class Labeler:
def __init__(self, labeler_model_file = None):
if labeler_model_file:
self.model = LabelerModel(labeler_model_file)
def __get_instances(self, model, conll_file, map_func):
instances = []
for sent in read_sentence(open(conll_file)):
unigrams = make_unigram_features(sent)
for d in xrange(1, len(sent)):
label = model.register_label(sent[d].label)
vector = make_features_for_labeler(sent, unigrams,sent[d].head, d, model.register_feature)
instances.append((label, vector))
model.make_weights()
return instances
def train(self, conll_file, model_file, epochs = 10):
model = LabelerModel()
instances = self.__get_instances(model, conll_file, model.register_feature)
print 'start training ...'
q = 0
for epoch in xrange(epochs):
correct = 0
total = 0
# random.shuffle(instances)
for (gold, vector) in iter(instances):
q += 1
pred = model.predict(vector)
if gold != pred:
model.update(vector, gold, pred, q)
else:
correct += 1
total += 1
print '\nepoch %d done, %6.2f%% correct' % (epoch,100.0*correct/total)
model.average(q)
model.save(model_file)
def predict(self, sent):
unigrams = make_unigram_features(sent)
for d in xrange(1, len(sent)):
h = sent[d].head
vector = make_features_for_labeler(sent, unigrams, h, d, self.model.map_feature)
pred = self.model.predict(vector)
sent[d].label = self.model.mapback_label(pred)
return sent
def train(self, conll_file, model_file, epochs = 10):
model = LabelerModel()
instances = self.__get_instances(model, conll_file, model.register_feature)
print 'start training ...'
q = 0
for epoch in xrange(epochs):
correct = 0
total = 0
# random.shuffle(instances)
for (gold, vector) in iter(instances):
q += 1
pred = model.predict(vector)
if gold != pred:
model.update(vector, gold, pred, q)
else:
correct += 1
total += 1
print '\nepoch %d done, %6.2f%% correct' % (epoch,100.0*correct/total)
model.average(q)
model.save(model_file)