朴素贝叶斯算法的python实现 -- 机器学习实战

  1 import numpy as np
  2 import re
  3 
  4 #词表到向量的转换函数
  5 def loadDataSet():
  6     postingList = [[‘my‘, ‘dog‘, ‘has‘, ‘flea‘, ‘problems‘, ‘help‘, ‘please‘],
  7                  [‘maybe‘, ‘not‘, ‘take‘, ‘him‘, ‘to‘, ‘dog‘, ‘park‘, ‘stupid‘],
  8                  [‘my‘, ‘dalmation‘, ‘is‘, ‘so‘, ‘cute‘, ‘I‘, ‘love‘, ‘him‘],
  9                  [‘stop‘, ‘posting‘, ‘stupid‘, ‘worthless‘, ‘garbage‘],
 10                  [‘mr‘, ‘licks‘, ‘ate‘, ‘my‘, ‘steak‘, ‘how‘, ‘to‘, ‘stop‘, ‘him‘],
 11                  [‘quit‘, ‘buying‘, ‘worthless‘, ‘dog‘, ‘food‘, ‘stupid‘]]
 12     classVec =[0,1,0,1,0,1] #1代表侮辱性文字，0代表正常言论
 13     return postingList, classVec
 14 
 15 #创建一个包含在所有文档中出现的不重复词的列表
 16 def createVocabList(dataSet):
 17     vocabSet = set([])  #创建一个空集
 18     for document in dataSet:
 19         vocabSet = vocabSet | set(document) #创建两个集合的并集
 20     return list(vocabSet)
 21 
 22 #词集模型：文档中的每个词在词集中只出现一次
 23 def setOfWords2Vec(vocabList, inputSet):
 24     returnVec = [0] * len(vocabList)    #创建长度与词汇表相同，元素都为0的向量
 25     for word in inputSet:
 26         if word in vocabList:   #将出现在文档中的词汇在词汇表中对应词汇位置置1
 27             returnVec[vocabList.index(word)] = 1
 28         else:
 29             print ("the word: %s isn‘t in my Vocabulary" % (word))
 30     return returnVec
 31 
 32 #词袋模型: 文档中的每个词在词袋中可以出现多次
 33 def bagOfWords2VecMN(vocabList, inputSet):
 34     returnVec = [0] * len(vocabList)
 35     for word in inputSet:
 36         if word in vocabList:
 37             returnVec[vocabList.index(word)] += 1
 38     return returnVec
 39 
 40 #朴素贝叶斯分类器训练函数
 41 def trainNB0(trainMatrix, trainCategory):
 42     numTrainDocs = len(trainMatrix)
 43     numWords = len(trainMatrix[0])
 44     pAbusive = sum(trainCategory)/float(numTrainDocs)
 45     #p0Num = np.zeros(numWords)
 46     #p1Num = np.zeros(numWords)
 47     #p0Denom = 0.0
 48     #p1Denom = 0.0
 49     p0Num = np.ones(numWords)       #|利用贝叶斯分类器对文档进行分类时，要计算多个概率的乘积以获得文档属于某个类别的概率，
 50     p1Num = np.ones(numWords)       #|如果其中一个概率值为0，那么最后的乘积也为0.
 51     p0Denom = 2.0                   #|为降低这种影响，可以将所有词的出现数初始化为1，并将分母初始化为2
 52     p1Denom = 2.0                   #|(拉普拉斯平滑)
 53     for i in range(numTrainDocs):
 54         if trainCategory[i] == 1:
 55             p1Num += trainMatrix[i]
 56             p1Denom += sum(trainMatrix[i])
 57         else:
 58             p0Num += trainMatrix[i]
 59             p0Denom += sum(trainMatrix[i])
 60     #p1Vect = p1Num/p1Denom
 61     #p0Vect = p0Num/p0Denom
 62     p1Vect = np.log(p1Num/p1Denom)  #|当太多很小的数相乘时，程序会下溢出，对乘积取自然对数可以避免下溢出或浮点数舍入导致的错误
 63     p0Vect = np.log(p0Num/p0Denom)  #|同时，采用自然对数进行处理不会有任何损失。ln(a*b)=ln(a)+ln(b)
 64     return p0Vect, p1Vect, pAbusive
 65 
 66 #朴素贝叶斯分类函数
 67 def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):
 68     p1 = sum(vec2Classify * p1Vec) + np.log(pClass1)    #元素相乘得到概率值
 69     p0 = sum(vec2Classify * p0Vec) + np.log(1.0 - pClass1)
 70     if p1 > p0:
 71         return 1
 72     else:
 73         return 0
 74 
 75 #便利函数，封装所有操作
 76 def testingNB():
 77     listOposts, listClasses = loadDataSet()
 78     myVocabList = createVocabList(listOposts)
 79     trainMat = []
 80     for postinDoc in listOposts:
 81         trainMat.append(setOfWords2Vec(myVocabList, postinDoc))
 82     p0V, p1V, pAb = trainNB0(np.array(trainMat), np.array(listClasses)) #获取训练文档返回的概率值
 83     testEntry = [‘love‘, ‘my‘, ‘dalmation‘] #正面测试文档
 84     thisDoc = np.array(setOfWords2Vec(myVocabList, testEntry))  #词汇表
 85     print (testEntry, ‘classified as:‘, classifyNB(thisDoc, p0V, p1V, pAb)) #分类结果
 86     testEntry = [‘stupid‘, ‘garbage‘]   #侮辱性测试文档
 87     thisDoc = np.array(setOfWords2Vec(myVocabList, testEntry))  #词汇表
 88     print (testEntry, ‘classified as:‘, classifyNB(thisDoc, p0V, p1V, pAb)) #分类结果
 89 
 90 #文件解析
 91 def textParse(bigString):
 92     listOfTokens = re.split(r‘\W+‘, bigString) #原书中的模式为\W*,匹配0个或多个
 93     return [tok.lower() for tok in listOfTokens if len(tok) > 2]
 94 
 95 #完整的垃圾邮件测试函数
 96 def spamTest():
 97     docList=[]; classList=[]; fullText=[]
 98     for i in range(1, 26):  #导入并解析文件
 99         wordList = textParse(open(‘email/spam/%d.txt‘ % i).read())
100         docList.append(wordList)
101         fullText.extend(wordList)
102         classList.append(1)
103         wordList = textParse(open(‘email/ham/%d.txt‘ % i).read())
104         docList.append(wordList)
105         fullText.extend(wordList)
106         classList.append(0)
107     vocabList = createVocabList(docList)
108     trainingSet = list(range(50)); testSet=[]
109     for i in range(10):     #随机构建训练集与测试集
110         randIndex = int(np.random.uniform(0, len(trainingSet)))
111         testSet.append(trainingSet[randIndex])
112         del(trainingSet[randIndex])
113     trainMat=[]; trainClasses=[]
114     for docIndex in trainingSet:
115         trainMat.append(setOfWords2Vec(vocabList, docList[docIndex]))
116         trainClasses.append(classList[docIndex])
117     p0V, p1V, pSpam = trainNB0(np.array(trainMat), np.array(trainClasses))
118     errorCount = 0
119     for docIndex in testSet:    #对测试集分类并计算错误率
120         wordVector = setOfWords2Vec(vocabList, docList[docIndex])
121         if classifyNB(np.array(wordVector), p0V, p1V, pSpam) != classList[docIndex]:
122             errorCount += 1
123     print (‘The error rate is: ‘, float(errorCount/len(testSet)))
124 
125 #Simple unit test of func: loadDataSet(), createVocabList(), setOfWords2Vec
126 #listOPosts, listClassed = loadDataSet()
127 #myVocabList =createVocabList(listOPosts)
128 #print (myVocabList)
129 #res = setOfWords2Vec(myVocabList, listOPosts[0])
130 #print (res)
131 
132 #Simple unit test of func: trainNB0()
133 #listOposts, listClasses = loadDataSet()
134 #myVocabList = createVocabList(listOposts)
135 #trainMat = []
136 #for postinDoc in listOposts:
137 #    trainMat.append(setOfWords2Vec(myVocabList, postinDoc))
138 #p0V, p1V, pAb = trainNB0(trainMat, listClasses)
139 #print (p0V); print (p1V); print (pAb)
140 
141 #Simple unit test of func: testingNB()
142 #testingNB()
143 
144 spamTest()