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python实现朴素贝叶斯算法

转载 作者：qq735679552 更新时间：2022-09-27 22:32:09 26 4
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本代码实现了朴素贝叶斯分类器（假设了条件独立的版本），常用于垃圾邮件分类，进行了拉普拉斯平滑.

关于朴素贝叶斯算法原理可以参考博客中原理部分的博文.

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#!/usr/bin/python
# -*- coding: utf-8 -*-
from math import log
from numpy import *
import operator
import matplotlib
import matplotlib.pyplot as plt
from os import listdir
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 ]
   return postingList,classVec
def createVocabList(dataSet):
   vocabSet = set ([]) #create empty set
   for document in dataSet:
     vocabSet = vocabSet | set (document) #union of the two sets
   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):  #训练模型
   numTrainDocs = len (trainMatrix)
   numWords = len (trainMatrix[ 0 ])
   pAbusive = sum (trainCategory) / float (numTrainDocs)
   p0Num = ones(numWords); p1Num = ones(numWords)  #拉普拉斯平滑
   p0Denom = 0.0 + 2.0 ; p1Denom = 0.0 + 2.0      #拉普拉斯平滑
   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)    #用log()是为了避免概率乘积时浮点数下溢
   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 bagOfWords2VecMN(vocabList, inputSet):
   returnVec = [ 0 ] * len (vocabList)
   for word in inputSet:
     if word in vocabList:
       returnVec[vocabList.index(word)] + = 1
   return returnVec
 
def testingNB():  #测试训练结果
   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): # 长字符转转单词列表
   import re
   listOfTokens = re.split(r '\W*' , bigString)
   return [tok.lower() for tok in listOfTokens if len (tok) > 2 ]
 
def spamTest():  #测试垃圾文件 需要数据
   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 = 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(bagOfWords2VecMN(vocabList, docList[docIndex]))
     trainClasses.append(classList[docIndex])
   p0V, p1V, pSpam = trainNB0(array(trainMat), array(trainClasses))
   errorCount = 0
   for docIndex in testSet:
     wordVector = bagOfWords2VecMN(vocabList, docList[docIndex])
     if classifyNB(array(wordVector), p0V, p1V, pSpam) ! = classList[docIndex]:
       errorCount + = 1
       print "classification error" , docList[docIndex]
   print 'the error rate is: ' , float (errorCount) / len (testSet)
 
 
 
listOPosts,listClasses = loadDataSet()
myVocabList = createVocabList(listOPosts)
print myVocabList, '\n'
# print setOfWords2Vec(myVocabList,listOPosts[0]),'\n'
trainMat = []
for postinDoc in listOPosts:
   trainMat.append(setOfWords2Vec(myVocabList,postinDoc))
print trainMat
p0V,p1V,pAb = trainNB0(trainMat,listClasses)
print pAb
print p0V, '\n' ,p1V
testingNB()

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我.

原文链接：https://blog.csdn.net/qq_35083093/article/details/79107514 。

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