Python实现的朴素贝叶斯算法经典示例【测试可用】
本文实例讲述了Python实现的朴素贝叶斯算法。分享给大家供大家参考，具体如下：
代码主要参考机器学习实战那本书，发现最近老外的书确实比中国人写的好，由浅入深，代码通俗易懂，不多说上代码：
#encoding:utf-8
'''''
Createdon2015年9月6日
@author:ZHOUMEIXU204
朴素贝叶斯实现过程
'''
#在该算法中类标签为1和0，如果是多标签稍微改动代码既可
importnumpyasnp
path=u"D:\\Users\\zhoumeixu204\Desktop\\python语言机器学习\\机器学习实战代码python\\机器学习实战代码\\machinelearninginaction\\Ch04\\"
defloadDataSet():
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]#1isabusive,0not
returnpostingList,classVec
defcreateVocabList(dataset):
vocabSet=set([])
fordocumentindataset:
vocabSet=vocabSet|set(document)
returnlist(vocabSet)
defsetOfWordseVec(vocabList,inputSet):
returnVec=[0]*len(vocabList)
forwordininputSet:
ifwordinvocabList:
returnVec[vocabList.index(word)]=1#vocabList.index()函数获取vocabList列表某个元素的位置，这段代码得到一个只包含0和1的列表
else:
print("theword:%sisnotinmyVocabulary!"%word)
returnreturnVec
listOPosts,listClasses=loadDataSet()
myVocabList=createVocabList(listOPosts)
print(len(myVocabList))
print(myVocabList)
print(setOfWordseVec(myVocabList,listOPosts[0]))
print(setOfWordseVec(myVocabList,listOPosts[3]))
#上述代码是将文本转化为向量的形式，如果出现则在向量中为1，若不出现，则为0
deftrainNB0(trainMatrix,trainCategory):#创建朴素贝叶斯分类器函数
numTrainDocs=len(trainMatrix)
numWords=len(trainMatrix[0])
pAbusive=sum(trainCategory)/float(numTrainDocs)
p0Num=np.ones(numWords);p1Num=np.ones(numWords)
p0Deom=2.0;p1Deom=2.0
foriinrange(numTrainDocs):
iftrainCategory[i]==1:
p1Num+=trainMatrix[i]
p1Deom+=sum(trainMatrix[i])
else:
p0Num+=trainMatrix[i]
p0Deom+=sum(trainMatrix[i])
p1vect=np.log(p1Num/p1Deom)#changetolog
p0vect=np.log(p0Num/p0Deom)#changetolog
returnp0vect,p1vect,pAbusive
listOPosts,listClasses=loadDataSet()
myVocabList=createVocabList(listOPosts)
trainMat=[]
forpostinDocinlistOPosts:
trainMat.append(setOfWordseVec(myVocabList,postinDoc))
p0V,p1V,pAb=trainNB0(trainMat,listClasses)
if__name__!='__main__':
print("p0的概况")
print(p0V)
print("p1的概率")
print(p1V)
print("pAb的概率")
print(pAb)
运行结果：
32
['him','garbage','problems','take','steak','quit','so','is','cute','posting','dog','to','love','licks','dalmation','flea','I','please','maybe','buying','my','stupid','park','food','stop','has','ate','help','how','mr','worthless','not']
[0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,1,0,0,0,0,1,0,1,0,0,0,0]
[0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0]
#-*-coding:utf-8-*-
#!python2
#构建样本分类器testEntry=['love','my','dalmation']testEntry=['stupid','garbage']到底属于哪个类别
importnumpyasnp
defloadDataSet():
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]#1isabusive,0not
returnpostingList,classVec
defcreateVocabList(dataset):
vocabSet=set([])
fordocumentindataset:
vocabSet=vocabSet|set(document)
returnlist(vocabSet)
defsetOfWordseVec(vocabList,inputSet):
returnVec=[0]*len(vocabList)
forwordininputSet:
ifwordinvocabList:
returnVec[vocabList.index(word)]=1#vocabList.index()函数获取vocabList列表某个元素的位置，这段代码得到一个只包含0和1的列表
else:
print("theword:%sisnotinmyVocabulary!"%word)
returnreturnVec
deftrainNB0(trainMatrix,trainCategory):#创建朴素贝叶斯分类器函数
numTrainDocs=len(trainMatrix)
numWords=len(trainMatrix[0])
pAbusive=sum(trainCategory)/float(numTrainDocs)
p0Num=np.ones(numWords);p1Num=np.ones(numWords)
p0Deom=2.0;p1Deom=2.0
foriinrange(numTrainDocs):
iftrainCategory[i]==1:
p1Num+=trainMatrix[i]
p1Deom+=sum(trainMatrix[i])
else:
p0Num+=trainMatrix[i]
p0Deom+=sum(trainMatrix[i])
p1vect=np.log(p1Num/p1Deom)#changetolog
p0vect=np.log(p0Num/p0Deom)#changetolog
returnp0vect,p1vect,pAbusive
defclassifyNB(vec2Classify,p0Vec,p1Vec,pClass1):
p1=sum(vec2Classify*p1Vec)+np.log(pClass1)
p0=sum(vec2Classify*p0Vec)+np.log(1.0-pClass1)
ifp1>p0:
return1
else:
return0
deftestingNB():
listOPosts,listClasses=loadDataSet()
myVocabList=createVocabList(listOPosts)
trainMat=[]
forpostinDocinlistOPosts:
trainMat.append(setOfWordseVec(myVocabList,postinDoc))
p0V,p1V,pAb=trainNB0(np.array(trainMat),np.array(listClasses))
print("p0V={0}".format(p0V))
print("p1V={0}".format(p1V))
print("pAb={0}".format(pAb))
testEntry=['love','my','dalmation']
thisDoc=np.array(setOfWordseVec(myVocabList,testEntry))
print(thisDoc)
print("vec2Classify*p0Vec={0}".format(thisDoc*p0V))
print(testEntry,'classifiedas:',classifyNB(thisDoc,p0V,p1V,pAb))
testEntry=['stupid','garbage']
thisDoc=np.array(setOfWordseVec(myVocabList,testEntry))
print(thisDoc)
print(testEntry,'classifiedas:',classifyNB(thisDoc,p0V,p1V,pAb))
if__name__=='__main__':
testingNB()
运行结果：
p0V=[-3.25809654-2.56494936-3.25809654-3.25809654-2.56494936-2.56494936
-3.25809654-2.56494936-2.56494936-3.25809654-2.56494936-2.56494936
-2.56494936-2.56494936-1.87180218-2.56494936-2.56494936-2.56494936
-2.56494936-2.56494936-2.56494936-3.25809654-3.25809654-2.56494936
-2.56494936-3.25809654-2.15948425-2.56494936-3.25809654-2.56494936
-3.25809654-3.25809654]
p1V=[-2.35137526-3.04452244-1.94591015-2.35137526-1.94591015-3.04452244
-2.35137526-3.04452244-3.04452244-1.65822808-3.04452244-3.04452244
-2.35137526-3.04452244-3.04452244-3.04452244-3.04452244-3.04452244
-3.04452244-3.04452244-3.04452244-2.35137526-2.35137526-3.04452244
-3.04452244-2.35137526-2.35137526-3.04452244-2.35137526-2.35137526
-2.35137526-2.35137526]
pAb=0.5
[00000000000000100100000000010000]
vec2Classify*p0Vec=[-0. -0. -0. -0. -0. -0. -0.
-0. -0. -0. -0. -0. -0. -0.
-1.87180218-0. -0. -2.56494936-0. -0. -0.
-0. -0. -0. -0. -0. -0.
-2.56494936-0. -0. -0. -0. ]
['love','my','dalmation']classifiedas:0
[00000000010000000000000000000001]
['stupid','garbage']classifiedas:1
#-*-coding:utf-8-*-
#!python2
#使用朴素贝叶斯过滤垃圾邮件
#1.收集数据：提供文本文件
#2.准备数据：讲文本文件见习成词条向量
#3.分析数据：检查词条确保解析的正确性
#4.训练算法：使用我们之前简历的trainNB0()函数
#5.测试算法：使用classifyNB()，并且对建一个新的测试函数来计算文档集的错误率
#6.使用算法，构建一个完整的程序对一组文档进行分类，将错分的文档输出到屏幕上
#importre
#mySent='thisbookisthebestbookonpythonorM.L.Ihvaeeverlaideyesupon.'
#print(mySent.split())
#regEx=re.compile('\\W*')
#print(regEx.split(mySent))
#emailText=open(path+"email\\ham\\6.txt").read()
importnumpyasnp
path=u"C:\\py\\jb51PyDemo\\src\\Demo\\Ch04\\"
defloadDataSet():
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]#1isabusive,0not
returnpostingList,classVec
defcreateVocabList(dataset):
vocabSet=set([])
fordocumentindataset:
vocabSet=vocabSet|set(document)
returnlist(vocabSet)
defsetOfWordseVec(vocabList,inputSet):
returnVec=[0]*len(vocabList)
forwordininputSet:
ifwordinvocabList:
returnVec[vocabList.index(word)]=1#vocabList.index()函数获取vocabList列表某个元素的位置，这段代码得到一个只包含0和1的列表
else:
print("theword:%sisnotinmyVocabulary!"%word)
returnreturnVec
deftrainNB0(trainMatrix,trainCategory):#创建朴素贝叶斯分类器函数
numTrainDocs=len(trainMatrix)
numWords=len(trainMatrix[0])
pAbusive=sum(trainCategory)/float(numTrainDocs)
p0Num=np.ones(numWords);p1Num=np.ones(numWords)
p0Deom=2.0;p1Deom=2.0
foriinrange(numTrainDocs):
iftrainCategory[i]==1:
p1Num+=trainMatrix[i]
p1Deom+=sum(trainMatrix[i])
else:
p0Num+=trainMatrix[i]
p0Deom+=sum(trainMatrix[i])
p1vect=np.log(p1Num/p1Deom)#changetolog
p0vect=np.log(p0Num/p0Deom)#changetolog
returnp0vect,p1vect,pAbusive
defclassifyNB(vec2Classify,p0Vec,p1Vec,pClass1):
p1=sum(vec2Classify*p1Vec)+np.log(pClass1)
p0=sum(vec2Classify*p0Vec)+np.log(1.0-pClass1)
ifp1>p0:
return1
else:
return0
deftextParse(bigString):
importre
listOfTokens=re.split(r'\W*',bigString)
return[tok.lower()fortokinlistOfTokensiflen(tok)>2]
defspamTest():
docList=[];classList=[];fullText=[]
foriinrange(1,26):
wordList=textParse(open(path+"email\\spam\\%d.txt"%i).read())
docList.append(wordList)
fullText.extend(wordList)
classList.append(1)
wordList=textParse(open(path+"email\\ham\\%d.txt"%i).read())
docList.append(wordList)
fullText.extend(wordList)
classList.append(0)
vocabList=createVocabList(docList)
trainingSet=range(50);testSet=[]
foriinrange(10):
randIndex=int(np.random.uniform(0,len(trainingSet)))
testSet.append(trainingSet[randIndex])
del(trainingSet[randIndex])
trainMat=[];trainClasses=[]
fordocIndexintrainingSet:
trainMat.append(setOfWordseVec(vocabList,docList[docIndex]))
trainClasses.append(classList[docIndex])
p0V,p1V,pSpam=trainNB0(np.array(trainMat),np.array(trainClasses))
errorCount=0
fordocIndexintestSet:
wordVector=setOfWordseVec(vocabList,docList[docIndex])
ifclassifyNB(np.array(wordVector),p0V,p1V,pSpam)!=classList[docIndex]:
errorCount+=1
print'theerrorrateis:',float(errorCount)/len(testSet)
if__name__=='__main__':
spamTest()
运行结果：
theerrorrateis:0.0
其中，path路径所使用到的Ch04文件点击此处本站下载。
注：本文算法源自《机器学习实战》一书。
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