def generateModelsForPeriod(years, minSentences, maxSentences, nSteps, chunkSize,
randomSeed, vector_size, modelFolder, fixedAlpha=False):
'''Generates sets of w2v models trained using randomly selected sentences
from the given year period. Models are trained with an increasing number of
sentences, starting with minSentences and going up to maxSentences (in nSteps,
so nSteps models are generated).
randomSeed is used to initialize the random number generator.
vector_size defines the number of dimensions used by the w2v models.
modelFolder indicates the directory where the models will be saved.
fixedAlpha can be set to True to use a fixed learning rate for w2v.
Files generated in modelFolder:
- nSentences.pkl -- list containing the number of sentences to be used in each batch.
- year_nSentences.w2v -- the models (e.g. 1990_000100000000.w2v) (and corresponding vocab)
'''
# Build chunks to be used (if needed)
checkPath(modelFolder)
for year in years:
buildChunks(year, chunkSize)
# Initialize loader
loader = RandomSentenceLoader(years, chunkSize=chunkSize, seed=randomSeed)
# The number of sentences to be used in each batch.
batchSizes = np.logspace(np.log10(minSentences), np.log10(
maxSentences), num=nSteps, dtype=int)
pkl.dump(batchSizes, open(modelFolder + '/nSentences.pkl', 'w'))
for batchSize in batchSizes:
cumSentencesStr = ('%d' % batchSize).zfill(12)
modelKey = '%d_' % years[0] + cumSentencesStr
modelName = modelFolder + '/%s.w2v' % (modelKey)
vocabName = modelName.replace('.w2v', '.vocab.w2v')
print 'Building model: ', modelName
# Initialize w2v model
vocabSizeMB = 1000 * 1024 * 1024
if fixedAlpha:
print '...using fixed alpha...'
model = gensim.models.Word2Vec(
max_vocab_size=vocabSizeMB, seed=randomSeed, size=vector_size, alpha=0.75, min_alpha=0.75)
else:
model = gensim.models.Word2Vec(
max_vocab_size=vocabSizeMB, seed=randomSeed, size=vector_size)
# build vocabulary, then train the model with same sentences.
# We use a generator to avoid loading all sentences at once, but that
# means in order to use the same set of sentences, we reset loader.
loader.reset()
batch = loader.nextBatchGenerator(batchSize=batchSize)
model.build_vocab(batch)
loader.reset()
batch = loader.nextBatchGenerator(batchSize=batchSize)
model.train(batch)
print '...saving'
model.wv.save_word2vec_format(modelName, fvocab=vocabName, binary=True)
def _getDocumentsForYear(year):
"""Retrieves the content of all documents for a year specified. Also caches
the document for later use (if required). This is meant to speed document
processing time, as document preparation requires significant time."""
cachedFile = _getCachedName(year)
checkPath(cacheDir)
print 'Loading cached file: ' + cachedFile
if os.path.exists(cachedFile):
with gzip.open(cachedFile, 'rb') as f:
documentsForYear = pkl.load(f)
else:
docs = _listDocsForYear(year)
documentsForYear = []
for doc in docs:
articles = _getArticlesInDoc(doc)
sentences = [
_getSentencesInArticle(article) for article in articles
]
sentences = [_prepareSentences(sentence) for sentence in sentences]
documentsForYear += sentences
print 'Saving to cache %s...' % cachedFile
with gzip.open(cachedFile, 'wb') as f:
pkl.dump(documentsForYear, f)
return documentsForYear
def generate(size, dir, path, outputName, params):
currentTime = datetime.datetime.now()
if len(outputName) <= 0:
batchName = currentTime.strftime("%b-%d-%Y-%H%M%p%s")
else:
batchName = util.checkPath(dir, outputName)
currentDirectory = os.getcwd()
childDirectory = dir + "/" + batchName
childDirectory = os.mkdir(childDirectory)
record = [['email', 'gender', 'school', 'date']]
ID = 1
progressBarStep = int(size / 10)
progress = QProgressDialog("Generating Resumes", "Close", 0, 100)
progress.setWindowModality(QtCore.Qt.WindowModal)
progress.setAutoClose(True)
progress.show()
while ID <= size:
pair = getPair(path, params)
record.append(makeDoc(pair, 'control', ID, batchName, params))
record.append(makeDoc(pair, 'test', ID, batchName, params))
ID += 1
progress.setValue(ID)
message = QMessageBox()
message.setText("Resume generation complete.")
message.exec()
del pair
return record
def measureConvergence(y0, nYears, saveDir):
"""
Call computeConvergenceOverYearRange for sentences in the given year range
and save convergence results to the given directory.
"""
batchSize = 1e6
maxSentences = 10e7
yN = y0 + nYears
allYears = getYears()
yearRange = allYears[(y0 <= allYears) & (allYears <= yN)]
convergence, sentenceYearCounter, vocabSize = computeConvergenceOverYearRange(
yearRange, batchSize, maxSentences)
checkPath(saveDir)
fname = saveDir + 'convergenceRange_%d-%d.pkl' % (y0, yN)
pkl.dump((convergence, sentenceYearCounter, vocabSize), open(fname, 'wb'))
def getMoods(self, qqnumber):
#下载动态文件并保存到/mood_result/qqnumber文件夹下
referer = 'http://user.qzone.qq.com/' + qqnumber
self.headers['Referer'] = referer
# 创建qqnumber夹用于保存文件
util.checkPath('mood_result/' + qqnumber)
# 获取目标url
urlBase = util.parseMoodsUrl(qqnumber)
pos = 0
key = True
while key:
print("\tDealing with position:\t%d" % pos)
url = urlBase + "&pos=%d" % pos
# print(url) 用于调试
res = self.session.get(url, headers = self.headers)
con = res.text
with open('mood_result/' + qqnumber + '/' + str(pos), 'w', encoding="utf-8") as f:
f.write(con)
if '''"msglist":null''' in con:
key = False
# 禁止访问
if '''"对不起,主人设置了保密,您没有权限查看"''' in con:
with open('naida_log.log', 'a', encoding="utf-8") as logFile:
logFile.write("%s No access..\n" % qqnumber)
self.access[qqnumber] = 'False'
key = False
else:
self.access[qqnumber] = 'True'
# Cookie 过期
if '''"subcode":-4001''' in con:
with open('naida_log.log', 'a', encoding="utf-8") as log_file:
log_file.write('Cookie expired! Time is %s\n' % time.ctime())
sys.exit()
pos += 20
time.sleep(5)
with open('Access.json', 'w', encoding = 'utf-8') as fp:
fp.write((json.dumps(self.access, ensure_ascii = False, indent = 4)))
def getMoodsStart(self):
app = Moods()
with open('qqnumber.inc', encoding="utf-8") as qnumberFile:
qnumberString = qnumberFile.read()
qnumberList = eval(qnumberString)
# 检查文件夹是否存在
# 不存在则创建
util.checkPath('mood_result')
self.sem = threading.Semaphore(10)
while qnumberList != []:
saveBackNumber = qnumberList[:]
item = qnumberList.pop()
self.sem.acquire()
t = threading.Thread(target=self.getMooding, args=(item, app, saveBackNumber))
t.start()
time.sleep(1)
else:
print("Finish All!")
def generateModels(y0, yN, yearsInModel, stepYears, modelFolder):
"""Generate time shifting w2v models on the given time range (y0 - yN).
Each model contains the specified number of years (yearsInModel). The start
year of each new model is set to be stepYears after the previous model.
Resulting models are saved on modelFolder.
"""
checkPath(modelFolder)
for year in range(y0, yN - yearsInModel + 1, stepYears):
startY = year
endY = year + yearsInModel
modelName = modelFolder + '/%d_%d.w2v' % (year, year + yearsInModel)
vocabName = modelName.replace('.w2v', '.vocab.w2v')
print 'Building model: ', modelName
sentences = getSentencesInRange(startY, endY)
model = gensim.models.Word2Vec(min_count=1)
model.build_vocab(sentences)
model.train(sentences)
print '...saving'
model.init_sims(replace=True)
model.wv.save_word2vec_format(modelName, fvocab=vocabName, binary=True)
def __init__(self):
self.headers = util.headers
self.baseUrl = util.parseFriendsUrl()
util.checkPath('friends')
print('Start get friends file')
def getAccess(self):
util.checkPath('Access.json')
with open('Access.json', 'r', encoding='utf-8') as f:
fstring = f.read()
fjson = eval(fstring)
return fjson
