def main(args):
argp = ARGPARSER.parse_args(args[1:])
if not argp.no_cache:
# We can't do it iteratively listening to stdin, read it all
doc = Document('<classify>', [], [], '<classify>')
for _string in (l.rstrip('\n') for l in argp.input):
doc.abstract.append(_string_to_ann_sent(_string))
docs = (doc, )
else:
docs = (Document('Line: %s' % i, [], [_string_to_ann_sent(_string)],
'<stdin>') for i, _string in enumerate(
(l.rstrip('\n') for l in argp.input), start=1))
# Cache the strings for speed
if not argp.no_cache:
cache_simstring((docs, ), verbose=argp.verbose)
with open(argp.model_path, 'r') as model_file:
classifier = pickle_load(model_file)
# TODO: Faster to do it in a batch instead
for doc in docs:
for sent in doc:
for ann in sent:
print '%s\t%s' % (sent.annotation_text(ann),
str(classifier.classify(doc, sent, ann, ranked=True)))
def main(args):
argp = ARGPARSER.parse_args(args[1:])
# Create a dataset out of the input
doc = _tab_separated_input_to_doc(argp.input)
# Cache the strings for speed
cache_simstring(((doc, ), ), verbose=argp.verbose)
classifier = SimStringInternalClassifier()
classifier.train((doc, ))
with open(argp.model_path, 'w') as model_file:
pickle_dump(classifier, model_file)
def main(args):
argp = ARGPARSER.parse_args(args[1:])
# Create a dataset out of the input
doc = _tab_separated_input_to_doc(argp.input)
# Cache the strings for speed
cache_simstring(((doc, ), ), verbose=argp.verbose)
classifier = SimStringInternalClassifier()
classifier.train((doc, ))
with open(argp.model_path, 'w') as model_file:
pickle_dump(classifier, model_file)
