def testingNB():
listOfPosts, listClasses = bayes.loadDataSet()
myVocabList = bayes.createVocabList(listOfPosts)
trainMatrix = []
for post in listOfPosts:
trainMatrix.append(bayes.setOfWordsToVector(myVocabList, post))
p0,p1, pAbusive = bayes.trainNB(array(trainMatrix), array(listClasses))
testEntry = ['love', 'my', 'dalmation']
thisDoc = array(bayes.setOfWordsToVector(myVocabList, testEntry))
print(testEntry, 'classified as: ', bayes.classifyNB(thisDoc, p0, p1, pAbusive))
testEntry = ['stupid', 'garbage']
thisDoc = array(bayes.setOfWordsToVector(myVocabList, testEntry))
print(testEntry, 'classified as: ', bayes.classifyNB(thisDoc, p0, p1, pAbusive))
def localWords(feed0, feed1):
import feedparser
import numpy as np
docList=[]
classList=[]
fullText=[]
minLen = min(len(feed1['entries']), len(feed0['entries']))
for i in range(minLen):
wordList = bayes.textParse(feed1['entries'][i]['summary'])
docList.append(wordList)
fullText.extend(wordList)
classList.append(1)
wordList = bayes.textParse(feed0['entries'][i]['summary'])
docList.append(wordList)
fullText.extend(wordList)
classList.append(0)
vocabList = bayes.createVocabList(docList)
top30Words = calculateMostFrequentValues(vocabList, fullText)
for pairW in top30Words:
if pairW[0] in vocabList: vocabList.remove(pairW[0])
trainingset = list(range(2*minLen))
testSet = []
for i in range(20):
randIndex = int(np.random.uniform(0, len(trainingset)))
testSet.append(trainingset[randIndex])
del(trainingset[randIndex])
trainMat = []
trainClasses = []
for docIndex in trainingset:
trainMat.append(bayes.bagOfWordsToVetor(vocabList, docList[docIndex]))
trainClasses.append(classList[docIndex])
p0v, p1v, pSpam = bayes.trainNB(np.asarray(trainMat), np.asarray(trainClasses))
errorCount = 0
for docIndex in testSet:
wordVector = bayes.bagOfWordsToVetor(vocabList, docList[docIndex])
if bayes.classifyNB(np.asarray(wordVector), p0v, p1v, pSpam) != classList[docIndex]:
errorCount +=1
print('the error rate is: ', float(errorCount)/len(testSet))
return vocabList, p0v, p1v
