Naive-Beyes.py

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([])
	for document in dataSet:
		vocabSet|=set(document)
	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

def trainNB0(trainMatrix,trainCategory):
	numTrainDocs=len(trainMatrix)
	numWords=len(trainMatrix[0])
	pAbusive=sum(trainCategory)/float(numTrainDocs)
	p0Num,p1Num=ones(numWords),ones(numWords) #Laplace Smoothing
	p0Denom,p1Denom=2.0,2.0 #Denomation +=2(two categories)
	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)
	p0Vect=log(p0Num/p0Denom)
	return p0Vect,p1Vect,pAbusive

def classifyNB(vec2Classify,p0Vec,p1Vec,pClass1):
	p1=sum(vec2Classify*p1Vec)+log(pClass1)
	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=['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 textParse(bigString):
	import re
	listOfTokens=re.split(r'\W*')
	return [tok.lower() for tok in listOfTokens if len(tok)>2]

def spamTest():
	docList,classList,fullText=[],[],[]