def train(self, sampler=None):
if sampler:
X = []
y = []
urls = sampler.get_urls()
for url in urls:
# calculate features, prepare for training/classification
candidates = self.get_candidates(url)
# transform dataset
features = []
authors = []
is_author_found = False
for candidate in candidates:
features.append(candidate.get_features())
# determine whether this is a true author or not
is_target = sampler.is_author(url, candidate.el, candidate.text)
is_author_found = is_author_found or is_target
authors.append(1 if is_target else 0)
if is_target:
print 'Candidate found: %s' % candidate.text
try:
print candidate
except UnicodeEncodeError:
pass
print
if is_author_found:
X.extend(features)
y.extend(authors)
data = (array(X), array(y))
self.save(data)
else:
data = self.restore()
start = etime()
self.classifier.fit(*data)
end = etime()
print 'Train classifier %s' % str(end - start)
def get_candidates(self, url):
print
print 'Working with: %s' % url
start = etime()
html = read_web(url)
candidates = self.feature_extractor.get_candidates(html)
end = etime()
print 'Gather candidates time: %s' % str(end - start)
start = etime()
for candidate in candidates:
candidate.calculate_features()
end = etime()
print 'Calculate features time: %s' % str(end - start)
return candidates