基于概率论的分类方法:朴素贝叶斯
2017-11-27 11:16
507 查看
前两章我们要求分类器做出决策,给出“该数据实例属于哪一类”这类问题的明确答案。
不过,分类器有时会产生错误结果,这时可以要求分类器给出一个最优的类别猜测结果,同时给出这个猜测的概率估计值。
假设有一个数据集,由两类数据组成,如下所示
bayes.py
不过,分类器有时会产生错误结果,这时可以要求分类器给出一个最优的类别猜测结果,同时给出这个猜测的概率估计值。
假设有一个数据集,由两类数据组成,如下所示
from numpy import * import feedparser import operator # 返回进行词条切分后的文档集合和人工标注的类别标签的集合 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代表存在侮辱性的文字,0代表不存在 return postingList, classVec # 统计所有文档中出现的词条 def createVocabList(dataSet): vocabSet = set([]) for document in dataSet: # 创建两个集合的并集 vocabSet = 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) # 输出文档向量,向量的每一元素为1或0 # 分别表示词汇表中的单词在输入文档中是否出现 return returnVec def bagOfWords2VecMN(vocabList, inputSet): returnVec = [0] * len(vocabList) for word in inputSet: if word in vocabList: returnVec[vocabList.index(word)] += 1 return returnVec # 朴素贝叶斯分类器训练函数 def trainNB0(trainMatrix, trainCategory): # 获取文档总数 numTrainDocs = len(trainMatrix) # 获取词条向量的长度 numWords = len(trainMatrix[0]) # 类1占所有文档的比例 pAbusive = sum(trainCategory) / float(numTrainDocs) # p0Num=zeros(numWords) # p1Num=zeros(numWords) # p0Denom=0.0 # p1Denom=0.0 p0Num = ones(numWords) p1Num = ones(numWords) p0Denom = 2.0 p1Denom = 2.0 for i in range(numTrainDocs): if trainCategory[i] == 1: # 向量加法,统计所有类别为1的词条向量中各个词条出现的次数 p1Num += trainMatrix[i] # 统计类别为1的词条向量中出现的所有词条的总数 # 即统计类1所有文档中出现单词的数目 p1Denom += sum(trainMatrix[i]) else: p0Num += trainMatrix[i] p0Denom += sum(trainMatrix[i]) # 利用NumPy数组计算p(wi|c1) # p1Vect = p1Num / p1Denom # p0Vect = p0Num / p0Denom 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 textParse(bigString): import re listOfTokens = re.split(r'\W*', bigString) return [tok.lower() for tok in listOfTokens if len(tok) > 2] def spanTest(): docList = [] classList = [] fullText = [] for i in range(1, 26): wordList = textParse(open('email/spam/%d.txt' % i).read()) docList.append(wordList) fullText.extend(wordList) classList.append(1) wordList = textParse(open('email/ham/%d.txt' % i).read()) docList.append(wordList) fullText.extend(wordList) classList.append(0) vocabList = createVocabList(docList) trainingSet = list(range(50)) testSet = [] for i in range(10): randIndex = int(random.uniform(0, len(trainingSet))) testSet.append(trainingSet[randIndex]) del (trainingSet[randIndex]) trainMat = [] trainClasses = [] for docIndex in trainingSet: trainMat.append(setOfWords2Vec(vocabList, docList[docIndex])) trainClasses.append(classList[docIndex]) p0V, p1V, pSpam = trainNB0(array(trainMat), array(trainClasses)) errorCount = 0 for docIndex in testSet: wordVector = setOfWords2Vec(vocabList, docList[docIndex]) if classifyNB(array(wordVector), p0V, p1V, pSpam) != classList[docIndex]: errorCount += 1 print('classification error') print('the error rate is: ', float(errorCount) / len(testSet)) # 实例:使用朴素贝叶斯分类器从个人广告中获取区域倾向 # RSS源分类器及高频词去除函数 def calcMostFreq(vocabList, fullText): freqDict = {} for token in vocabList: # 计算每个单词出现的次数 freqDict[token] = fullText.count(token) # 按照逆序从大到小对freqDict进行排序 sortedFreq = sorted(freqDict.items(), key=operator.itemgetter(1), reverse=True) # 返回前30个高频单词 return sortedFreq[:30] def localWords(feed1, feed0): docList = []; classList = []; fullText = [] # 求两个源长度较小的那个长度值 minLen = min(len(feed1['entries']), len(feed0['entries'])) for i in range(minLen): # 每次访问一条RSS源 wordList = textParse(feed1['entries'][i]['summary']) docList.append(wordList) fullText.extend(wordList) classList.append(1) wordList = textParse(feed0['entries'][i]['summary']) docList.append(wordList) fullText.extend(wordList) classList.append(0) vocabList = createVocabList(docList) # 得到在两个源中出现次数最高的30个单词 top30Words = calcMostFreq(vocabList, fullText) for pairW in top30Words: if pairW[0] in vocabList: # 从词汇表中把高频的30个词移除 vocabList.remove(pairW[0]) trainingSet = list(range(2 * minLen)) testSet = [] # 从两个rss源中挑出20条作为测试文本 for i in range(20): randIndex = int(random.uniform(0, len(trainingSet))) testSet.append(trainingSet[randIndex]) del (trainingSet[randIndex]) trainMat = [] trainClasses = [] # 训练文本 for docIndex in trainingSet: trainMat.append(bagOfWords2VecMN(vocabList, docList[docIndex])) trainClasses.append(classList[docIndex]) p0V, p1V, pSpam = trainNB0(array(trainMat), array(trainClasses)) errorCount = 0 # 计算分类,和错误率 for docIndex in testSet: wordVector = bagOfWords2VecMN(vocabList, docList[docIndex]) if classifyNB(array(wordVector), p0V, p1V, pSpam) != classList[docIndex]: errorCount += 1 print('the error rate is: ', float(errorCount) / len(testSet)) return vocabList, p0V, p1V def getTopWords(ny, sf): # 返回频率大于某个阈值的所有值 vocabList, p0V, p1V = localWords(ny, sf) topNY = [] topSF = [] for i in range(len(p0V)): if p0V[i] > -4.5: topSF.append((vocabList[i], p0V[i])) if p1V[i] > -4.5: topNY.append((vocabList[i], p1V[i])) sortedSF = sorted(topSF, key=lambda pair: pair[1], reverse=True) print("SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF") for item in sortedSF: print(item[0]) sortedNY = sorted(topNY, key=lambda pair: pair[1], reverse=True) print("NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY") for item in sortedNY: print(item[0]) if __name__ == '__main__': listOPosts, listClasses = loadDataSet() myVocabList = createVocabList(listOPosts) print(myVocabList) print(listOPosts[0]) print(setOfWords2Vec(myVocabList, listOPosts[0])) print(listOPosts[3]) print(setOfWords2Vec(myVocabList, listOPosts[3])) trainMat = [] for postinDoc in listOPosts: trainMat.append(setOfWords2Vec(myVocabList, postinDoc)) p0V, p1V, pAb = trainNB0(trainMat, listClasses) print(p0V) print(p1V) print(pAb) 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)) spanTest() spanTest() ny = feedparser.parse('http://newyork.craigslist.org/stp/index.rss') sf = feedparser.parse('http://sfbay.craigslist.org/stp/index.rss') # (ny, sf) getTopWords(ny, sf)
bayes.py
相关文章推荐
- 基于概率论的分类方法:朴素贝叶斯
- 机器学习——基于概率论的分类方法:朴素贝叶斯
- 【机器学习实战】第4章 基于概率论的分类方法:朴素贝叶斯
- 基于概率论的分类方法:朴素贝叶斯
- 机器学习实战---读书笔记: 第4章 基于概率论的分类而方法:朴素贝叶斯
- Machine Learning in Action 学习笔记-(4)基于概率论的分类方法:朴素贝叶斯
- 基于概率论的分类方法:朴素贝叶斯
- 【机器学习实战】第4章 基于概率论的分类方法:朴素贝叶斯
- 基于概率论的分类方法:朴素贝叶斯
- 机器学习实战(四)——基于概率论的分类方法:朴素贝叶斯
- 基于概率论的分类方法:朴素贝叶斯
- [机器学习实战] 基于概率论的分类方法:朴素贝叶斯
- 《机器学习实战》笔记之四——基于概率论的分类方法:朴素贝叶斯
- 机器学习之基于概率论的分类方法 : 朴素贝叶斯
- 机器学习四 -- 基于概率论的分类方法:朴素贝叶斯
- 基于概率论的分类方法--朴素贝叶斯
- Python编程之基于概率论的分类方法:朴素贝叶斯
- 机器学习实战(3)--(基于概率论的分类方法)朴素贝叶斯
- 第四章 基于概率论的分类方法:朴素贝叶斯
- 代码注释:机器学习实战第4章 基于概率论的分类方法:朴素贝叶斯