def _get_wv(sentence):
'''
get word2vec data by sentence
sentence is segmented string.
'''
global _vectors
vectors = []
for y in sentence.split():
y_ = any2unicode(y).strip()
if y_ not in _stopwords:
syns = nearby(y_)[0]
# print("sentence %s word: %s" %(sentence, y_))
# print("sentence %s word nearby: %s" %(sentence, " ".join(syns)))
c = []
try:
c.append(_vectors.word_vec(y_))
except KeyError as error:
print("not exist in w2v model: %s" % y_)
c.append(np.zeros((100, ), dtype=float))
for n in syns:
if n is None: continue
try:
v = _vectors.word_vec(any2unicode(n))
except KeyError as error:
v = np.zeros((100, ), dtype=float)
c.append(v)
r = np.average(c, axis=0)
vectors.append(r)
return vectors
def add_word_to_vocab(word, nearby, nearby_score):
'''
Add word into vocab by word, nearby lis and nearby_score lis
'''
global _size
if word is not None:
if PLT == 2:
word = any2unicode(word)
nearby = [any2unicode(z) for z in nearby]
_vocab[word] = [nearby, nearby_score]
_size += 1
def nearby(word):
'''
Nearby word
'''
try:
return _vocab[any2unicode(word)]
except KeyError as e:
return [[], []]
def _load_stopwords(file_path):
'''
load stop words
'''
global _stopwords
words = open(file_path, 'r')
stopwords = words.readlines()
for w in stopwords:
_stopwords.add(any2unicode(w).strip())
def _load_stopwords(file_path):
'''
load stop words
'''
global _stopwords
if sys.version_info[0] < 3:
words = open(file_path, 'r')
else:
words = open(file_path, 'r', encoding='utf-8')
stopwords = words.readlines()
for w in stopwords:
_stopwords.add(any2unicode(w).strip())
def crawl_url(browser, url , savedir, debug=False, delay=0):
html = url2filenames(url)
browser.LoadUrl(escape_url(url), synchronous=True)
logger.info('Fetched '+url)
if delay:
time.sleep(delay)
# save raw page
htmlfile = os.path.join(savedir, html)
with open(htmlfile, 'wb') as srcfp:
html = browser.getSource(True) # synchronous get
assert(html)
srcfp.write(html)
logger.debug('Wrote to %s' % htmlfile)
# parse page for features, get attribute table
logger.info('Extracting features')
header, attributes, dom, bodyhtml = collect_features(browser)
logger.debug('%d elements with features extracted' % len(attributes))
# write as CSV
csvfile = htmlfile + '.csv'
with open(csvfile, 'wb') as csvfp:
csvfp.write(codecs.BOM_UTF8) # Excel requires BOM
csvout = UnicodeWriter(csvfp)
csvout.writerow(header)
csvout.writerows([[any2unicode(x) for x in row] for row in attributes])
logger.info('Wrote to %s' % csvfile)
if debug:
lxmlfile = htmlfile+'.lxml'
domfile = htmlfile+'.raw.csv'
with open(domfile, 'wb') as csvfp: # write DOM csv as recognized by JS
csvfp.write(codecs.BOM_UTF8) # Excel requires BOM
csvout = UnicodeWriter(csvfp)
csvout.writerow(
"xpath display visible x y width height fgcolor bgcolor fontsize "
"textonly htmlcode".split()
)
csvout.writerows([[any2unicode(x) for x in row] for row in dom])
logger.info('Wrote to %s' % domfile)
with open(lxmlfile, 'wb') as fp:
fp.write(bodyhtml.encode('utf8'))
logger.info('Wrote to %s' % lxmlfile)