def evaluate_all(word_dict, template_word_dict, slot_dict, dset, decoder, params):
results = []
if params['dia_slot_val'] == 2 or params['dia_slot_val'] == 3:
inverse_word_dict = {template_word_dict[k]:k for k in template_word_dict.keys()}
else:
inverse_word_dict = {word_dict[k]:k for k in word_dict.keys()}
for i, ele in enumerate(dset.diaact_sentence_pairs):
#pred_ys, pred_words = decoder.forward(inverse_word_dict, ele, params, predict_model=True)
pred_ys, pred_words = decoder.beam_forward(inverse_word_dict, ele, params, predict_model=True)
pred_template_sentence = ' '.join(pred_words[:-1])
real_sentence = ' '.join(ele['sentence'].split(' ')[1:-1])
real_template_sentence = ' '.join(ele['sentence_template'].split(' ')[1:-1])
# replace the slot_val place holder with real value
sentence = post_process(pred_template_sentence, ele['slotval'], slot_dict)
#results.append({'real':real_template_sentence, 'pred':pred_template_sentence})
results.append({'real':real_sentence, 'pred':sentence})
print 'test case', i, "/", len(dset.diaact_sentence_pairs)
print 'teamplate:', real_template_sentence
print 'pred_template:', pred_template_sentence
print 'real:', real_sentence
print 'pred_sentence:', sentence
print ''
return results
def read_diaact_from_string(text, word_dict, template_word_dict, act_dict,
slot_dict, decoder, params):
if params['dia_slot_val'] == 2 or params['dia_slot_val'] == 3:
inverse_word_dict = {
template_word_dict[k]: k
for k in template_word_dict.keys()
}
else:
inverse_word_dict = {word_dict[k]: k for k in word_dict.keys()}
annot = text.strip(' ').strip('\n').strip('\r')
dia_act = parse_str_to_diaact(annot, act_dict, slot_dict)
dia_act_rep = prepare_diaact_representation(dia_act, word_dict,
template_word_dict, act_dict,
slot_dict, params)
pred_ys, pred_words = decoder.beam_forward(inverse_word_dict,
dia_act_rep,
params,
predict_model=True)
pred_sentence = ' '.join(pred_words[:-1])
# replace the slot_val place holder with real value
sentence = post_process(pred_sentence, dia_act['inform_slots'], slot_dict)
return sentence
def read_diaact_from_cmd(word_dict, template_word_dict, act_dict, slot_dict, decoder, params):
if params['dia_slot_val'] == 2 or params['dia_slot_val'] == 3:
inverse_word_dict = {template_word_dict[k]:k for k in template_word_dict.keys()}
else:
inverse_word_dict = {word_dict[k]:k for k in word_dict.keys()}
while True:
print("Your input: ")
command = raw_input()
annot = command.strip(' ').strip('\n').strip('\r')
if len(annot) > 0:
dia_act = parse_str_to_diaact(annot, act_dict, slot_dict)
dia_act_rep = prepare_diaact_representation(dia_act, word_dict, template_word_dict, act_dict, slot_dict, params)
#pred_ys, pred_words = decoder.forward(inverse_word_dict, dia_act_rep, params, predict_model=True)
pred_ys, pred_words = decoder.beam_forward(inverse_word_dict, dia_act_rep, params, predict_model=True)
pred_sentence = ' '.join(pred_words[:-1])
# replace the slot_val place holder with real value
sentence = post_process(pred_sentence, dia_act['inform_slots'], slot_dict)
print 'Dia-Act:', annot
print 'Dia-Act Parsing:', dia_act
print 'Pred_Template:', pred_sentence
print 'NLG:', sentence
else:
break
def read_diaact_from_file(path, word_dict, template_word_dict, act_dict, slot_dict, decoder, params):
file = open(path, 'r')
lines = [line for line in file]
if params['dia_slot_val'] == 2 or params['dia_slot_val'] == 3:
inverse_word_dict = {template_word_dict[k]:k for k in template_word_dict.keys()}
else:
inverse_word_dict = {word_dict[k]:k for k in word_dict.keys()}
for lineindex, l in enumerate(lines):
l = l.strip('\n')
dia_act = parse_str_to_diaact(l, act_dict, slot_dict)
dia_act_rep = prepare_diaact_representation(dia_act, word_dict, template_word_dict, act_dict, slot_dict, params)
#pred_ys, pred_words = decoder.forward(inverse_word_dict, dia_act_rep, params, predict_model=True)
pred_ys, pred_words = decoder.beam_forward(inverse_word_dict, dia_act_rep, params, predict_model=True)
pred_sentence = ' '.join(pred_words[:-1])
# replace the slot_val place holder with real value
sentence = post_process(pred_sentence, dia_act['inform_slots'], slot_dict)
print 'Test case', lineindex
print 'Dia-Act:', l
print 'Pred_Template:', pred_sentence
print 'NLG:', sentence
def read_diaact_from_string(text, word_dict, template_word_dict, act_dict, slot_dict, decoder, params):
if params['dia_slot_val'] == 2 or params['dia_slot_val'] == 3:
inverse_word_dict = {template_word_dict[k]:k for k in template_word_dict.keys()}
else:
inverse_word_dict = {word_dict[k]:k for k in word_dict.keys()}
annot = text.strip(' ').strip('\n').strip('\r')
dia_act = parse_str_to_diaact(annot, act_dict, slot_dict)
dia_act_rep = prepare_diaact_representation(dia_act, word_dict, template_word_dict, act_dict, slot_dict, params)
pred_ys, pred_words = decoder.beam_forward(inverse_word_dict, dia_act_rep, params, predict_model=True)
pred_sentence = ' '.join(pred_words[:-1])
# replace the slot_val place holder with real value
sentence = post_process(pred_sentence, dia_act['inform_slots'], slot_dict)
return sentence
def evaluate(word_dict, template_word_dict, slot_dict, dset, split, decoder,
params):
results = []
if params['dia_slot_val'] == 2 or params['dia_slot_val'] == 3:
inverse_word_dict = {
template_word_dict[k]: k
for k in template_word_dict.keys()
}
else:
inverse_word_dict = {word_dict[k]: k for k in word_dict.keys()}
for i, ele in enumerate(dset.split[split]):
#pred_ys, pred_words = decoder.forward(inverse_word_dict, ele, params, predict_model=True)
pred_ys, pred_words = decoder.beam_forward(inverse_word_dict,
ele,
params,
predict_model=True)
pred_template_sentence = ' '.join(pred_words[:-1])
real_sentence = ' '.join(ele['sentence'].split(' ')[1:-1])
real_template_sentence = ' '.join(
ele['sentence_template'].split(' ')[1:-1])
# replace the slot_val place holder with real value
sentence = post_process(pred_template_sentence, ele['slotval'],
slot_dict)
#results.append({'real':real_template_sentence, 'pred':pred_template_sentence})
results.append({'real': real_sentence, 'pred': sentence})
print 'test case', i, "/", len(dset.split[split])
print 'teamplate:', real_template_sentence
print 'pred_template:', pred_template_sentence
print 'real:', real_sentence
print 'pred_sentence:', sentence
print ''
return results
def read_diaact_from_cmd(word_dict, template_word_dict, act_dict, slot_dict,
decoder, params):
if params['dia_slot_val'] == 2 or params['dia_slot_val'] == 3:
inverse_word_dict = {
template_word_dict[k]: k
for k in template_word_dict.keys()
}
else:
inverse_word_dict = {word_dict[k]: k for k in word_dict.keys()}
while True:
print("Your input: ")
command = raw_input()
annot = command.strip(' ').strip('\n').strip('\r')
if len(annot) > 0:
dia_act = parse_str_to_diaact(annot, act_dict, slot_dict)
dia_act_rep = prepare_diaact_representation(
dia_act, word_dict, template_word_dict, act_dict, slot_dict,
params)
#pred_ys, pred_words = decoder.forward(inverse_word_dict, dia_act_rep, params, predict_model=True)
pred_ys, pred_words = decoder.beam_forward(inverse_word_dict,
dia_act_rep,
params,
predict_model=True)
pred_sentence = ' '.join(pred_words[:-1])
# replace the slot_val place holder with real value
sentence = post_process(pred_sentence, dia_act['inform_slots'],
slot_dict)
print 'Dia-Act:', annot
print 'Dia-Act Parsing:', dia_act
print 'Pred_Template:', pred_sentence
print 'NLG:', sentence
else:
break
def read_diaact_from_file(path, word_dict, template_word_dict, act_dict,
slot_dict, decoder, params):
file = open(path, 'r')
lines = [line for line in file]
if params['dia_slot_val'] == 2 or params['dia_slot_val'] == 3:
inverse_word_dict = {
template_word_dict[k]: k
for k in template_word_dict.keys()
}
else:
inverse_word_dict = {word_dict[k]: k for k in word_dict.keys()}
for lineindex, l in enumerate(lines):
l = l.strip('\n')
dia_act = parse_str_to_diaact(l, act_dict, slot_dict)
dia_act_rep = prepare_diaact_representation(dia_act, word_dict,
template_word_dict,
act_dict, slot_dict,
params)
#pred_ys, pred_words = decoder.forward(inverse_word_dict, dia_act_rep, params, predict_model=True)
pred_ys, pred_words = decoder.beam_forward(inverse_word_dict,
dia_act_rep,
params,
predict_model=True)
pred_sentence = ' '.join(pred_words[:-1])
# replace the slot_val place holder with real value
sentence = post_process(pred_sentence, dia_act['inform_slots'],
slot_dict)
print 'Test case', lineindex
print 'Dia-Act:', l
print 'Pred_Template:', pred_sentence
print 'NLG:', sentence