option = parser.parse_args()
logging.basicConfig(
stream=sys.stdout,
level=logging.DEBUG,
format=
'%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s'
)
torch.manual_seed(option.random_seed)
glove = Glove(option.emb_file)
logging.info('Embeddings loaded')
embeddings = nn.Embedding(option.vocab_size, option.emb_dim, padding_idx=1)
if option.model == 'NTN':
event_model = NeuralTensorNetwork(embeddings, option.em_k)
elif option.model == 'RoleFactor':
event_model = RoleFactoredTensorModel(embeddings, option.em_k)
elif option.model == 'LowRankNTN':
event_model = LowRankNeuralTensorNetwork(embeddings, option.em_k,
option.em_r)
else:
logging.info('Unknown model type: ' + option.model)
exit(1)
intent_model = BiLSTMEncoder(embeddings, option.im_hidden_size,
option.im_num_layers)
if option.em_actv_func == 'sigmoid':
em_actv_func = nn.Sigmoid()
elif option.em_actv_func == 'relu':
em_actv_func = nn.ReLU()
def main(option):
random.seed(option.random_seed)
torch.manual_seed(option.random_seed)
LOG_FORMAT = '%(asctime)s %(name)-12s %(levelname)-8s %(message)s'
logging.basicConfig(format=LOG_FORMAT, level='INFO', stream=sys.stdout)
glove = Glove(option.emb_file)
logging.info('loaded embeddings from ' + option.emb_file)
src_vocab = Vocab.build_from_glove(glove)
tgt_vocab = Vocab.load(option.intent_vocab)
train_dataset = load_intent_prediction_dataset(option.train_dataset,
src_vocab,
tgt_vocab,
device=option.device)
dev_dataset = load_intent_prediction_dataset(option.dev_dataset,
src_vocab,
tgt_vocab,
device=option.device)
train_data_loader = DataLoader(train_dataset,
batch_size=option.batch_size,
shuffle=True)
dev_data_loader = DataLoader(dev_dataset,
batch_size=len(dev_dataset),
shuffle=False)
src_vocab_size = len(src_vocab)
tgt_vocab_size = len(tgt_vocab)
# Prepare loss
weight = torch.ones(tgt_vocab_size)
pad = tgt_vocab.stoi[tgt_vocab.pad_token]
loss = Perplexity(weight, pad)
loss.criterion.to(option.device)
# Initialize model
encoder = NeuralTensorNetwork(nn.Embedding(src_vocab_size, option.emb_dim),
option.em_k)
decoder = DecoderRNN(tgt_vocab_size,
option.im_max_len,
option.im_hidden_size,
use_attention=False,
bidirectional=False,
eos_id=tgt_vocab.stoi[tgt_vocab.eos_token],
sos_id=tgt_vocab.stoi[tgt_vocab.bos_token])
encoder.to(option.device)
decoder.to(option.device)
init_model(encoder)
init_model(decoder)
encoder.embeddings.weight.data.copy_(torch.from_numpy(glove.embd).float())
optimizer_params = [{
'params': encoder.parameters()
}, {
'params': decoder.parameters()
}]
optimizer = Optimizer(optim.Adam(optimizer_params, lr=option.lr),
max_grad_norm=5)
trainer = NTNTrainer(loss,
print_every=option.report_every,
device=option.device)
encoder, decoder = trainer.train(
encoder,
decoder,
optimizer,
train_data_loader,
num_epochs=option.epochs,
dev_data_loader=dev_data_loader,
teacher_forcing_ratio=option.im_teacher_forcing_ratio)
predictor = NTNPredictor(encoder, decoder, src_vocab, tgt_vocab,
option.device)
samples = [
("PersonX", "eventually told", "___"),
("PersonX", "tells", "PersonY 's tale"),
("PersonX", "always played", " ___"),
("PersonX", "would teach", "PersonY"),
("PersonX", "gets", "a ride"),
]
for sample in samples:
subj, verb, obj = sample
subj = subj.lower().split(' ')
verb = verb.lower().split(' ')
obj = obj.lower().split(' ')
print(sample, predictor.predict(subj, verb, obj))
def main(option):
glove = Glove(option.emb_file)
print(option.emb_file + ' loaded')
embedding = Embedding(option.vocab_size, option.emb_dim, padding_idx=1)
if option.model_type == 'NTN':
model = NeuralTensorNetwork(embedding, option.em_k)
elif option.model_type == 'LowRankNTN':
model = LowRankNeuralTensorNetwork(embedding, option.em_k, option.em_r)
checkpoint = torch.load(option.model_file, map_location='cpu')
if type(checkpoint) == dict:
if 'event_model_state_dict' in checkpoint:
state_dict = checkpoint['event_model_state_dict']
else:
state_dict = checkpoint['model_state_dict']
else:
state_dict = checkpoint
model.load_state_dict(state_dict)
print(option.model_file + ' loaded')
model.eval()
model.to(option.device)
all_subj_id = []
all_subj_w = []
all_verb_id = []
all_verb_w = []
all_obj_id = []
all_obj_w = []
all_labels = []
all_event_texts = []
for label, filename in enumerate(option.input_files):
print('loading ' + filename)
lines = open(filename, 'r').readlines()
for line in lines:
subj, verb, obj = line.lower().strip().split(' | ')
event_text = '(' + subj + ', ' + verb + ', ' + obj + ')'
subj = subj.split(' ')
verb = verb.split(' ')
obj = obj.split(' ')
subj_id, subj_w = glove.transform(subj, 10)
verb_id, verb_w = glove.transform(verb, 10)
obj_id, obj_w = glove.transform(obj, 10)
if subj_id is not None and verb_id is not None and obj_id is not None and event_text not in all_event_texts:
all_subj_id.append(subj_id)
all_subj_w.append(subj_w)
all_verb_id.append(verb_id)
all_verb_w.append(verb_w)
all_obj_id.append(obj_id)
all_obj_w.append(obj_w)
all_labels.append(label)
all_event_texts.append(event_text)
all_subj_id = torch.tensor(all_subj_id,
dtype=torch.long,
device=option.device)
all_subj_w = torch.tensor(all_subj_w,
dtype=torch.float,
device=option.device)
all_verb_id = torch.tensor(all_verb_id,
dtype=torch.long,
device=option.device)
all_verb_w = torch.tensor(all_verb_w,
dtype=torch.float,
device=option.device)
all_obj_id = torch.tensor(all_obj_id,
dtype=torch.long,
device=option.device)
all_obj_w = torch.tensor(all_obj_w,
dtype=torch.float,
device=option.device)
all_event_embeddings = model(all_subj_id, all_subj_w, all_verb_id,
all_verb_w, all_obj_id,
all_obj_w).detach().cpu()
torch.save(
{
'embeddings': all_event_embeddings,
'labels': torch.tensor(all_labels, dtype=torch.long),
'event_texts': all_event_texts
}, option.output_file)
print('saved to ' + option.output_file)
def main(option):
logging.basicConfig(
stream=sys.stdout,
level=logging.DEBUG,
format=
'%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s'
)
torch.manual_seed(option.random_seed)
glove = Glove(option.emb_file)
logging.info('Embeddings loaded')
train_dataset = EventIntentSentimentDataset()
logging.info('Loading train dataset: ' + option.train_dataset)
train_dataset.load(option.train_dataset, glove)
logging.info('Loaded train dataset: ' + option.train_dataset)
train_data_loader = torch.utils.data.DataLoader(
train_dataset,
collate_fn=EventIntentSentimentDataset_collate_fn,
batch_size=option.batch_size,
shuffle=True)
if option.dev_dataset is not None:
dev_dataset = EventIntentSentimentDataset()
logging.info('Loading dev dataset: ' + option.dev_dataset)
dev_dataset.load(option.dev_dataset, glove)
logging.info('Loaded dev dataset: ' + option.dev_dataset)
dev_data_loader = torch.utils.data.DataLoader(
dev_dataset,
collate_fn=EventIntentSentimentDataset_collate_fn,
batch_size=len(dev_dataset),
shuffle=False)
yago_train_dataset = YagoDataset()
logging.info('Loading YAGO train dataset: ' + option.yago_train_dataset)
yago_train_dataset.load(option.yago_train_dataset, glove)
yago_train_data_loader = torch.utils.data.DataLoader(
yago_train_dataset,
collate_fn=YagoDataset_collate_fn,
batch_size=option.batch_size,
shuffle=True)
if option.yago_dev_dataset is not None:
yago_dev_dataset = YagoDataset()
logging.info('Loading YAGO dev dataset: ' + option.yago_dev_dataset)
yago_dev_dataset.load(option.yago_dev_dataset, glove)
yago_dev_data_loader = torch.utils.data.DataLoader(
yago_dev_dataset,
collate_fn=YagoDataset_collate_fn,
batch_size=len(yago_dev_dataset),
shuffle=False)
embeddings = nn.Embedding(option.vocab_size, option.emb_dim, padding_idx=1)
if option.model_type == 'NTN':
event_model = NeuralTensorNetwork(embeddings, option.em_k)
elif option.model_type == 'RoleFactor':
event_model = RoleFactoredTensorModel(embeddings, option.em_k)
elif option.model_type == 'LowRankNTN':
event_model = LowRankNeuralTensorNetwork(embeddings, option.em_k,
option.em_r)
intent_model = BiLSTMEncoder(embeddings, option.im_hidden_size,
option.im_num_layers)
relation_models = {}
for relation in yago_train_dataset.relations:
relation_model = NeuralTensorNetwork_yago(embeddings, option.em_k)
relation_models[relation] = relation_model
if option.scorer_actv_func == 'sigmoid':
scorer_actv_func = nn.Sigmoid
elif option.scorer_actv_func == 'relu':
scorer_actv_func = nn.ReLU
elif option.scorer_actv_func == 'tanh':
scorer_actv_func = nn.Tanh
event_scorer = nn.Sequential(nn.Linear(option.em_k, 1), scorer_actv_func())
relation_scorer = nn.Sequential(nn.Linear(option.em_k, 1),
scorer_actv_func())
intent_scorer = nn.CosineSimilarity(dim=1)
sentiment_classifier = nn.Linear(option.em_k, 1)
criterion = MarginLoss(option.margin)
sentiment_criterion = nn.BCEWithLogitsLoss()
yago_criterion = MarginLoss(option.yago_margin)
# load pretrained embeddings
embeddings.weight.data.copy_(torch.from_numpy(glove.embd).float())
if not option.update_embeddings:
event_model.embeddings.weight.requires_grad = False
if option.use_gpu:
event_model.cuda()
intent_model.cuda()
sentiment_classifier.cuda()
event_scorer.cuda()
relation_scorer.cuda()
for relation_model in relation_models.values():
relation_model.cuda()
embeddings_param_id = [id(param) for param in embeddings.parameters()]
params = [{
'params': embeddings.parameters()
}, {
'params': [
param for param in event_model.parameters()
if id(param) not in embeddings_param_id
],
'weight_decay':
option.weight_decay
}, {
'params': [
param for param in event_scorer.parameters()
if id(param) not in embeddings_param_id
],
'weight_decay':
option.weight_decay
}, {
'params': [
param for param in intent_model.parameters()
if id(param) not in embeddings_param_id
],
'weight_decay':
option.weight_decay
}, {
'params': [
param for param in sentiment_classifier.parameters()
if id(param) not in embeddings_param_id
],
'weight_decay':
option.weight_decay
}]
for relation in relation_models:
params.append({
'params': [
param for param in relation_models[relation].parameters()
if id(param) not in embeddings_param_id
],
'weight_decay':
option.weight_decay
})
optimizer = torch.optim.Adagrad(params, lr=option.lr)
# load checkpoint if provided:
if option.load_checkpoint != '':
checkpoint = torch.load(option.load_checkpoint)
event_model.load_state_dict(checkpoint['event_model_state_dict'])
intent_model.load_state_dict(checkpoint['intent_model_state_dict'])
event_scorer.load_state_dict(checkpoint['event_scorer_state_dict'])
sentiment_classifier.load_state_dict(
checkpoint['sentiment_classifier_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
for relation in relation_models:
relation_models[relation].load_state_dict(
checkpoint['relation_model_state_dict'][relation])
logging.info('Loaded checkpoint: ' + option.load_checkpoint)
# load pretrained event model instead:
elif option.pretrained_event_model != '':
checkpoint = torch.load(option.pretrained_event_model)
event_model.load_state_dict(checkpoint['model_state_dict'])
logging.info('Loaded pretrained event model: ' +
option.pretrained_event_model)
for epoch in range(option.epochs):
epoch += 1
logging.info('Epoch ' + str(epoch))
# train set
# train set
avg_loss_e = 0
avg_loss_i = 0
avg_loss_s = 0
avg_loss = 0
avg_loss_sum = 0
avg_loss_event = 0
avg_loss_attr = 0
k = 0
# assume yago dataset is larger than atomic
atomic_iterator = itertools.cycle(iter(train_data_loader))
yago_iterator = iter(yago_train_data_loader)
# iterate over yago dataset (atomic dataset is cycled)
for i, yago_batch in enumerate(yago_iterator):
atomic_batch = next(atomic_iterator)
optimizer.zero_grad()
loss, loss_e, loss_i, loss_s = run_batch(
option, atomic_batch, event_model, intent_model, event_scorer,
intent_scorer, sentiment_classifier, criterion,
sentiment_criterion)
loss.backward()
optimizer.step()
avg_loss_e += loss_e.item() / option.report_every
avg_loss_i += loss_i.item() / option.report_every
avg_loss_s += loss_s.item() / option.report_every
avg_loss += loss.item() / option.report_every
if i % option.report_every == 0:
logging.info(
'Atomic batch %d, loss_e=%.4f, loss_i=%.4f, loss_s=%.4f, loss=%.4f'
% (i, avg_loss_e, avg_loss_i, avg_loss_s, avg_loss))
avg_loss_e = 0
avg_loss_i = 0
avg_loss_s = 0
avg_loss = 0
optimizer.zero_grad()
loss_sum, loss_event, loss_attr = run_yago_batch(
option, yago_batch, event_model, relation_models,
yago_criterion, relation_scorer, event_scorer)
loss_sum.backward()
optimizer.step()
avg_loss_sum += loss_sum.item() / option.report_every
avg_loss_event += loss_event.item() / option.report_every
avg_loss_attr += loss_attr.item() / option.report_every
if i % option.report_every == 0:
logging.info(
'YAGO batch %d, loss_event=%.4f, loss_attr=%.4f, loss=%.4f'
% (i, avg_loss_event, avg_loss_attr, avg_loss_sum))
avg_loss_sum = 0
avg_loss_event = 0
avg_loss_attr = 0
# dev set
if option.dev_dataset is not None:
event_model.eval()
intent_model.eval()
event_scorer.eval()
sentiment_classifier.eval()
batch = next(iter(dev_data_loader))
with torch.no_grad():
loss, loss_e, loss_i, loss_s = run_batch(
option, batch, event_model, intent_model, event_scorer,
intent_scorer, sentiment_classifier, criterion,
sentiment_criterion)
logging.info(
'Eval on dev set, loss_e=%.4f, loss_i=%.4f, loss_s=%.4f, loss=%.4f'
% (loss_e.item(), loss_i.item(), loss_s.item(), loss.item()))
event_model.train()
intent_model.train()
event_scorer.train()
sentiment_classifier.train()
# dev set (yago)
if option.yago_dev_dataset is not None:
for key in relation_models.keys():
relation_models[key].eval()
relation_scorer.eval()
event_model.eval()
yago_dev_batch = next(iter(yago_dev_data_loader))
with torch.no_grad():
loss_sum, loss_event, loss_attr = run_yago_batch(
option, yago_dev_batch, event_model, relation_models,
criterion_yago, relation_scorer, event_scorer)
logging.info(
'Eval on yago dev set, loss_sum=%.4f, loss_event=%.4f, loss_attr=%.4f, '
% (loss_sum.item(), loss_event.item(), loss_attr.item()))
for key in relation_models.keys():
relation_models[key].train()
relation_scorer.train()
if option.save_checkpoint != '':
checkpoint = {
'event_model_state_dict':
event_model.state_dict(),
'intent_model_state_dict':
intent_model.state_dict(),
'event_scorer_state_dict':
event_scorer.state_dict(),
'sentiment_classifier_state_dict':
sentiment_classifier.state_dict(),
'optimizer_state_dict':
optimizer.state_dict(),
'relation_model_state_dict': {
relation: relation_models[relation].state_dict()
for relation in relation_models
}
}
torch.save(checkpoint, option.save_checkpoint + '_' + str(epoch))
logging.info('Saved checkpoint: ' + option.save_checkpoint + '_' +
str(epoch))
def main(option):
logging.basicConfig(
stream=sys.stdout,
level=logging.DEBUG,
format=
'%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s'
)
dataset = HardSimilarityDataset()
if option.dataset_cache is None:
glove = Glove(option.emb_file)
logging.info('Embeddings loaded')
dataset.load(option.dataset_file, glove)
else:
dataset.load_cache(option.dataset_cache)
logging.info('Dataset loaded')
embeddings = nn.Embedding(option.vocab_size, option.emb_dim, padding_idx=1)
if option.model == 'NTN':
model = NeuralTensorNetwork(embeddings, option.em_k)
elif option.model == 'LowRankNTN':
model = LowRankNeuralTensorNetwork(embeddings, option.em_k,
option.em_r)
elif option.model == 'RoleFactor':
model = RoleFactoredTensorModel(embeddings, option.em_k)
elif option.model == 'Predicate':
model = PredicateTensorModel(embeddings)
elif option.model == 'NN':
model = NN(embeddings, 2 * option.em_k, option.em_k)
elif option.model == 'EMC':
model = EMC(embeddings, 2 * option.em_k, option.em_k)
else:
logging.info('Unknown model type: ' + option.model)
exit(1)
checkpoint = torch.load(option.model_file, map_location='cpu')
if type(checkpoint) == dict:
if 'event_model_state_dict' in checkpoint:
state_dict = checkpoint['event_model_state_dict']
else:
state_dict = checkpoint['model_state_dict']
else:
state_dict = checkpoint
model.load_state_dict(state_dict)
logging.info(option.model_file + ' loaded')
# embeddings = nn.Embedding(option.vocab_size, option.emb_dim, padding_idx=1)
# embeddings.weight.data = torch.from_numpy(glove.embd).float()
# model = Averaging(embeddings)
if option.use_gpu:
model.cuda()
model.eval()
data_loader = torch.utils.data.DataLoader(
dataset,
collate_fn=HardSimilarityDataset_collate_fn,
shuffle=False,
batch_size=len(dataset))
batch = next(iter(data_loader))
pos_e1_subj_id, pos_e1_subj_w, pos_e1_verb_id, pos_e1_verb_w, pos_e1_obj_id, pos_e1_obj_w, \
pos_e2_subj_id, pos_e2_subj_w, pos_e2_verb_id, pos_e2_verb_w, pos_e2_obj_id, pos_e2_obj_w, \
neg_e1_subj_id, neg_e1_subj_w, neg_e1_verb_id, neg_e1_verb_w, neg_e1_obj_id, neg_e1_obj_w, \
neg_e2_subj_id, neg_e2_subj_w, neg_e2_verb_id, neg_e2_verb_w, neg_e2_obj_id, neg_e2_obj_w = batch
if option.use_gpu:
pos_e1_subj_id = pos_e1_subj_id.cuda()
pos_e1_subj_w = pos_e1_subj_w.cuda()
pos_e1_verb_id = pos_e1_verb_id.cuda()
pos_e1_verb_w = pos_e1_verb_w.cuda()
pos_e1_obj_id = pos_e1_obj_id.cuda()
pos_e1_obj_w = pos_e1_obj_w.cuda()
pos_e2_subj_id = pos_e2_subj_id.cuda()
pos_e2_subj_w = pos_e2_subj_w.cuda()
pos_e2_verb_id = pos_e2_verb_id.cuda()
pos_e2_verb_w = pos_e2_verb_w.cuda()
pos_e2_obj_id = pos_e2_obj_id.cuda()
pos_e2_obj_w = pos_e2_obj_w.cuda()
neg_e1_subj_id = neg_e1_subj_id.cuda()
neg_e1_subj_w = neg_e1_subj_w.cuda()
neg_e1_verb_id = neg_e1_verb_id.cuda()
neg_e1_verb_w = neg_e1_verb_w.cuda()
neg_e1_obj_id = neg_e1_obj_id.cuda()
neg_e1_obj_w = neg_e1_obj_w.cuda()
neg_e2_subj_id = neg_e2_subj_id.cuda()
neg_e2_subj_w = neg_e2_subj_w.cuda()
neg_e2_verb_id = neg_e2_verb_id.cuda()
neg_e2_verb_w = neg_e2_verb_w.cuda()
neg_e2_obj_id = neg_e2_obj_id.cuda()
neg_e2_obj_w = neg_e2_obj_w.cuda()
pos_e1_emb = model(pos_e1_subj_id, pos_e1_subj_w, pos_e1_verb_id,
pos_e1_verb_w, pos_e1_obj_id, pos_e1_obj_w)
pos_e2_emb = model(pos_e2_subj_id, pos_e2_subj_w, pos_e2_verb_id,
pos_e2_verb_w, pos_e2_obj_id, pos_e2_obj_w)
neg_e1_emb = model(neg_e1_subj_id, neg_e1_subj_w, neg_e1_verb_id,
neg_e1_verb_w, neg_e1_obj_id, neg_e1_obj_w)
neg_e2_emb = model(neg_e2_subj_id, neg_e2_subj_w, neg_e2_verb_id,
neg_e2_verb_w, neg_e2_obj_id, neg_e2_obj_w)
if option.distance_metric == 'cosine':
distance_func = cosine_distance
elif option.distance_metric == 'euclid':
distance_func = euclid_distance
pos_dist = distance_func(pos_e1_emb, pos_e2_emb)
neg_dist = distance_func(neg_e1_emb, neg_e2_emb)
num_correct = (pos_dist < neg_dist).sum().item()
accuracy = num_correct / len(dataset)
if option.output_file.strip() != '':
output_file = open(option.output_file, 'w')
for i, j, k in zip(pos_dist, neg_dist, (pos_dist < neg_dist)):
output_file.write(
' '.join([str(i.item(
)), str(j.item()), str(k.item())]) + '\n')
output_file.close()
logging.info('Output saved to ' + option.output_file)
logging.info('Num correct: %d' % (num_correct, ))
logging.info('Num total: %d' % (len(dataset), ))
logging.info('Accuracy: %.4f' % (accuracy, ))
if option.output_vectors is not None:
vectors = torch.stack([pos_e1_emb, pos_e2_emb, neg_e1_emb, neg_e2_emb],
dim=1).cpu()
torch.save(vectors, option.output_vectors)
print('Vectors saved to %s' % (option.output_vectors, ))
def main(option):
logging.basicConfig(
stream=sys.stdout,
level=logging.DEBUG,
format=
'%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s'
)
torch.manual_seed(option.random_seed)
glove = Glove(option.emb_file)
logging.info('Embeddings loaded')
embeddings = nn.Embedding(option.vocab_size, option.emb_dim, padding_idx=1)
if option.model_type == 'NTN':
event_model = NeuralTensorNetwork(embeddings, option.em_k)
elif option.model_type == 'RoleFactor':
event_model = RoleFactoredTensorModel(embeddings, option.em_k)
elif option.model_type == 'LowRankNTN':
event_model = LowRankNeuralTensorNetwork(embeddings, option.em_k,
option.em_r)
intent_model = BiLSTMEncoder(embeddings, option.im_hidden_size,
option.im_num_layers)
if option.em_actv_func == 'sigmoid':
em_actv_func = nn.Sigmoid()
elif option.em_actv_func == 'relu':
em_actv_func = nn.ReLU()
elif option.em_actv_func == 'tanh':
em_actv_func = nn.Tanh()
else:
logging.info('Unknown event activation func: ' + option.em_actv_func)
exit(1)
event_scorer = nn.Sequential(nn.Linear(option.em_k, 1), em_actv_func)
intent_scorer = nn.CosineSimilarity(dim=1)
sentiment_classifier = nn.Linear(option.em_k, 1)
criterion = MarginLoss(option.margin)
sentiment_criterion = nn.BCEWithLogitsLoss()
# load pretrained embeddings
embeddings.weight.data.copy_(torch.from_numpy(glove.embd).float())
if not option.update_embeddings:
event_model.embeddings.weight.requires_grad = False
if option.use_gpu:
event_model.cuda()
intent_model.cuda()
sentiment_classifier.cuda()
event_scorer.cuda()
embeddings_param_id = [id(param) for param in embeddings.parameters()]
params = [{
'params': embeddings.parameters()
}, {
'params': [
param for param in event_model.parameters()
if id(param) not in embeddings_param_id
],
'weight_decay':
option.weight_decay
}, {
'params': [
param for param in event_scorer.parameters()
if id(param) not in embeddings_param_id
],
'weight_decay':
option.weight_decay
}, {
'params': [
param for param in intent_model.parameters()
if id(param) not in embeddings_param_id
],
'weight_decay':
option.weight_decay
}, {
'params': [
param for param in sentiment_classifier.parameters()
if id(param) not in embeddings_param_id
],
'weight_decay':
option.weight_decay
}]
optimizer = torch.optim.Adagrad(params, lr=option.lr)
# load checkpoint if provided:
if option.load_checkpoint != '':
checkpoint = torch.load(option.load_checkpoint)
event_model.load_state_dict(checkpoint['event_model_state_dict'])
intent_model.load_state_dict(checkpoint['intent_model_state_dict'])
event_scorer.load_state_dict(checkpoint['event_scorer_state_dict'])
sentiment_classifier.load_state_dict(
checkpoint['sentiment_classifier_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
logging.info('Loaded checkpoint: ' + option.load_checkpoint)
# load pretrained event model instead:
elif option.pretrained_event_model != '':
checkpoint = torch.load(option.pretrained_event_model)
event_model.load_state_dict(checkpoint['model_state_dict'])
logging.info('Loaded pretrained event model: ' +
option.pretrained_event_model)
train_dataset = EventIntentSentimentDataset()
logging.info('Loading train dataset: ' + option.train_dataset)
train_dataset.load(option.train_dataset, glove)
logging.info('Loaded train dataset: ' + option.train_dataset)
train_data_loader = torch.utils.data.DataLoader(
train_dataset,
collate_fn=EventIntentSentimentDataset_collate_fn,
batch_size=option.batch_size,
shuffle=True)
if option.dev_dataset is not None:
dev_dataset = EventIntentSentimentDataset()
logging.info('Loading dev dataset: ' + option.dev_dataset)
dev_dataset.load(option.dev_dataset, glove)
logging.info('Loaded dev dataset: ' + option.dev_dataset)
dev_data_loader = torch.utils.data.DataLoader(
dev_dataset,
collate_fn=EventIntentSentimentDataset_collate_fn,
batch_size=len(dev_dataset),
shuffle=False)
for epoch in range(option.epochs):
epoch += 1
logging.info('Epoch ' + str(epoch))
# train set
avg_loss_e = 0
avg_loss_i = 0
avg_loss_s = 0
avg_loss = 0
for i, batch in enumerate(train_data_loader):
i += 1
optimizer.zero_grad()
loss, loss_e, loss_i, loss_s = run_batch(
option, batch, event_model, intent_model, event_scorer,
intent_scorer, sentiment_classifier, criterion,
sentiment_criterion)
loss.backward()
optimizer.step()
avg_loss_e += loss_e.item() / option.report_every
avg_loss_i += loss_i.item() / option.report_every
avg_loss_s += loss_s.item() / option.report_every
avg_loss += loss.item() / option.report_every
if i % option.report_every == 0:
logging.info(
'Batch %d, loss_e=%.4f, loss_i=%.4f, loss_s=%.4f, loss=%.4f'
% (i, avg_loss_e, avg_loss_i, avg_loss_s, avg_loss))
avg_loss_e = 0
avg_loss_i = 0
avg_loss_s = 0
avg_loss = 0
# dev set
if option.dev_dataset is not None:
event_model.eval()
intent_model.eval()
event_scorer.eval()
sentiment_classifier.eval()
batch = next(iter(dev_data_loader))
loss, loss_e, loss_i, loss_s = run_batch(
option, batch, event_model, intent_model, event_scorer,
intent_scorer, sentiment_classifier, criterion,
sentiment_criterion)
logging.info(
'Eval on dev set, loss_e=%.4f, loss_i=%.4f, loss_s=%.4f, loss=%.4f'
% (loss_e.item(), loss_i.item(), loss_s.item(), loss.item()))
event_model.train()
intent_model.train()
event_scorer.train()
sentiment_classifier.train()
if option.save_checkpoint != '':
checkpoint = {
'event_model_state_dict':
event_model.state_dict(),
'intent_model_state_dict':
intent_model.state_dict(),
'event_scorer_state_dict':
event_scorer.state_dict(),
'sentiment_classifier_state_dict':
sentiment_classifier.state_dict(),
'optimizer_state_dict':
optimizer.state_dict()
}
torch.save(checkpoint, option.save_checkpoint + '_' + str(epoch))
logging.info('Saved checkpoint: ' + option.save_checkpoint + '_' +
str(epoch))
def __init__(self,
vocab_size,
hidden_dim,
word_vec,
L2_penalty,
MARGIN,
LR,
T,
BATCH_SIZE=1000,
em_r=10,
dropout_p=0.2,
event_repr='cat',
pretrained_event_model=''):
super(EventGraph_With_Args, self).__init__()
self.hidden_dim = hidden_dim
self.vocab_size = vocab_size
self.batch_size = BATCH_SIZE
self.embedding = nn.Embedding(self.vocab_size, self.hidden_dim)
self.embedding.weight.data = torch.from_numpy(word_vec)
# self.embedding.weight.requires_grad=False
self.gnn = GNN(self.hidden_dim, T)
# self.fnn = FNN(self.hidden_dim)
# compute
self.linear_s_one = nn.Linear(hidden_dim, 1, bias=False)
self.linear_s_two = nn.Linear(hidden_dim, 1, bias=True)
self.linear_u_one = nn.Linear(hidden_dim,
int(0.5 * hidden_dim),
bias=True)
self.linear_u_one2 = nn.Linear(int(0.5 * hidden_dim), 1, bias=True)
self.linear_u_two = nn.Linear(hidden_dim,
int(0.5 * hidden_dim),
bias=True)
self.linear_u_two2 = nn.Linear(int(0.5 * hidden_dim), 1, bias=True)
# end compute
# event compositional model
self.event_repr = event_repr
if event_repr in ['ntn', 'role_factor', 'low_rank_ntn']:
if event_repr == 'ntn':
self.event_model = NeuralTensorNetwork(int(hidden_dim / 4),
int(hidden_dim / 4),
int(hidden_dim / 4))
elif event_repr == 'low_rank_ntn':
self.event_model = LowRankNeuralTensorNetwork(
int(hidden_dim / 4), int(hidden_dim / 4), em_r,
int(hidden_dim / 4))
elif event_repr == 'role_factor':
self.event_model = RoleFactoredTensorModel(
int(hidden_dim / 4), int(hidden_dim / 4))
if pretrained_event_model != '':
state_dict = torch.load(pretrained_event_model)
if 'model_state_dict' in state_dict:
state_dict = state_dict['model_state_dict']
elif 'event_model_state_dict' in state_dict:
state_dict = state_dict['event_model_state_dict']
embedding_weight = state_dict['embeddings.weight']
del state_dict['embeddings.weight']
self.event_model.load_state_dict(state_dict)
self.embedding.weight.data = embedding_weight
# end event compositional model
self.multi = Parameter(torch.ones(3))
self.dropout = nn.Dropout(dropout_p)
self.loss_function = nn.MultiMarginLoss(margin=MARGIN)
model_grad_params = filter(lambda p: p.requires_grad == True,
self.parameters())
train_params = list(self.embedding.parameters())
if self.event_repr in ['ntn', 'role_factor']:
train_params += list(self.event_model.parameters())
train_params_id = list(map(id, train_params))
tune_params = filter(lambda p: id(p) not in train_params_id,
model_grad_params)
self.optimizer = optim.RMSprop([{
'params': tune_params
}, {
'params': train_params,
'lr': LR * 0.06
}],
lr=LR,
weight_decay=L2_penalty,
momentum=0.2)
def main(option):
logging.basicConfig(
stream=sys.stdout,
level=logging.DEBUG,
format=
'%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s'
)
dataset = TransitiveSentenceSimilarityDataset()
if option.dataset_cache is None:
glove = Glove(option.emb_file)
logging.info('Embeddings loaded')
dataset.load(option.dataset_file, glove)
else:
dataset.load_cache(option.dataset_cache)
logging.info('Dataset loaded')
embeddings = nn.Embedding(option.vocab_size, option.emb_dim, padding_idx=1)
if option.model == 'NTN':
model = NeuralTensorNetwork(embeddings, option.em_k)
elif option.model == 'LowRankNTN':
model = LowRankNeuralTensorNetwork(embeddings, option.em_k,
option.em_r)
elif option.model == 'RoleFactor':
model = RoleFactoredTensorModel(embeddings, option.em_k)
elif option.model == 'Predicate':
model = PredicateTensorModel(embeddings)
elif option.model == 'NN':
model = NN(embeddings, 2 * option.em_k, option.em_k)
elif option.model == 'EMC':
model = EMC(embeddings, 2 * option.em_k, option.em_k)
else:
logging.info('Unknown model type: ' + option.model)
exit(1)
checkpoint = torch.load(option.model_file, map_location='cpu')
if type(checkpoint) == dict:
if 'event_model_state_dict' in checkpoint:
state_dict = checkpoint['event_model_state_dict']
else:
state_dict = checkpoint['model_state_dict']
else:
state_dict = checkpoint
model.load_state_dict(state_dict)
logging.info(option.model_file + ' loaded')
# embeddings = nn.Embedding(option.vocab_size, option.emb_dim, padding_idx=1)
# embeddings.weight.data = torch.from_numpy(glove.embd).float()
# model = Averaging(embeddings)
if option.use_gpu:
model.cuda()
model.eval()
data_loader = torch.utils.data.DataLoader(
dataset,
collate_fn=TransitiveSentenceSimilarityDataset_collate_fn,
shuffle=False,
batch_size=len(dataset))
batch = next(iter(data_loader))
e1_subj_id, e1_subj_w, e1_verb_id, e1_verb_w, e1_obj_id, e1_obj_w, \
e2_subj_id, e2_subj_w, e2_verb_id, e2_verb_w, e2_obj_id, e2_obj_w, \
gold = batch
if option.use_gpu:
e1_subj_id = e1_subj_id.cuda()
e1_subj_w = e1_subj_w.cuda()
e1_verb_id = e1_verb_id.cuda()
e1_verb_w = e1_verb_w.cuda()
e1_obj_id = e1_obj_id.cuda()
e1_obj_w = e1_obj_w.cuda()
e2_subj_id = e2_subj_id.cuda()
e2_subj_w = e2_subj_w.cuda()
e2_verb_id = e2_verb_id.cuda()
e2_verb_w = e2_verb_w.cuda()
e2_obj_id = e2_obj_id.cuda()
e2_obj_w = e2_obj_w.cuda()
e1_emb = model(e1_subj_id, e1_subj_w, e1_verb_id, e1_verb_w, e1_obj_id,
e1_obj_w)
e2_emb = model(e2_subj_id, e2_subj_w, e2_verb_id, e2_verb_w, e2_obj_id,
e2_obj_w)
if option.distance_metric == 'cosine':
distance_func = cosine_distance
elif option.distance_metric == 'euclid':
distance_func = euclid_distance
pred = -distance_func(e1_emb, e2_emb)
if option.use_gpu:
pred = pred.cpu()
pred = pred.detach().numpy()
gold = gold.numpy()
spearman_correlation, spearman_p = scipy.stats.spearmanr(pred, gold)
if option.output_file.strip() != '':
output_file = open(option.output_file, 'w')
for score in pred:
output_file.write(str(score) + '\n')
output_file.close()
logging.info('Output saved to ' + option.output_file)
logging.info('Spearman correlation: %.4f' % (spearman_correlation, ))