def train_sent(self, sent):
# take sent from corpus and update weight vector
# according to this example
self.model.update_perceptron_counter()
# init state
sent = [ROOT] + sent
init_state = get_state(sent)
# build gold tree
gold_tree = get_tree(sent)
beam = Beam(self.model.beam_size)
top_valid_state = None
beam.add(init_state)
# loop n-1 step
step = 0
# above check
for i in xrange(len(sent) - 1):
step += 1
# extend current beam
beam, top_valid_state = self.extend_beam_for_train(beam, gold_tree)
try:
if not beam.has_element(top_valid_state.top()):
self.update_paramaters(beam.top(), -1)
self.update_paramaters(top_valid_state.top(), 1)
break
except Exception:
raise
if step == len(sent) - 1:
top_beam = beam.top()
predict_arcs = top_beam['arcs']
if not compare_arcs(predict_arcs, gold_tree):
self.update_paramaters(top_beam, -1)
self.update_paramaters(top_valid_state.top(), 1)
def train(self, sent):
# update paramaters with one sent
# ROOT token at begining of pending
sent = [ROOT] + sent
# oracle object to check valid action
oracle = Oracle(sent)
# gold_deps for full update
gold_deps = self._build_gold(sent)
# create start state
init_state = self._get_state(sent)
# create beam
beam = Beam(self.model.beam_size)
# add state to beam
beam.add(init_state)
# correct action with highest score at one step
valid_action = None
for step in range(len(sent) - 1):
beam, valid_action = self._extend_beam(beam, oracle)
# if beam not contain valid action in it, update
if not beam.has_element(valid_action):
beam_top = beam.top()
self.model.update(beam_top, valid_action)
break
else:
beam_top = beam.top()
beam_deps = beam_top['deps']
# if final deps is not like gold_deps, do full update
if not self._check_equal(gold_deps, beam_deps):
self.model.update(beam_top, valid_action)
