def backtrack_k(bins,paths):
partial_scores = []
translated_sentences = []
tags = heap_lm_k.FT.tags
for path in paths:
partial_score = bins[0].data[0].partial_score
translated_sentence = []
father_key = bins[0].data[0].getKey()
for i in xrange(len(path)):
bin_id = path[i][0]
state_id = path[i][1]
ft_id = path[i][2]
state = bins[bin_id].data[state_id]
father_tuple = state.fathers[ft_id]
assert( father_key == father_tuple[tags.FATHER_KEY].getKey())
e_pharse = father_tuple[tags.E_PHRASE]
ps = father_tuple[tags.PARTIAL_SCORE]
partial_score = array_plus(partial_score,ps)
translated_sentence.append(e_pharse)
father_key = state.getKey()
partial_scores.append(partial_score)
translated_sentences.append(translated_sentence)
return partial_scores, translated_sentences
def decode_k(f_sentence, phrase_table, seen_words, lm_model,
lm_weight, d_weight, d_limit, beam_size, num_feature, tempFilePath, debug=False, k_best = 1):
'''generate the k-best translations
return partial_scores, translated_sentences
partial_scores: [[feature_value]*number_features] * k_best
translated_sentences : [[e_phrase] * num_phrases ] * k_best
'''
start = datetime.now()
# using monotone to generate future cost and transition options
fcost, options, new_phrase_table = future_cost_and_options(
f_sentence, phrase_table, seen_words, num_feature)
new_phrase_table = lmize(lm_model, lm_weight, new_phrase_table)
if debug:
for key in new_phrase_table:
s = new_phrase_table[key][0]
print key, s
print sorted(options)
# beam search
bins = []
for i in xrange(len(f_sentence) + 1):
b = heap_lm_k.Heap(beam_size,k_best)
bins.append(b)
empty = heap_lm_k.State((), 0, '<s>', 0, 0,[0.0]*num_feature)
bins[0].add(empty)
for i in xrange(len(f_sentence)):
b = bins[i]
for k in xrange(b.size):
state = b.data[k]
for option in options:
cover = state.cover
j = state.j
d = option[0] - j
last_e = state.last_e
if (not cross(cover, option)) and (abs(d) <= d_limit):
# whether current f_pharse is the last part un-translated
is_end = False
new_cover = merge(cover, option)
if len(new_cover) == len(f_sentence):
is_end = True
# reorder the e_pharse based on last_e LM score
f_phrase = tuple(f_sentence[option[0]:option[1]])
items = new_phrase_table[f_phrase]
items = lm_order_list(
lm_model, items, lm_weight, last_e, is_end,limit = 10)
# add into the bins
for item in items:
t_score = item[0] # lm score has already incorporated
d_score = - abs(d) * d_weight # note!
score = d_score + t_score + state.s
partial_score = list(item[2])
partial_score.append( - abs(d) )
new_partial_score = array_plus(partial_score, state.partial_score)
e_phrase = item[1]
new_last_e = e_phrase[-1]
new_j = option[1]
new_h = get_future_cost(fcost, new_cover, len(f_sentence))
child_state = heap_lm_k.State(
new_cover, new_j, new_last_e, score, new_h, new_partial_score)
child_state.e_phrase = e_phrase
delta = (child_state.f, child_state.s - state.s, child_state.e_phrase,array_minus(child_state.partial_score, state.partial_score),state)
child_state.fathers = [delta,]
n_cover = len(new_cover)
bins[n_cover].add(child_state)
if debug:
for b in bins:
print b.data
paths = _mcr.get_k_best_paths(bins,k_best,tempFilePath)
partial_scores, translated_sentence = _mcr.backtrack_k(bins,paths)
end = datetime.now()
print end-start
return partial_scores, translated_sentence
def decode_k(f_sentence,
phrase_table,
seen_words,
lm_model,
lm_weight,
d_weight,
d_limit,
beam_size,
num_feature,
tempFilePath,
debug=False,
k_best=1):
'''generate the k-best translations
return partial_scores, translated_sentences
partial_scores: [[feature_value]*number_features] * k_best
translated_sentences : [[e_phrase] * num_phrases ] * k_best
'''
start = datetime.now()
# using monotone to generate future cost and transition options
fcost, options, new_phrase_table = future_cost_and_options(
f_sentence, phrase_table, seen_words, num_feature)
new_phrase_table = lmize(lm_model, lm_weight, new_phrase_table)
if debug:
for key in new_phrase_table:
s = new_phrase_table[key][0]
print key, s
print sorted(options)
# beam search
bins = []
for i in xrange(len(f_sentence) + 1):
b = heap_lm_k.Heap(beam_size, k_best)
bins.append(b)
empty = heap_lm_k.State((), 0, '<s>', 0, 0, [0.0] * num_feature)
bins[0].add(empty)
for i in xrange(len(f_sentence)):
b = bins[i]
for k in xrange(b.size):
state = b.data[k]
for option in options:
cover = state.cover
j = state.j
d = option[0] - j
last_e = state.last_e
if (not cross(cover, option)) and (abs(d) <= d_limit):
# whether current f_pharse is the last part un-translated
is_end = False
new_cover = merge(cover, option)
if len(new_cover) == len(f_sentence):
is_end = True
# reorder the e_pharse based on last_e LM score
f_phrase = tuple(f_sentence[option[0]:option[1]])
items = new_phrase_table[f_phrase]
items = lm_order_list(lm_model,
items,
lm_weight,
last_e,
is_end,
limit=10)
# add into the bins
for item in items:
t_score = item[0] # lm score has already incorporated
d_score = -abs(d) * d_weight # note!
score = d_score + t_score + state.s
partial_score = list(item[2])
partial_score.append(-abs(d))
new_partial_score = array_plus(partial_score,
state.partial_score)
e_phrase = item[1]
new_last_e = e_phrase[-1]
new_j = option[1]
new_h = get_future_cost(fcost, new_cover,
len(f_sentence))
child_state = heap_lm_k.State(new_cover, new_j,
new_last_e, score, new_h,
new_partial_score)
child_state.e_phrase = e_phrase
delta = (child_state.f, child_state.s - state.s,
child_state.e_phrase,
array_minus(child_state.partial_score,
state.partial_score), state)
child_state.fathers = [
delta,
]
n_cover = len(new_cover)
bins[n_cover].add(child_state)
if debug:
for b in bins:
print b.data
paths = _mcr.get_k_best_paths(bins, k_best, tempFilePath)
partial_scores, translated_sentence = _mcr.backtrack_k(bins, paths)
end = datetime.now()
print end - start
return partial_scores, translated_sentence
