def fnc(persona, debug=False): # list of sentences
ret = []
for sent in persona:
cur_sent = {'sent':sent}
if debug:
print()
print("-x"*33)
print("=====>>>> sent = ", sent)
input_event = sent
category = "all"
if debug:
print()
print("category = ", category)
sampler = interactive.set_sampler(opt, sampling_algorithm, data_loader)
outputs = interactive.get_atomic_sequence(
input_event, model, sampler, data_loader, text_encoder, category)
# print("outputs = ", outputs)
cur_sent['comet'] = outputs
ret.append(cur_sent)
return ret
def query(input_event, category='xNeed', sampling_algorithm='beam-10 '):
sampler = interactive.set_sampler(opt, sampling_algorithm, data_loader)
outputs = interactive.get_atomic_sequence(input_event, model, sampler, data_loader, text_encoder, category)[category]
for k, v in outputs.items():
outputs[k] = v[::-1]
return outputs
def _get_result(self, event: str, category: Sequence[str]) -> Dict:
raw_result = interactive.get_atomic_sequence(event, self._model,
self._sampler,
self._data_loader,
self._text_encoder,
category)
return self.all_beams_cleanup(raw_result)
def get_comet_prediction(all_story):
# all_story list (bs) of list (5)
all_outputs = []
for i, story in enumerate(all_story):
output = []
if len(story) < 3:
all_outputs.append(output)
continue
protagonist = find_gender(story)
pos = create_pos(story, protagonist)
for k, input_event in enumerate(story):
#print(input_event)
input_event = input_event.replace(
",", "").strip(".").strip("!").strip()
input_event = ' '.join(i for i in input_event.split()[:17])
category = 'xReact' if pos[k] == 'x' else 'oReact'
sampling_algorithm = 'topk-1' if pos[k] == 'x' else 'topk-2'
sampler = interactive.set_sampler(opt, sampling_algorithm,
data_loader)
out = interactive.get_atomic_sequence(input_event, model, sampler,
data_loader, text_encoder,
category)
# out_file.write("%s\t%s\n" % (input_event, out[category]["beams"][0]))
if category == "oReact" and out[category]["beams"][0] == "none":
output.append(out[category]["beams"][1])
else:
output.append(out[category]["beams"][0])
all_outputs.append(output)
return all_outputs # list(bs) of list(5)
def gen_preds(model_obj, text_chunk):
model, sampler, data_loader, text_encoder = model_obj
category_list = ['oEffect', 'xEffect', 'xNeed']
atomic_output = {}
for sent_idx, sentence in enumerate(text_chunk):
atomic_output[sent_idx] = {}
for category in category_list:
outputs = interactive.get_atomic_sequence(sentence, model, sampler,
data_loader,
text_encoder, category)
atomic_output[sent_idx][category] = outputs[category]
return atomic_output
def augment(article):
title_new = (article.numpy().decode('UTF-8'))
category = "oEffect"
entity_list = nlp(title_new)
input_event = title_new
replacement_list = ["PersonX", "PersonY", "PersonZ"]
r = 0
for entity in entity_list.ents:
if entity.label_ == 'PERSON' or entity.label_ == 'NORP':
input_event = input_event.replace(entity.text, replacement_list[r])
r += 1
if (r == 3):
break
outputs = interactive.get_atomic_sequence(input_event, model, sampler,
data_loader, text_encoder,
category)
for key in outputs:
article = title_new + ((outputs[key]["beams"][0]))
return article
input_event = "help"
category = "help"
sampling_algorithm = args.sampling_algorithm
while input_event is None or input_event.lower() == "help":
input_event = input("Give an event (e.g., PersonX went to the mall): ")
if input_event == "help":
interactive.print_help(opt.dataset)
while category.lower() == "help":
category = input("Give an effect type (type \"help\" for an explanation): ")
if category == "help":
interactive.print_category_help(opt.dataset)
while sampling_algorithm.lower() == "help":
sampling_algorithm = input("Give a sampling algorithm (type \"help\" for an explanation): ")
if sampling_algorithm == "help":
interactive.print_sampling_help()
sampler = interactive.set_sampler(opt, sampling_algorithm, data_loader)
if category not in data_loader.categories:
category = "all"
outputs = interactive.get_atomic_sequence(
input_event, model, sampler, data_loader, text_encoder, category)
def augment(article):
context = (article.numpy().decode('UTF-8'))
category_list = ["xNeed", "xIntent", "xWant", "xReact"]
for category in category_list:
entity_list = nlp(context)
input_event = context
replaced = []
replacement_list = ["PersonX", "PersonY", "PersonZ"]
r = 0
for entity in entity_list.ents:
if entity.label_ == 'PERSON' or entity.label_ == 'NORP':
input_event = input_event.replace(entity.text,
replacement_list[r])
r += 1
if (r == 3):
break
outputs = interactive.get_atomic_sequence(input_event, model,
sampler, data_loader,
text_encoder, category)
for key in outputs:
prefix = ""
if (key[0] == "o"):
if (key == "oEffect"):
prefix = " Everyone else "
elif (key == "oReact"):
prefix = "They are "
elif (key == "oWant"):
prefix = "They want "
else:
if (len(replaced) != 0):
prefix = replaced[0]
else:
prefix = "Person"
if (key == "xAttr"):
prefix += " is "
elif (key == "xEffect"):
prefix += " "
elif (key == "xIntent"):
prefix += " intends "
elif (key == "xReact"):
prefix += " is "
elif (key == "xNeed"):
prefix += " needs "
elif (key == "xWant"):
prefix += " wants "
for j in range(5):
if (outputs[key]["beams"][j] != 'none'):
comet_inf = outputs[key]["beams"][j]
if (len(replaced) > 0):
comet_inf = comet_inf.replace(
"personx", replaced[0])
if (len(replaced) > 1):
comet_inf = comet_inf.replace(
"persony", replaced[1])
article += prefix + (comet_inf) + ". "
break
return article
def augment(self, article, dl, nlp, model, sampler, text_encoder):
# category = [""]
entity_list = nlp(article)
input_event = article
category_list = ["xNeed", "xIntent", "xWant", "xReact"]
for category in category_list:
replaced = []
replacement_list = ["PersonX", "PersonY"]
r = 0
for entity in entity_list.ents:
replaced += [entity.text]
input_event = input_event.replace(entity.text,
replacement_list[r])
r += 1
if (r == 2):
break
outputs = interactive.get_atomic_sequence(input_event, model,
sampler, dl,
text_encoder, category)
for key in outputs:
prefix = ""
if (key[0] == "o"):
if (key == "oEffect"):
prefix = " Everyone else "
elif (key == "oReact"):
prefix = "They are "
elif (key == "oWant"):
prefix = "They want "
else:
if (len(replaced) != 0):
prefix = replaced[0]
else:
prefix = "Person"
if (key == "xAttr"):
prefix += " is "
elif (key == "xEffect"):
prefix += " "
elif (key == "xIntent"):
prefix += " intends "
elif (key == "xReact"):
prefix += " is "
elif (key == "xNeed"):
prefix += " needs "
elif (key == "xWant"):
prefix += " wants "
for j in range(5):
if (outputs[key]["beams"][j] != 'none'):
comet_inf = outputs[key]["beams"][j]
if (len(replaced) > 0):
comet_inf = comet_inf.replace(
"personx", replaced[0])
if (len(replaced) > 1):
comet_inf = comet_inf.replace(
"persony", replaced[1])
article += prefix + (comet_inf) + ". "
break
# print(article)
# print("---------")
# assert 1 == 0
return article
news_list_of_input_events = []
for i in list_of_input_events:
if i not in news_list_of_input_events:
news_list_of_input_events.append(i)
list_of_input_events = news_list_of_input_events
#list_of_input_events = list(set(list_of_input_events))
list_of_input_events = list_of_input_events[10000:20000]
print(len(list_of_input_events))
print(list_of_input_events[:5])
output_file = open(args.output_file, 'a', encoding='utf-8')
for idx, sentence in enumerate(list_of_input_events):
outputs = interactive.get_atomic_sequence("PersonX says " + sentence,
model, sampler, data_loader,
text_encoder, args.category)
if idx % 100 == 0:
print(idx)
#print(outputs)
output_file.write(str(outputs))
output_file.write("\n")
output_file.flush()
output_file.close()
# print(type(outputs))
# with open(args.output_file, 'w', encoding = 'utf-8') as f:
# json.dump(corpus,f,sort_keys=False, indent=4, separators=(',', ': '), ensure_ascii=False)
n_ctx = data_loader.max_event + data_loader.max_effect
n_vocab = len(text_encoder.encoder) + n_ctx
model = interactive.make_model(opt, n_vocab, n_ctx, state_dict)
if args.device != "cpu":
cfg.device = int(args.device)
cfg.do_gpu = True
torch.cuda.set_device(cfg.device)
model.cuda(cfg.device)
else:
cfg.device = "cpu"
sampling_algorithm = args.sampling_algorithm
#category = args.category
#args.category = ["xReact","oReact"]
sampler = interactive.set_sampler(opt, sampling_algorithm, data_loader)
list_of_input_events = [
"I will go to school! I will go to school! I will go to school! I will go to school! I will go to school! I will go to school!",
"good",
"beautiful campus",
]
outputs = interactive.get_atomic_sequence(list_of_input_events[0], model,
sampler, data_loader,
text_encoder, args.category)
print(type(outputs))
print(outputs)
def _get_sequence(self, event, relations, sampler):
return interactive.get_atomic_sequence(event, self._model, sampler,
self._data_loader,
self._text_encoder, relations)
