def getHeapsData(filenames):
outfile = open('vocabWordCount.csv', 'w')
outfile.write('Vocab,WordCount\n')
totalVocab = set()
wordCount = 0
for i in range(len(filenames)):
f = filenames[i].strip()
html = readTextFromFile(f)
text = getTextFromHTML(html)
if (len(text) == 0):
continue
countVectorizer = CountVectorizer(min_df=1,
stop_words='english',
ngram_range=(1, 2))
termFreqMat = countVectorizer.fit_transform([text])
totalVocab = totalVocab.union(set(countVectorizer.vocabulary_.keys()))
wordCount += termFreqMat.todense().sum()
outfile.write(str(len(totalVocab)) + ', ' + str(wordCount) + '\n')
if (i % 100 == 0):
print(i, 'of', len(filenames))
outfile.close()
def getVocabFreqDict(filenames, stop, ngramTup=(1, 1)):
vocabDict = {}
for i in range(len(filenames)):
f = filenames[i].strip()
html = readTextFromFile(f)
text = getTextFromHTML(html)
#writeTextToFile(f + '.txt', text)
if (len(text) == 0):
continue
countVectorizer = CountVectorizer(min_df=1,
stop_words='english',
ngram_range=ngramTup)
termFreqMat = countVectorizer.fit_transform([text])
for term in list(countVectorizer.vocabulary_.keys()):
vocabDict.setdefault(term, {'f': []})
vocabDict[term]['f'].append(f)
if (i % 100 == 0):
print(i, 'of', len(filenames))
if (i > stop):
break
return vocabDict
def getVocabFreqDict(filenames):
vocabDict = {}
for i in range(len(filenames)):
f = filenames[i].strip()
html = readTextFromFile(f)
text = getTextFromHTML(html)
if (len(text) == 0):
continue
countVectorizer = CountVectorizer(min_df=1,
stop_words='english',
ngram_range=(1, 2))
termFreqMat = countVectorizer.fit_transform([text])
for term in list(countVectorizer.vocabulary_.keys()):
vocabDict.setdefault(term, 0)
vocabDict[term] += 1
if (i % 100 == 0):
print(i, 'of', len(filenames))
dumpJsonToFile('1-2-gram.json', vocabDict)
def getAssociationForPair(vocabDict, pair, windowSize):
a, b = pair
Na = 0
Nb = 0
Nab = 0
if (vocabDict[a] and vocabDict[b]):
for f in vocabDict[a]['f']:
f = f + '.txt'
f = readTextFromFile(f)
counts = searchKwordWindowsOpt(f, windowSize, a, b)
Na += counts['left']
Nab += counts['both']
for f in vocabDict[b]['f']:
counts = searchKwordWindowsOpt(f, windowSize, b, a, True)
Nb += counts['left']
if (Nab != 0):
return Nab / (Na + Nb)
else:
return -1
def transformDocToWindow(vocabDict, vocab):
if (vocab not in vocabDict):
print('term:', vocab, 'not in vocab')
return
allWindows = []
for i in range(len(vocabDict[vocab]['f'])):
f = vocabDict[vocab]['f'][i] + '.txt'
f = readTextFromFile(f)
allWindows += getKwordWindows(f, 5)
vocabDict[vocab]['f'] = allWindows
def transformDocToWindowOpt(vocabDict, vocab):
if (vocab not in vocabDict):
print('term:', vocab, 'not in vocab')
return
allWindows = {'tot': 0, 'windows': []}
for i in range(len(vocabDict[vocab]['f'])):
f = vocabDict[vocab]['f'][i] + '.txt'
f = readTextFromFile(f)
allWindows['windows'] += getKwordWindowsOpt(f, 5)
allWindows['tot'] = len(allWindows['windows'])
windowsWithVocab = []
for win in allWindows['windows']:
if (vocab in win):
windowsWithVocab.append(win)
allWindows['windows'] = windowsWithVocab
vocabDict[vocab]['f'] = allWindows
def getTopKPages(pathnames, filenames):
if( len(pathnames) == 0 ):
return []
outlinksDict = {}
for i in range(len(pathnames)):
wiki = pathnames[i]
wiki = wiki.strip()
html = readTextFromFile(wiki)
if( i % 100 == 0 ):
print(i, 'of', len(pathnames), 'wiki file:', wiki)
print('\tlen:', len(outlinksDict))
sourcewiki = getHTMLFilename(wiki)
getWikiOutlinks(sourcewiki, html, outlinksDict)
#if( i == 3 ):
# break
dumpJsonToFile('./outlinksDict.json', outlinksDict)
from Porter import PorterStemmer from krovetzstemmer import Stemmer from common import readTextFromFile from common import getTextFromHTML krov = Stemmer() f = 'en/articles/d/o/r/Dorothy_Block_a8f8.html' text = getTextFromHTML(readTextFromFile(f)) print 'ori:\n', text, '\n' print 'porter:\n', PorterStemmer.useStemer(text), '\n' print 'krov:\n', krov.stem(text), '\n'
