【4858.com】机械学习实战,汉语词云代码调节和测试

By admin in 4858.com on 2019年4月3日

词云是个很风趣的东西。

华语词云代码调节和测试,中文调试

词云是个很有趣的东西。

用jieba断词,小说文本存入”mori.txt”,停用词列表在”stopword.txt”中,断词结果好坏,停用词很要紧,须求持续调整补充。

from wordcloud import WordCloud
import jieba

f = open(u'mori.txt','r').read()
##cuttext=" ".join(jieba.cut(f))
cuttext= jieba.cut(f) 
final= [] 
stopwords=open(u'stopword.txt','r').read() 

for seg in cuttext:
    ##seg = seg.encode('utf-8')
    if seg[0] not  in ['0','1','2','3','4','5','6','7','8','9']:##忽略数字
        if seg not in stopwords:
            final.append( seg) ## 列表添加   

font=r"c:/Windows/Fonts/simsun.ttc"##中文显示必须加
wordcloud = WordCloud(font_path=font,background_color="white",width=1000, height=860, margin=2).generate(" ".join(final))

import matplotlib.pyplot as plt
plt.imshow(wordcloud)
plt.axis("off")
plt.show()

  wordcloud.to_file(‘test.png’)

效果图:

4858.com 1

下边是词频计算排序,词长排序的代码。

##统计词频
freqD2 = {}
for word2 in final:
  freqD2[word2] = freqD2.get(word2, 0) + 1 

##按词频排序输出
counter_list = sorted(freqD2.items(), key=lambda x: x[1], reverse=True) 
_2000=counter_list[0][1] + 1
print(_2000)##用于词长词频排序用
fp = open('sort.txt',"w+",encoding='utf-8')
for d in counter_list:
  fp.write(d[0]+':'+str(d[1]))
  fp.write('\n')
fp.close()

##按词长词频排序输出
counter_list = sorted(freqD2.items(), key=lambda x: len(x[0])*_2000+x[1], reverse=True) 
fp = open('sortlen.txt',"w+",encoding='utf-8')
for d in counter_list:
  fp.write(d[0]+':'+str(d[1]))
  fp.write('\n')
fp.close()

排序代码很便利,也值得借鉴,Python是个好东西,强大,易重用。

 

词云是个很风趣的东西。
用jieba断词,小说文本存入”mori.txt”,停用词列表在”stopword.txt”中,断词结果好坏,…

《机器学习实战》中贝叶斯分类中程导弹入奥迪Q3SS源例子,机器学习实战

继之书中代码往下写在这边卡住了,大概还会有其他同学也碰到了这么的难点,记下来分享。

 

随即书中代码往下写在那边卡住了,思量到大概还会有其余同学也遇上了如此的标题,记下来分享。

用jieba断词,随笔文本存入”mori.txt”,停用词列表在”stopword.txt”中,断词结果好坏,停用词很重大,须求不停调整补充。

怎么设置feedparser?

按书中提供的网站直接设置feedparser会提醒错误说未有setuptools,然后去找setuptools,官方的传教是windows最棒用ez_setup.py安装,小编真正下载不下去官网的要命ez_etup.py,那一个帖子给出了缓解方案:

ez_setup.py

将这么些文件一向拷贝到C:\\python27文件夹中,输入命令行:python
ez_setup.py install

然后转到放feedparser安装文件的文件夹中,命令行输入:python setup.py
install

 

先吐槽一下,相信当先4六%网络好友在此间卡住的主要缘由是巨大的GFW,所以随便软件FQ依旧肉身FQ的同伴们预计是无论怎么着也看不到那篇博文的,不想往下看的请自觉使用FQ技能。

from wordcloud import WordCloud
import jieba

f = open(u'mori.txt','r').read()
##cuttext=" ".join(jieba.cut(f))
cuttext= jieba.cut(f) 
final= [] 
stopwords=open(u'stopword.txt','r').read() 

for seg in cuttext:
    ##seg = seg.encode('utf-8')
    if seg[0] not  in ['0','1','2','3','4','5','6','7','8','9']:##忽略数字
        if seg not in stopwords:
            final.append( seg) ## 列表添加   

font=r"c:/Windows/Fonts/simsun.ttc"##中文显示必须加
wordcloud = WordCloud(font_path=font,background_color="white",width=1000, height=860, margin=2).generate(" ".join(final))

import matplotlib.pyplot as plt
plt.imshow(wordcloud)
plt.axis("off")
plt.show()

作者提供的奥德赛SS源链接“

书中小编的情趣是的话自源
中的作品作为分类为一的篇章,以来自源
中的小说作为分类为0的篇章

为了能够跑通示例代码,可以找两可用的凯雷德SS源作为代表。

自己用的是那多少个源:

NASA Image of the
Day:

Yahoo Sports – NBA – Houston Rockets
News:

也正是说,要是算法运维正确的话,全数来自于 nasa
的篇章将会被分门别类为1,全体来自于yahoo sports的休斯顿火箭队(休斯敦 罗克ets)情报将会分类为0

 

 

  wordcloud.to_file(‘test.png’)

运用本人定义的CRUISERSS源,当程序运维到trainNB0(array(trainMat),array(trainClasses))时会报错,如何是好?

从书中作者的例子来看,笔者运用的源粤语章数量较多,len(ny[‘entries’])
为 100,作者找的多少个 BMWX三SS 源唯有10-1几个左右。

>>> import feedparser
>>>ny=feedparser.parse(”)
>>> ny[‘entries’]
>>> len(ny[‘entries’])
100

因为笔者的1个哈弗SS源有100篇小说,所以他得以在代码中剔除了2伍个“停用词”,随机挑选20篇文章作为测试集,但是当大家应用替代凯雷德SS源时大家只有拾篇小说却要取出20篇小说作为测试集,那样让人惊讶是会出错的。只要本身调整下测试集的数码就能够让代码跑通;如若小说中的词太少,收缩剔除的“停用词”数量能够提升算法的准确度。

 

怎么设置feedparser?

按书中提供的网站直接设置feedparser会提示错误说并未有setuptools,然后去找setuptools,官方的传教是windows最佳用ez_setup.py安装,作者真正下载不下来官网的尤其ez_etup.py,那些帖子给出了缓解方案:

ez_setup.py

将这些文件直接拷贝到C:\\python27文件夹中,输入命令行:python
ez_setup.py install

下一场转到放feedparser安装文件的公文夹中,命令行输入:python setup.py
install

 

效果图:

若是不想将应运而生频率排序最高的二十九个单词移除,该怎么去掉“停用词”?

能够把要去掉的停用词存放到txt文件中,使用时读取(替代移除高频词的代码)。具体要求停用哪些词能够参照那里

以下代码想符合规律运营需求将停用词保存至stopword.txt中。

小编的txt中保留了以下单词,效果还能够:

a
about
above
after
again
against
all
am
an
and
any
are
aren’t
as
at
be
because
been
before
being
below
between
both
but
by
can’t
cannot
could
couldn’t
did
didn’t
do
does
doesn’t
doing
don’t
down
during
each
few
for
from
further
had
hadn’t
has
hasn’t
have
haven’t
having
he
he’d
he’ll
he’s
her
here
【4858.com】机械学习实战,汉语词云代码调节和测试。here’s
hers
herself
him
himself
his
how
4858.com ,how’s
i
i’d
i’ll
i’m
i’ve
if
in
into
is
isn’t
it
it’s
its
itself
let’s
me
more
most
mustn’t
my
myself
no
nor
not
of
off
on
once
only
or
other
ought
our
ours
ourselves
out
over
own
same
shan’t
she
she’d
she’ll
she’s
should
shouldn’t
so
some
such
than
that
that’s
the
their
theirs
them
themselves
then
there
there’s
these
they
they’d
they’ll
they’re
they’ve
this
those
through
to
too
under
until
up
very
was
wasn’t
we
we’d
we’ll
we’re
we’ve
were
weren’t
what
what’s
when
when’s
where
where’s
which
while
who
who’s
whom
why
why’s
with
won’t
would
wouldn’t
you
you’d
you’ll
you’re
you’ve
your
yours
yourself
yourselves

 

'''
Created on Oct 19, 2010

@author: Peter
'''
from numpy import *

def loadDataSet():
    postingList=[['my', 'dog', 'has', 'flea', 'problems', 'help','my','dog', '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 bagOfWords2Vec(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():
    print '*** load dataset for training ***'
    listOPosts,listClasses = loadDataSet()
    print 'listOPost:\n',listOPosts
    print 'listClasses:\n',listClasses
    print '\n*** create Vocab List ***'
    myVocabList = createVocabList(listOPosts)
    print 'myVocabList:\n',myVocabList
    print '\n*** Vocab show in post Vector Matrix ***'
    trainMat=[]
    for postinDoc in listOPosts:
        trainMat.append(bagOfWords2Vec(myVocabList, postinDoc))
    print 'train matrix:',trainMat
    print '\n*** train P0V p1V pAb ***'
    p0V,p1V,pAb = trainNB0(array(trainMat),array(listClasses))
    print 'p0V:\n',p0V
    print 'p1V:\n',p1V
    print 'pAb:\n',pAb
    print '\n*** classify ***'
    testEntry = ['love', 'my', 'dalmation']
    thisDoc = array(bagOfWords2Vec(myVocabList, testEntry))
    print testEntry,'classified as: ',classifyNB(thisDoc,p0V,p1V,pAb)
    testEntry = ['stupid', 'garbage']
    thisDoc = array(bagOfWords2Vec(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 stopWords():
    import re
    wordList =  open('stopword.txt').read() # see http://www.ranks.nl/stopwords
    listOfTokens = re.split(r'\W*', wordList)
    return [tok.lower() for tok in listOfTokens] 
    print 'read stop word from \'stopword.txt\':',listOfTokens
    return listOfTokens

def localWords(feed1,feed0):
    import feedparser
    docList=[]; classList = []; fullText =[]
    print 'feed1 entries length: ', len(feed1['entries']), '\nfeed0 entries length: ', len(feed0['entries'])
    minLen = min(len(feed1['entries']),len(feed0['entries']))
    print '\nmin Length: ', minLen
    for i in range(minLen):
        wordList = textParse(feed1['entries'][i]['summary'])
        print '\nfeed1\'s entries[',i,']\'s summary - ','parse text:\n',wordList
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(1) #NY is class 1
        wordList = textParse(feed0['entries'][i]['summary'])
        print '\nfeed0\'s entries[',i,']\'s summary - ','parse text:\n',wordList
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(0)
    vocabList = createVocabList(docList)#create vocabulary
    print '\nVocabList is ',vocabList
    print '\nRemove Stop Word:'
    stopWordList = stopWords()
    for stopWord in stopWordList:
        if stopWord in vocabList:
            vocabList.remove(stopWord)
            print 'Removed: ',stopWord
##    top30Words = calcMostFreq(vocabList,fullText)   #remove top 30 words
##    print '\nTop 30 words: ', top30Words
##    for pairW in top30Words:
##        if pairW[0] in vocabList:
##            vocabList.remove(pairW[0])
##            print '\nRemoved: ',pairW[0]
    trainingSet = range(2*minLen); testSet=[]           #create test set
    print '\n\nBegin to create a test set: \ntrainingSet:',trainingSet,'\ntestSet',testSet
    for i in range(5):
        randIndex = int(random.uniform(0,len(trainingSet)))
        testSet.append(trainingSet[randIndex])
        del(trainingSet[randIndex])
    print 'random select 5 sets as the testSet:\ntrainingSet:',trainingSet,'\ntestSet',testSet
    trainMat=[]; trainClasses = []
    for docIndex in trainingSet:#train the classifier (get probs) trainNB0
        trainMat.append(bagOfWords2VecMN(vocabList, docList[docIndex]))
        trainClasses.append(classList[docIndex])
    print '\ntrainMat length:',len(trainMat)
    print '\ntrainClasses',trainClasses
    print '\n\ntrainNB0:'
    p0V,p1V,pSpam = trainNB0(array(trainMat),array(trainClasses))
    #print '\np0V:',p0V,'\np1V',p1V,'\npSpam',pSpam
    errorCount = 0
    for docIndex in testSet:        #classify the remaining items
        wordVector = bagOfWords2VecMN(vocabList, docList[docIndex])
        classifiedClass = classifyNB(array(wordVector),p0V,p1V,pSpam)
        originalClass = classList[docIndex]
        result =  classifiedClass != originalClass
        if result:
            errorCount += 1
        print '\n',docList[docIndex],'\nis classified as: ',classifiedClass,', while the original class is: ',originalClass,'. --',not result
    print '\nthe error rate is: ',float(errorCount)/len(testSet)
    return vocabList,p0V,p1V

def testRSS():
    import feedparser
    ny=feedparser.parse('http://www.nasa.gov/rss/dyn/image_of_the_day.rss')
    sf=feedparser.parse('http://sports.yahoo.com/nba/teams/hou/rss.xml')
    vocabList,pSF,pNY = localWords(ny,sf)

def testTopWords():
    import feedparser
    ny=feedparser.parse('http://www.nasa.gov/rss/dyn/image_of_the_day.rss')
    sf=feedparser.parse('http://sports.yahoo.com/nba/teams/hou/rss.xml')
    getTopWords(ny,sf)

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]

def test42():
    print '\n*** Load DataSet ***'
    listOPosts,listClasses = loadDataSet()
    print 'List of posts:\n', listOPosts
    print 'List of Classes:\n', listClasses

    print '\n*** Create Vocab List ***'
    myVocabList = createVocabList(listOPosts)
    print 'Vocab List from posts:\n', myVocabList

    print '\n*** Vocab show in post Vector Matrix ***'
    trainMat=[]
    for postinDoc in listOPosts:
        trainMat.append(bagOfWords2Vec(myVocabList,postinDoc))
    print 'Train Matrix:\n', trainMat

    print '\n*** Train ***'
    p0V,p1V,pAb = trainNB0(trainMat,listClasses)
    print 'p0V:\n',p0V
    print 'p1V:\n',p1V
    print 'pAb:\n',pAb

我提供的昂CoraSS源链接“

书中作者的情致是的话自源
中的小说作为分类为一的小说,以来自源
中的文章作为分类为0的稿子

为了能够跑通示例代码,能够找两可用的RubiconSS源作为代表。

自个儿用的是那三个源:

NASA Image of the
Day:

Yahoo Sports – NBA – Houston Rockets
News:

也正是说,借使算法运转正确的话,全数来自于 nasa
的稿子将会被分类为1,全体来自于yahoo sports的休斯顿休斯敦火箭音信将会分类为0

 

4858.com 2

选拔本身定义的讴歌MDXSS源,当程序运维到trainNB0(array(trainMat),array(trainClasses))时会报错,如何是好?

从书中小编的例证来看,我利用的源中文章数量较多,len(ny[‘entries’])
为 100,作者找的多少个 奇骏SS 源唯有10-十八个左右。

>>> import feedparser
>>>ny=feedparser.parse(”)
>>> ny[‘entries’]
>>> len(ny[‘entries’])
100

因为笔者的三个LX570SS源有十0篇小说,所以她能够在代码中剔除了二1几个“停用词”,随机接纳20篇小说作为测试集,不过当大家使用替代卡宴SS源时大家只有十篇小说却要取出20篇作品作为测试集,那样显明是会出错的。只要本身调整下测试集的数目就足以让代码跑通;假诺小说中的词太少,缩小剔除的“停用词”数量可以增加算法的准确度。

 

下边是词频总括排序,词长排序的代码。

跟着书中代码往下写在那边卡住了,可能还会有别的同学也碰到了这么的问…

倘诺不想将应运而生频率排序最高的二二十个单词移除,该怎么去掉“停用词”?

能够把要去掉的停用词存放到txt文件中,使用时读取(替代移除高频词的代码)。具体供给停用哪些词能够参照那里

以下代码想健康运转要求将停用词保存至stopword.txt中。

自作者的txt中保存了以下单词,效果还不易:

a
about
above
after
again
against
all
am
an
and
any
are
aren’t
as
at
be
because
been
before
being
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both
but
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can’t
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do
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doing
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down
during
each
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for
from
further
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have
haven’t
having
he
he’d
he’ll
he’s
her
here
here’s
hers
herself
him
himself
his
how
how’s
i
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i’m
i’ve
if
in
into
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it
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its
itself
let’s
me
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mustn’t
my
myself
no
nor
not
of
off
on
once
only
or
other
ought
our
ours
ourselves
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own
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should
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so
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the
their
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them
themselves
then
there
there’s
these
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they’ve
this
those
through
to
too
under
until
up
very
was
wasn’t
we
we’d
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we’re
we’ve
were
weren’t
what
what’s
when
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where
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which
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whom
why
why’s
with
won’t
would
wouldn’t
you
you’d
you’ll
you’re
you’ve
your
yours
yourself
yourselves

 

'''
Created on Oct 19, 2010

@author: Peter
'''
from numpy import *

def loadDataSet():
    postingList=[['my', 'dog', 'has', 'flea', 'problems', 'help','my','dog', '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 bagOfWords2Vec(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():
    print '*** load dataset for training ***'
    listOPosts,listClasses = loadDataSet()
    print 'listOPost:\n',listOPosts
    print 'listClasses:\n',listClasses
    print '\n*** create Vocab List ***'
    myVocabList = createVocabList(listOPosts)
    print 'myVocabList:\n',myVocabList
    print '\n*** Vocab show in post Vector Matrix ***'
    trainMat=[]
    for postinDoc in listOPosts:
        trainMat.append(bagOfWords2Vec(myVocabList, postinDoc))
    print 'train matrix:',trainMat
    print '\n*** train P0V p1V pAb ***'
    p0V,p1V,pAb = trainNB0(array(trainMat),array(listClasses))
    print 'p0V:\n',p0V
    print 'p1V:\n',p1V
    print 'pAb:\n',pAb
    print '\n*** classify ***'
    testEntry = ['love', 'my', 'dalmation']
    thisDoc = array(bagOfWords2Vec(myVocabList, testEntry))
    print testEntry,'classified as: ',classifyNB(thisDoc,p0V,p1V,pAb)
    testEntry = ['stupid', 'garbage']
    thisDoc = array(bagOfWords2Vec(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 stopWords():
    import re
    wordList =  open('stopword.txt').read() # see http://www.ranks.nl/stopwords
    listOfTokens = re.split(r'\W*', wordList)
    return [tok.lower() for tok in listOfTokens] 
    print 'read stop word from \'stopword.txt\':',listOfTokens
    return listOfTokens

def localWords(feed1,feed0):
    import feedparser
    docList=[]; classList = []; fullText =[]
    print 'feed1 entries length: ', len(feed1['entries']), '\nfeed0 entries length: ', len(feed0['entries'])
    minLen = min(len(feed1['entries']),len(feed0['entries']))
    print '\nmin Length: ', minLen
    for i in range(minLen):
        wordList = textParse(feed1['entries'][i]['summary'])
        print '\nfeed1\'s entries[',i,']\'s summary - ','parse text:\n',wordList
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(1) #NY is class 1
        wordList = textParse(feed0['entries'][i]['summary'])
        print '\nfeed0\'s entries[',i,']\'s summary - ','parse text:\n',wordList
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(0)
    vocabList = createVocabList(docList)#create vocabulary
    print '\nVocabList is ',vocabList
    print '\nRemove Stop Word:'
    stopWordList = stopWords()
    for stopWord in stopWordList:
        if stopWord in vocabList:
            vocabList.remove(stopWord)
            print 'Removed: ',stopWord
##    top30Words = calcMostFreq(vocabList,fullText)   #remove top 30 words
##    print '\nTop 30 words: ', top30Words
##    for pairW in top30Words:
##        if pairW[0] in vocabList:
##            vocabList.remove(pairW[0])
##            print '\nRemoved: ',pairW[0]
    trainingSet = range(2*minLen); testSet=[]           #create test set
    print '\n\nBegin to create a test set: \ntrainingSet:',trainingSet,'\ntestSet',testSet
    for i in range(5):
        randIndex = int(random.uniform(0,len(trainingSet)))
        testSet.append(trainingSet[randIndex])
        del(trainingSet[randIndex])
    print 'random select 5 sets as the testSet:\ntrainingSet:',trainingSet,'\ntestSet',testSet
    trainMat=[]; trainClasses = []
    for docIndex in trainingSet:#train the classifier (get probs) trainNB0
        trainMat.append(bagOfWords2VecMN(vocabList, docList[docIndex]))
        trainClasses.append(classList[docIndex])
    print '\ntrainMat length:',len(trainMat)
    print '\ntrainClasses',trainClasses
    print '\n\ntrainNB0:'
    p0V,p1V,pSpam = trainNB0(array(trainMat),array(trainClasses))
    #print '\np0V:',p0V,'\np1V',p1V,'\npSpam',pSpam
    errorCount = 0
    for docIndex in testSet:        #classify the remaining items
        wordVector = bagOfWords2VecMN(vocabList, docList[docIndex])
        classifiedClass = classifyNB(array(wordVector),p0V,p1V,pSpam)
        originalClass = classList[docIndex]
        result =  classifiedClass != originalClass
        if result:
            errorCount += 1
        print '\n',docList[docIndex],'\nis classified as: ',classifiedClass,', while the original class is: ',originalClass,'. --',not result
    print '\nthe error rate is: ',float(errorCount)/len(testSet)
    return vocabList,p0V,p1V

def testRSS():
    import feedparser
    ny=feedparser.parse('http://www.nasa.gov/rss/dyn/image_of_the_day.rss')
    sf=feedparser.parse('http://sports.yahoo.com/nba/teams/hou/rss.xml')
    vocabList,pSF,pNY = localWords(ny,sf)

def testTopWords():
    import feedparser
    ny=feedparser.parse('http://www.nasa.gov/rss/dyn/image_of_the_day.rss')
    sf=feedparser.parse('http://sports.yahoo.com/nba/teams/hou/rss.xml')
    getTopWords(ny,sf)

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]

def test42():
    print '\n*** Load DataSet ***'
    listOPosts,listClasses = loadDataSet()
    print 'List of posts:\n', listOPosts
    print 'List of Classes:\n', listClasses

    print '\n*** Create Vocab List ***'
    myVocabList = createVocabList(listOPosts)
    print 'Vocab List from posts:\n', myVocabList

    print '\n*** Vocab show in post Vector Matrix ***'
    trainMat=[]
    for postinDoc in listOPosts:
        trainMat.append(bagOfWords2Vec(myVocabList,postinDoc))
    print 'Train Matrix:\n', trainMat

    print '\n*** Train ***'
    p0V,p1V,pAb = trainNB0(trainMat,listClasses)
    print 'p0V:\n',p0V
    print 'p1V:\n',p1V
    print 'pAb:\n',pAb
##统计词频
freqD2 = {}
for word2 in final:
  freqD2[word2] = freqD2.get(word2, 0) + 1 

##按词频排序输出
counter_list = sorted(freqD2.items(), key=lambda x: x[1], reverse=True) 
_2000=counter_list[0][1] + 1
print(_2000)##用于词长词频排序用
fp = open('sort.txt',"w+",encoding='utf-8')
for d in counter_list:
  fp.write(d[0]+':'+str(d[1]))
  fp.write('\n')
fp.close()

##按词长词频排序输出
counter_list = sorted(freqD2.items(), key=lambda x: len(x[0])*_2000+x[1], reverse=True) 
fp = open('sortlen.txt',"w+",encoding='utf-8')
for d in counter_list:
  fp.write(d[0]+':'+str(d[1]))
  fp.write('\n')
fp.close()

排序代码很有益,也值得借鉴,Python是个好东西,强大,易重用。

 

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