做概率题经常会用到。核心思想就是找出特征值对结果影响概率最大的项。公式如下：

mamicode.com,码迷

什么是朴素贝叶斯，就是特征值相互独立互不影响的情况。贝叶斯可以有很多变形，这里先搞一个简单的，以后遇到复杂的再写。

2.数据集

摘自机器学习实战。

[[‘my‘,‘dog‘,‘has‘, ‘flea‘,‘problems‘,‘help‘, ‘please‘], 0

[‘maybe‘,‘not‘,‘take‘, ‘him‘,‘to‘,‘dog‘, ‘park‘,‘stupid‘], 1

[‘my‘,‘dalmation‘,‘is‘,‘so‘, ‘cute‘,‘I‘,‘love‘, ‘him‘], 0

[‘stop‘,‘posting‘,‘stupid‘, ‘worthless‘,‘garbage‘], 1

[‘mr‘,‘licks‘,‘ate‘, ‘my‘,‘steak‘,‘how‘, ‘to‘,‘stop‘,‘him‘], 0

[‘quit‘,‘buying‘,‘worthless‘, ‘dog‘,‘food‘,‘stupid‘]] 1

以上是六句话，标记是0句子的表示粗口句，标记是1句子的表示为粗口。我们通过分析每个句子中的每个词，在粗口句或是正常句出现的概率，可以找出那些词是粗口。

3.代码

#以矩阵形式创建数据集
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

#将矩阵内容添加到列表，set获取list中不重复的元素
def createVocabList(dataSet):
    vocabSet = set([])  #create empty set
    for document in dataSet:
        vocabSet = vocabSet | set(document) #union of the two sets
    return list(vocabSet)

#判断list中每个词在总共词语list中的位置
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)#脏句的比例 
    p0Num = zeros(numWords); p1Num = zeros(numWords) #zero是numpy带的函数，zeros(i)长度为i的list          
    p0Denom = 0.0; p1Denom = 0.0                        
    for i in range(numTrainDocs):
        if trainCategory[i] == 1:#如果是粗口句，每个词在p1num加一
            p1Num += trainMatrix[i]
            p1Denom += sum(trainMatrix[i])
        else:
            p0Num += trainMatrix[i]
            p0Denom += sum(trainMatrix[i])
           
    p1Vect = p1Num/p1Denom          #粗口字概率
    p0Vect = p0Num/p0Denom         
    return p0Vect,p1Vect,pAbusive

实现效果:

输出粗口字概率list：

[ 0. 0. 0. 0.05263158 0.05263158 0. 0.

0. 0.05263158 0.05263158 0. 0. 0.

0.05263158 0.05263158 0.05263158 0.05263158 0.05263158 0.

0.10526316 0. 0.05263158 0.05263158 0. 0.10526316

0. 0.15789474 0. 0.05263158 0. 0. 0. ]

出现概率最大项：

0.157894736842

对应的词是：stupid

[‘cute‘, ‘love‘, ‘help‘, ‘garbage‘, ‘quit‘, ‘I‘, ‘problems‘, ‘is‘, ‘park‘, ‘stop‘, ‘flea‘, ‘dalmation‘, ‘licks‘, ‘food‘, ‘not‘, ‘him‘, ‘buying‘, ‘posting‘, ‘has‘, ‘worthless‘, ‘ate‘, ‘to‘, ‘maybe‘, ‘please‘, ‘dog‘, ‘how‘, ‘stupid‘, ‘so‘, ‘take‘, ‘mr‘, ‘steak‘, ‘my‘]

4.下载

下载地址（Bayes）