def build_model(cache=True):
if cache:
f = "%s/word2vec.model" % cache_dir()
if os.path.isfile(f):
return Word2Vec.load(f)
texts = []
for url in crawl_report_list():
html = get(url)
enc, time, title, text = ce.parse(url, html)
sentences = text_util.get_sentences(text)
for s in sentences:
texts.append([w for w in jieba.cut(s)])
b = Word2Vec(texts)
if cache:
b.save(f)
return b
def tf(cache=True, force=False):
f = "%s/tf.txt" % cache_dir()
if cache and not force:
if os.path.isfile(f):
return True
d = defaultdict(int)
for url in (crawl_report_list() + crawl_plan_list()):
html = get(url)
enc, time, title, text = ce.parse(url, html)
sentences = text_util.get_sentences(text)
for s in sentences:
for w in jieba.cut(s):
d[w] += 1
r = sorted(d.items(), key=lambda x:x[1], reverse=True)
if cache:
out = open(f, "w")
for k,v in r:
out.write(("%s\t%s\n" % (k,v)).encode('utf-8', 'ignore'))
out.close()
return True
#encoding: utf-8
import sys
import content_extract as ce
sys.path.append("../../lib")
import download
if __name__ == "__main__":
html = download.getPage(sys.argv[1])
enc, time, title, text = ce.parse(sys.argv[1], html)
print "标题：" + title.encode('utf-8', 'ignore')
print "时间：" + time.encode('utf-8', 'ignore')
print '=' * 10
print "内容：" + text.encode('utf-8', 'ignore')
