python实现朴素贝叶斯算法

#!/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()