def _greedy_decode(self):
"""Performs greedy decoding from the start node. Used to obtain
the initial hypothesis.
"""
hypo = PartialHypothesis()
hypos = []
posteriors = []
score_breakdowns = []
scores = []
bag = dict(self.full_bag)
while bag:
posterior,score_breakdown = self.apply_predictors()
hypo.predictor_states = copy.deepcopy(self.get_predictor_states())
hypos.append(hypo)
posteriors.append(posterior)
score_breakdowns.append(score_breakdown)
best_word = utils.argmax({w: posterior[w] for w in bag})
bag[best_word] -= 1
if bag[best_word] < 1:
del bag[best_word]
self.consume(best_word)
hypo = hypo.expand(best_word,
None,
posterior[best_word],
score_breakdown[best_word])
scores.append(posterior[best_word])
posterior,score_breakdown = self.apply_predictors()
hypo.predictor_states = self.get_predictor_states()
hypos.append(hypo)
posteriors.append(posterior)
score_breakdowns.append(score_breakdown)
hypo = hypo.expand(utils.EOS_ID,
None,
posterior[utils.EOS_ID],
score_breakdown[utils.EOS_ID])
logging.debug("Greedy hypo (%f): %s" % (
hypo.score,
' '.join([str(w) for w in hypo.trgt_sentence])))
scores.append(posterior[utils.EOS_ID])
self.best_score = hypo.score
self.add_full_hypo(hypo.generate_full_hypothesis())
self._process_new_hypos(FlipCandidate(hypo.trgt_sentence,
scores,
self._create_dummy_bigrams(),
hypo.score),
len(hypo.trgt_sentence),
hypos,
posteriors,
score_breakdowns)
def create_initial_node(self):
"""Create the root node for the search tree. """
init_hypo = PartialHypothesis()
posterior,score_breakdown = self.apply_predictors()
best_word = utils.argmax(posterior)
init_hypo.predictor_states = self.get_predictor_states()
init_node = BOWNode(init_hypo, posterior, score_breakdown, [])
self._add_to_heap(init_node, best_word, 0.0) # Expected score irrelevant
def create_initial_node(self):
"""Create the root node for the search tree. """
init_hypo = PartialHypothesis()
posterior,score_breakdown = self.apply_predictors()
children = sorted([RestartingChild(w, posterior[w], score_breakdown[w])
for w in posterior],
key=lambda c: c.score, reverse=True)
init_hypo.predictor_states = self.get_predictor_states()
heappush(self.open_nodes, (0.0, RestartingNode(init_hypo, children)))
def create_initial_node(self):
"""Create the root node for the search tree. """
init_hypo = PartialHypothesis()
posterior, score_breakdown = self.apply_predictors()
best_word = utils.argmax(posterior)
init_hypo.predictor_states = self.get_predictor_states()
init_node = BOWNode(init_hypo, posterior, score_breakdown, [])
self._add_to_heap(init_node, best_word,
0.0) # Expected score irrelevant
def create_initial_node(self):
"""Create the root node for the search tree. """
init_hypo = PartialHypothesis()
posterior,score_breakdown = self.apply_predictors()
children = sorted([RestartingChild(w, posterior[w], score_breakdown[w])
for w in posterior],
key=lambda c: c.score, reverse=True)
init_hypo.predictor_states = self.get_predictor_states()
heappush(self.open_nodes, (0.0, RestartingNode(init_hypo, children)))
def _greedy_decode(self):
"""Performs greedy decoding from the start node. Used to obtain
initial bigram statistics.
"""
hypo = PartialHypothesis()
hypos = []
posteriors = []
score_breakdowns = []
bag = dict(self.full_bag)
while bag:
posterior,score_breakdown = self.apply_predictors()
hypo.predictor_states = copy.deepcopy(self.get_predictor_states())
bag_posterior = {w: posterior[w] for w in self.full_bag_with_eos}
bag_breakdown = {w: score_breakdown[w]
for w in self.full_bag_with_eos}
posteriors.append(bag_posterior)
score_breakdowns.append(bag_breakdown)
hypos.append(hypo)
best_word = utils.argmax({w: bag_posterior[w] for w in bag})
bag[best_word] -= 1
if bag[best_word] < 1:
del bag[best_word]
self.consume(best_word)
hypo = hypo.expand(best_word,
None,
bag_posterior[best_word],
score_breakdown[best_word])
posterior,score_breakdown = self.apply_predictors()
hypo.predictor_states = copy.deepcopy(self.get_predictor_states())
bag_posterior = {w: posterior[w] for w in self.full_bag_with_eos}
bag_breakdown = {w: score_breakdown[w] for w in self.full_bag_with_eos}
posteriors.append(bag_posterior)
score_breakdowns.append(bag_breakdown)
hypos.append(hypo)
hypo = hypo.cheap_expand(utils.EOS_ID,
bag_posterior[utils.EOS_ID],
score_breakdown[utils.EOS_ID])
logging.debug("Greedy hypo (%f): %s" % (
hypo.score,
' '.join([str(w) for w in hypo.trgt_sentence])))
self._process_new_hypos(hypos, posteriors, score_breakdowns, hypo)
def _greedy_decode(self):
"""Performs greedy decoding from the start node. Used to obtain
initial bigram statistics.
"""
hypo = PartialHypothesis()
hypos = []
posteriors = []
score_breakdowns = []
bag = dict(self.full_bag)
while bag:
posterior,score_breakdown = self.apply_predictors()
hypo.predictor_states = copy.deepcopy(self.get_predictor_states())
bag_posterior = {w: posterior[w] for w in self.full_bag_with_eos}
bag_breakdown = {w: score_breakdown[w]
for w in self.full_bag_with_eos}
posteriors.append(bag_posterior)
score_breakdowns.append(bag_breakdown)
hypos.append(hypo)
best_word = utils.argmax({w: bag_posterior[w] for w in bag})
bag[best_word] -= 1
if bag[best_word] < 1:
del bag[best_word]
self.consume(best_word)
hypo = hypo.expand(best_word,
None,
bag_posterior[best_word],
score_breakdown[best_word])
posterior,score_breakdown = self.apply_predictors()
hypo.predictor_states = copy.deepcopy(self.get_predictor_states())
bag_posterior = {w: posterior[w] for w in self.full_bag_with_eos}
bag_breakdown = {w: score_breakdown[w] for w in self.full_bag_with_eos}
posteriors.append(bag_posterior)
score_breakdowns.append(bag_breakdown)
hypos.append(hypo)
hypo = hypo.cheap_expand(utils.EOS_ID,
bag_posterior[utils.EOS_ID],
score_breakdown[utils.EOS_ID])
logging.debug("Greedy hypo (%f): %s" % (
hypo.score,
' '.join([str(w) for w in hypo.trgt_sentence])))
self._process_new_hypos(hypos, posteriors, score_breakdowns, hypo)
def _initialize_decoding(self, src_sentence):
"""Helper function for ``decode`` to which initializes all the
class attributes
"""
self.initialize_predictors(src_sentence)
self.max_expansions = self.get_max_expansions(
self.max_expansions_param, src_sentence)
init_hypo = PartialHypothesis()
init_hypo.predictor_states = self.get_predictor_states()
init_hypo.scores = []
init_hypo.parent_hypo_array_idx = 0 # point to guardian
self.buckets = [[] for _ in xrange(self.max_len + 1)]
self.expanded_hypos = [[] for _ in xrange(self.max_len + 1)]
self.buckets[0].append((0.0, init_hypo))
self.expand_counts = [0.0] # with guardian
self.expand_backpointers = [0] # with guardian
self.last_bucket = 0
self.best_score = self.get_lower_score_bound()
self.best_word_scores = [NEG_INF] * (self.max_len + 1)
self.compressed = [True] * (self.max_len + 1)
self.guaranteed_optimality = True
self.cur_iter = 0
def _initialize_decoding(self, src_sentence):
"""Helper function for ``decode`` to which initializes all the
class attributes
"""
self.initialize_predictors(src_sentence)
self.max_expansions = self.get_max_expansions(self.max_expansions_param,
src_sentence)
init_hypo = PartialHypothesis()
init_hypo.predictor_states = self.get_predictor_states()
init_hypo.scores = []
init_hypo.parent_hypo_array_idx = 0 # point to guardian
self.buckets = [[] for _ in xrange(self.max_len+1)]
self.expanded_hypos = [[] for _ in xrange(self.max_len+1)]
self.buckets[0].append((0.0, init_hypo))
self.expand_counts = [0.0] # with guardian
self.expand_backpointers = [0] # with guardian
self.last_bucket = 0
self.best_score = self.get_lower_score_bound()
self.best_word_scores = [NEG_INF] * (self.max_len+1)
self.compressed = [True] * (self.max_len+1)
self.guaranteed_optimality = True
self.cur_iter = 0