python编写朴素贝叶斯用于文本分类
朴素贝叶斯估计 朴素贝叶斯是基于贝叶斯定理与特征条件独立分布假设的分类方法。首先根据特征条件独立的假设学习输入/输出的联合概率分布,然后基于此模型,对给定的输入x,利用贝叶斯定理求出后验概率最大的输出y。 以及条件概率为 Xl表示第l个特征,由于特征条件独立的假设,可得 条件概率的极大似然估计为 根据贝叶斯定理 则由上式可以得到条件概率P(Y=ck|X=x)。 贝叶斯估计 用极大似然估计可能会出现所估计的概率为0的情况。后影响到后验概率结果的计算,使分类产生偏差。采用如下方法解决。 其中Sl表示第l个特征可能取值的个数。 $$ 表示Y的所有可能取值的个数,即类型的个数。 文本分类 朴素贝叶斯分类器可以给出一个最有结果的猜测值,并给出估计概率。通常用于文本分类。 词条:将每个词出现的次数作为特征。 完整代码如下; import numpy as np import re import feedparser import operator def loadDataSet(): postingList=[['my','dog','has','flea','problems','help','please'],['maybe','not','take','him','to','park','stupid'],['my','dalmation','is','so','cute','I','love','him'],['stop','posting','stupid','worthless','garbage'],['mr','licks','ate','my','steak','how','stop',['quit','buying','food','stupid']] classVec = [0,1,1] #1 is abusive,0 not return postingList,classVec def createVocabList(data): #创建词向量 returnList = set([]) for subdata in data: returnList = returnList | set(subdata) return list(returnList) def setofWords2Vec(vocabList,data): #将文本转化为词条 returnList = [0]*len(vocabList) for vocab in data: if vocab in vocabList: returnList[vocabList.index(vocab)] += 1 return returnList def trainNB0(trainMatrix,trainCategory): #训练,得到分类概率 pAbusive = sum(trainCategory)/len(trainCategory) p1num = np.ones(len(trainMatrix[0])) p0num = np.ones(len(trainMatrix[0])) p1Denom = 2 p0Denom = 2 for i in range(len(trainCategory)): if trainCategory[i] == 1: p1num = p1num + trainMatrix[i] p1Denom = p1Denom + sum(trainMatrix[i]) else: p0num = p0num + trainMatrix[i] p0Denom = p0Denom + sum(trainMatrix[i]) p1Vect = np.log(p1num/p1Denom) p0Vect = np.log(p0num/p0Denom) return p0Vect,p1Vect,pAbusive def classifyNB(vec2Classify,p0Vec,p1Vec,pClass1): #分类 p0 = sum(vec2Classify*p0Vec)+np.log(1-pClass1) p1 = sum(vec2Classify*p1Vec)+np.log(pClass1) if p1 > p0: return 1 else: return 0 def textParse(bigString): #文本解析 splitdata = re.split(r'W+',bigString) splitdata = [token.lower() for token in splitdata if len(token) > 2] return splitdata def spamTest(): docList = [] classList = [] for i in range(1,26): with open('spam/%d.txt'%i) as f: doc = f.read() docList.append(doc) classList.append(1) with open('ham/%d.txt'%i) as f: doc = f.read() docList.append(doc) classList.append(0) vocalList = createVocabList(docList) trainList = list(range(50)) testList = [] for i in range(13): num = int(np.random.uniform(0,len(docList))-10) testList.append(trainList[num]) del(trainList[num]) docMatrix = [] docClass = [] for i in trainList: subVec = setofWords2Vec(vocalList,docList[i]) docMatrix.append(subVec) docClass.append(classList[i]) p0v,p1v,pAb = trainNB0(docMatrix,docClass) errorCount = 0 for i in testList: subVec = setofWords2Vec(vocalList,docList[i]) if classList[i] != classifyNB(subVec,p0v,pAb): errorCount += 1 return errorCount/len(testList) def calcMostFreq(vocabList,fullText): count = {} for vocab in vocabList: count[vocab] = fullText.count(vocab) sortedFreq = sorted(count.items(),key=operator.itemgetter(1),reverse=True) return sortedFreq[:30] def localWords(feed1,feed0): docList = [] classList = [] fullText = [] numList = min(len(feed1['entries']),len(feed0['entries'])) for i in range(numList): doc1 = feed1['entries'][i]['summary'] docList.append(doc1) classList.append(1) fullText.extend(doc1) doc0 = feed0['entries'][i]['summary'] docList.append(doc0) classList.append(0) fullText.extend(doc0) vocabList = createVocabList(docList) top30Words = calcMostFreq(vocabList,fullText) for word in top30Words: if word[0] in vocabList: vocabList.remove(word[0]) trainingSet = list(range(2*numList)) testSet = [] for i in range(20): randnum = int(np.random.uniform(0,len(trainingSet)-5)) testSet.append(trainingSet[randnum]) del(trainingSet[randnum]) trainMat = [] trainClass = [] for i in trainingSet: trainClass.append(classList[i]) trainMat.append(setofWords2Vec(vocabList,docList[i])) p0V,p1V,pSpam = trainNB0(trainMat,trainClass) errCount = 0 for i in testSet: testData = setofWords2Vec(vocabList,docList[i]) if classList[i] != classifyNB(testData,p0V,pSpam): errCount += 1 return errCount/len(testData) if __name__=="__main__": ny = feedparser.parse('http://newyork.craigslist.org/stp/index.rss') sf = feedparser.parse('http://sfbay.craigslist.org/stp/index.rss') print(localWords(ny,sf)) 编程技巧: 1.两个集合的并集 vocab = vocab | set(document) 2.创建元素全为零的向量 vec = [0]*10 代码及数据集下载:贝叶斯 以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持编程小技巧。 (编辑:李大同) 【声明】本站内容均来自网络,其相关言论仅代表作者个人观点,不代表本站立场。若无意侵犯到您的权利,请及时与联系站长删除相关内容! |