使用Python进行文本分类

注：下面写明的是整体思路，后面附上综合代码

1、准备数据：从文本中构建词向量

（1）词表到向量的转换函数

**创建一些实验样本

loadDataSet()

**创建一个包含在所有文档中出现的不重复词的列表

createVocabList(dataSet)

该函数的输入参数为词汇表及某个文档，输出的是文档向量，向量的每一个元素为1或0，分别表示词汇表中的单词在输入文档中是否出现。又被称为词集模型

setOfWords2Vec()

2、训练算法：从词向量计算概率

**朴素贝叶斯分类器训练函数，文档矩阵trainMatrix，每篇文档类别所构成的向量trainCategory

trainNB0(trainMatrix,trainCategory)

3、测试算法：根据现实情况修改分类器

一个是计算多个概率的乘积以获得文档属于某个类别的概率时，如果其中一个概率值为0，那么最后的乘积也为0。

另一个是下溢出，这是由于太多很小的书相乘造成的。

**要分类的向量vec2Classify，以及使用函数trainNB0()计算得到的三个概率

classifyNB(vec2Classify,p0Vec,p1Vec,pClass1)

**一个便利函数，该函数封装所有操作，以节省代码输入的时间

testingNB()

4、准备数据：文档词袋模型

**朴素贝叶斯词袋模型

注：词袋模型与词集模型几乎完全相同，唯一不同的是当遇到一个单词时，它会增加词向量中的对应值，而不只是将对应的数值设为1.

bagOfWords2VecMN(vocabList,inputSet)

示例1：使用贝叶斯过滤垃圾邮件

**接受一个大字符串并将其解析为字符串列表。该函数去掉少于两个字符的字符串，并将所有字符串转换为小写。

textParse(bigString)

**对贝叶斯垃圾邮件分类器进行自动化处理。

<1>导入并解析文本文件

<2>随机构建训练集

<3>对测试集分类

spamTest()

示例2：使用朴素贝叶斯分类器从个人广告中获取区域倾向

(1)收集数据：导入RSS源

**该函数遍历词汇表中的每个词并统计它在文本中出现的次数，然后根据出现次数从高到低对词典进行排序，最后返回排序最高的30个单词

calcMostFreq(vocabList,fullText)

**每次访问一条RSS源，并去掉出现次数最高的那些词

localWords（feed1,feed0)

(2)显示地域相关的用词

**使用两个RSS源作为输入，然后训练并测试朴素贝叶斯分类器，返回使用的概率值。然后创建两个列表用于元组的存储。与之前返回排名最高的X个单词不同，这里可以返回大于某个阈值的所有值。这些元素会按照它们的条件概率进行排序。

getTopWords(ny,sf)

具体代码参考如下，自行对照上面介绍的函数名称带入：

from numpy import *

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 is abusive, 0 not
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):
numTrainDocs = len(trainMatrix)
numWords = len(trainMatrix[0])
pAbusive = sum(trainCategory)/float(numTrainDocs)
p0Num = ones(numWords); p1Num = ones(numWords)      #change to ones()
p0Denom = 2.0; p1Denom = 2.0                        #change to 2.0
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)          #change to log()
p0Vect = log(p0Num/p0Denom)          #change to log()
return p0Vect,p1Vect,pAbusive

def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):
p1 = sum(vec2Classify * p1Vec) + log(pClass1)    #element-wise mult
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():
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):    #input is big string, #output is word list
import re
listOfTokens = re.split(r'\W*', bigString)
return [tok.lower() for tok in listOfTokens if len(tok) > 2]

def spamTest():
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)#create vocabulary
trainingSet = range(50); testSet=[]           #create test set
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:#train the classifier (get probs) trainNB0
trainMat.append(bagOfWords2VecMN(vocabList, docList[docIndex]))
trainClasses.append(classList[docIndex])
p0V,p1V,pSpam = trainNB0(array(trainMat),array(trainClasses))
errorCount = 0
for docIndex in testSet:        #classify the remaining items
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)
#return vocabList,fullText

def calcMostFreq(vocabList,fullText):
import operator
freqDict = {}
for token in vocabList:
freqDict[token]=fullText.count(token)
sortedFreq = sorted(freqDict.iteritems(), key=operator.itemgetter(1), reverse=True)
return sortedFreq[:30]

def localWords(feed1,feed0):
import feedparser
docList=[]; classList = []; fullText =[]
minLen = min(len(feed1['entries']),len(feed0['entries']))
for i in range(minLen):
wordList = textParse(feed1['entries'][i]['summary'])
docList.append(wordList)
fullText.extend(wordList)
classList.append(1) #NY is class 1
wordList = textParse(feed0['entries'][i]['summary'])
docList.append(wordList)
fullText.extend(wordList)
classList.append(0)
vocabList = createVocabList(docList)#create vocabulary
top30Words = calcMostFreq(vocabList,fullText)   #remove top 30 words
for pairW in top30Words:
if pairW[0] in vocabList: vocabList.remove(pairW[0])
trainingSet = range(2*minLen); testSet=[]           #create test set
for i in range(20):
randIndex = int(random.uniform(0,len(trainingSet)))
testSet.append(trainingSet[randIndex])
del(trainingSet[randIndex])
trainMat=[]; trainClasses = []
for docIndex in trainingSet:#train the classifier (get probs) trainNB0
trainMat.append(bagOfWords2VecMN(vocabList, docList[docIndex]))
trainClasses.append(classList[docIndex])
p0V,p1V,pSpam = trainNB0(array(trainMat),array(trainClasses))
errorCount = 0
for docIndex in testSet:        #classify the remaining items
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):
import operator
vocabList,p0V,p1V=localWords(ny,sf)
topNY=[]; topSF=[]
for i in range(len(p0V)):
if p0V[i] > -6.0 : topSF.append((vocabList[i],p0V[i]))
if p1V[i] > -6.0 : 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**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**NY**NY**NY**NY**NY**"
for item in sortedNY:
print item[0]