def predict(self, sent, asp="null", k=1):
"""
:param sent: processed sentence
:param asp: an aspect mentioned inside sent
:param k: int
:return: top k predictions
"""
wl = self.args.vocab.wl
## set model in eval model
self.model.eval()
fake_label = [0]
words, asp_loc = self.word2idx(sent, asp)
word_ids, sequence_lengths = seqPAD.pad_sequences([words],
pad_tok=0,
wthres=wl)
data_tensors = Data2tensor.sort_tensors(fake_label, [asp_loc],
word_ids, sequence_lengths,
self.device)
fake_label_tensor, aspect_tensor, word_tensor, sequence_lengths, word_seq_recover = data_tensors
arange_tensor = Data2tensor.idx2tensor(
list(range(word_tensor.size(0))), self.device)
label_score = self.model(word_tensor, sequence_lengths, aspect_tensor,
arange_tensor)
label_prob, label_pred = self.model.inference(label_score, k)
return label_prob, label_pred
def evaluate_batch(self, eva_data):
with torch.no_grad():
wl = self.args.vocab.wl
batch_size = self.args.batch_size
## set model in eval model
self.model.eval()
start = time.time()
y_true = Data2tensor.idx2tensor([], self.device)
y_pred = Data2tensor.idx2tensor([], self.device)
for i, (words, label_ids) in enumerate(
self.args.vocab.minibatches(eva_data,
batch_size=batch_size)):
word_ids, sequence_lengths = seqPAD.pad_sequences(words,
pad_tok=0,
wthres=wl)
data_tensors = Data2tensor.sort_tensors(
label_ids, word_ids, sequence_lengths, self.device)
label_tensor, word_tensor, sequence_lengths, word_seq_recover = data_tensors
y_true = torch.cat([y_true, label_tensor])
label_score = self.model(word_tensor, sequence_lengths)
label_prob, label_pred = self.model.inference(label_score, k=1)
y_pred = torch.cat([y_pred, label_pred])
#measures = Classifier.class_metrics(y_true, y_pred.squeeze())
measures = Classifier.class_metrics(
y_true.data.cpu().numpy(),
y_pred.squeeze().data.cpu().numpy())
end = time.time() - start
speed = len(y_true) / end
return measures, speed
def predict(self, doc="", topk=5):
"""
Inputs:
doc: a document
topk: number of recommended tokens
Outputs:
A list form of predicted labels and their probabilities
e,g, [('5_star', 0.2020701915025711),
('3_star', 0.2010505348443985),
('2_star', 0.2006799429655075),
('1_star', 0.1990940123796463),
('4_star', 0.1971053034067154)]
"""
doc_ids = self.word2idx(doc.split())
#######################
# YOUR CODE STARTS HERE
pred_lb, pred_probs = None, None
#convert to tensor
doc_tensor = Data2tensor.idx2tensor(doc_ids)
doc_lengths_tensor = Data2tensor.idx2tensor(len(doc_tensor))
#call the model
output, _, _ = self.model(doc_tensor.unsqueeze(0),
doc_lengths_tensor.unsqueeze(0))
#get the probablities and predicted label
pred_probs, pred_lb = self.model.inference(output, topk)
#applied and to list to get individual element out of tensor
#as a tensor value cannot be compared with the index positon of labels in args.vocab.i2l
pred_probs = pred_probs.flatten().tolist()
pred_lb = pred_lb.flatten().tolist()
#get label information for the predicted output
pred_lb = [self.args.vocab.i2l[x] for x in pred_lb]
# YOUR CODE ENDS HERE
#######################
return list(zip(pred_lb, pred_probs))
def train_batch(self,train_data):
wl = self.args.vocab.wl
clip_rate = self.args.clip
batch_size = self.args.batch_size
num_train = len(train_data)
total_batch = num_train//batch_size+1
prog = Progbar(target=total_batch)
## set model in train model
self.model.train()
train_loss = []
for i,(words, label_ids) in enumerate(self.args.vocab.minibatches(train_data, batch_size=batch_size)):
word_ids, sequence_lengths = seqPAD.pad_sequences(words, pad_tok=0, wthres=wl)
data_tensors = Data2tensor.sort_tensors(label_ids, word_ids,sequence_lengths,self.device)
label_tensor, word_tensor, sequence_lengths, word_seq_recover = data_tensors
self.model.zero_grad()
label_score = self.model(word_tensor, sequence_lengths)
# print("inside training batch, ", label_score.size(), label_tensor.size(), label_score, label_tensor)
batch_loss = self.model.NLL_loss(label_score, label_tensor)
train_loss.append(batch_loss.item())
batch_loss.backward()
if clip_rate>0:
torch.nn.utils.clip_grad_norm_(self.model.parameters(), clip_rate)
self.optimizer.step()
prog.update(i + 1, [("Train loss", batch_loss.item())])
return np.mean(train_loss)
def evaluate_batch(self, eval_data):
start_time = time.time()
eval_batch = self.args.vocab.minibatches(
eval_data, batch_size=self.args.batch_size)
# Turn on evaluation mode which disables dropout.
self.model.eval()
total_loss = 0.
total_word = 0
with torch.no_grad():
for seq_batch in eval_batch:
word_pad_ids, seq_lens = seqPAD.pad_sequences(
seq_batch, pad_tok=self.args.vocab.w2i[PAD])
seq_tensor = Data2tensor.idx2tensor(word_pad_ids, self.device)
hidden = self.model.init_hidden(seq_tensor.size(0))
for i in range(0, seq_tensor.size(1) - 1, self.args.bptt):
data, target = self.bptt_batch(seq_tensor, i)
mask_target = target > 0
output, hidden = self.model(data, hidden)
batch_loss = self.model.NLL_loss(output, target)
total_loss += batch_loss.item()
hidden = self.repackage_hidden(hidden)
total_word = total_word + mask_target.sum().item()
cur_loss = total_loss / total_word
elapsed = time.time() - start_time
print('-' * 89)
print('| EVALUATION | words {:5d} | lr {:02.2f} | words/s {:5.2f} | '
'loss {:5.2f} | ppl {:8.2f}'.format(total_word, self.args.lr,
total_word / elapsed,
cur_loss,
math.exp(cur_loss)))
print('-' * 89)
return cur_loss, total_word, elapsed
def recommend(self, context="", topk=5):
"""
Inputs:
context: the text form of given context
topk: number of recommended tokens
Outputs:
A list form of recommended words and their probabilities
e,g, [('i', 0.044447630643844604),
('it', 0.027285737916827202),
("don't", 0.026111900806427002),
('will', 0.023868300020694733),
('had', 0.02248169668018818)]
"""
rec_wds, rec_probs = [], []
#######################
# YOUR CODE STARTS HERE
#get the data from args.vocab.i2w
data = self.args.vocab.i2w
#get the context data for which the topk values need to be fetched
context = context.split()
#split the context into tokens
#get the index of the words in the context
idx = []
for i in context:
#print(i)
for j in range(0, len(data)):
#if word is present in data then append its index position in idx list
if i == data[j]:
idx.append(j)
#print("index",idx)
#convert the index to the tensor
idx = Data2tensor.idx2tensor(idx)
#print(idx)
prob = 0
label = 0
batch_size = 1
hidden = self.model.init_hidden(batch_size)
#print(hidden)
output, hidden = self.model.forward(idx.reshape(1, -1), hidden)
#print(hidden)
#get the topk words and their probablities
p, l = self.model.inference(output, topk)
prob = list(p[0][-1])
label = list(l[0][-1])
#print(prob,label)
#the below list comprehension
#is used to get words based on the index position in label
#it matches the values stored in label which are index position
#so the values in label are matched wiht values in data
#the word residing at that index position is returned and appended to rec_wds list
rec_wds += [data[k.item()] for k in label]
#the prob list contains tensor so item is used to get a number and not a tensor
#so in below list comprehension p.item() or tensor.item() returns number and not a tensor
rec_probs += [k.item() for k in prob]
# YOUR CODE ENDS HERE
#######################
return list(zip(rec_wds, rec_probs))
def evaluate_batch(self, eval_data):
start_time = time.time()
eval_batch = self.args.vocab.minibatches_with_label(
eval_data, batch_size=self.args.batch_size)
# Turn on evaluation mode which disables dropout.
self.model.eval()
total_loss = 0.
total_docs = 0
y_true, y_pred = [], []
with torch.no_grad():
for doc_batch, lb_batch in eval_batch:
doc_pad_ids, doc_lengths = seqPAD.pad_sequences(
doc_batch, pad_tok=self.args.vocab.w2i[PAD])
#######################
# YOUR CODE STARTS HERE
doc_tensor = Data2tensor.idx2tensor(doc_pad_ids, self.device)
doc_lengths_tensor = Data2tensor.idx2tensor(
doc_lengths, self.device)
lb_tensor = Data2tensor.idx2tensor(lb_batch, self.device)
total_docs += doc_tensor.size(0)
output, _, _ = self.model(doc_tensor, doc_lengths_tensor)
loss = self.model.NLL_loss(output, lb_tensor)
label_prob, label_pred = self.model.inference(output, k=1)
#print("shape label_tensor",lb_tensor.shape)
#print("shape label_pred",label_pred.squeeze(1).shape)
y_true.extend(lb_tensor)
y_pred.extend(label_pred.squeeze(1))
total_loss += loss.item()
# YOUR CODE ENDS HERE
#######################
precision, recall, f1, acc = Sentimentmodel.cal_metrics(y_true, y_pred)
cur_loss = total_loss / total_docs
elapsed = time.time() - start_time
metrics = {
"precision": precision * 100,
"recall": recall * 100,
"f1": f1 * 100,
"acc": acc * 100,
"loss": cur_loss
}
return metrics, total_docs, elapsed
def train_batch(self, train_data):
total_loss = 0.
total_docs = 0
start_time = time.time()
train_batch = self.args.vocab.minibatches_with_label(
train_data, batch_size=self.args.batch_size)
# Turn on training mode which enables dropout.
self.model.train()
for batch, (doc_batch, lb_batch) in enumerate(train_batch):
doc_pad_ids, doc_lengths = seqPAD.pad_sequences(
doc_batch, pad_tok=self.args.vocab.w2i[PAD])
doc_tensor = Data2tensor.idx2tensor(doc_pad_ids, self.device)
doc_lengths_tensor = Data2tensor.idx2tensor(
doc_lengths, self.device)
lb_tensor = Data2tensor.idx2tensor(lb_batch, self.device)
# doc_tensor = [batch_size, max_doc_length]
total_docs += doc_tensor.size(0)
self.model.zero_grad()
output, _, _ = self.model(doc_tensor, doc_lengths_tensor)
loss = self.model.NLL_loss(output, lb_tensor)
avg_loss = loss / doc_tensor.size(0)
avg_loss.backward()
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
torch.nn.utils.clip_grad_norm_(self.model.parameters(),
self.args.clip)
# update parameters in all sub-graphs
self.model_optimizer.step()
# for p in self.model.parameters():
# p.data.add_(p.grad.data, alpha=-self.args.lr)
total_loss += loss.item()
cur_loss = total_loss / total_docs
elapsed = time.time() - start_time
# print('-' * 89)
# print('| TRAINING | epoch {:3d} | documents {:5d} | lr {:02.2f} | documents/s {:5.2f} | '
# 'loss {:5.2f}'.format(epoch, total_docs, self.args.lr, total_docs / elapsed, cur_loss))
# print('-' * 89)
return cur_loss, total_docs, elapsed
def train_batch(self, train_data):
wl = self.args.vocab.wl
cl = self.args.vocab.cl
clip_rate = self.args.clip
batch_size = self.args.batch_size
num_train = len(train_data)
total_batch = num_train // batch_size + 1
prog = Progbar(target=total_batch)
## set model in train model
self.model.train()
train_loss = []
for i, (words, label_ids) in enumerate(
self.args.vocab.minibatches(train_data,
batch_size=batch_size)):
char_ids, word_ids = zip(*words)
word_ids, sequence_lengths = seqPAD.pad_sequences(word_ids,
pad_tok=0,
wthres=wl,
cthres=cl)
char_ids, word_lengths = seqPAD.pad_sequences(char_ids,
pad_tok=0,
nlevels=2,
wthres=wl,
cthres=cl)
label_ids, _ = seqPAD.pad_sequences(label_ids,
pad_tok=0,
wthres=wl,
cthres=cl)
data_tensors = Data2tensor.sort_tensors(label_ids, word_ids,
sequence_lengths, char_ids,
word_lengths)
label_tensor, word_tensor, sequence_lengths, word_seq_recover, char_tensor, word_lengths, char_seq_recover = data_tensors
mask_tensor = word_tensor > 0
label_score = self.model(word_tensor, sequence_lengths,
char_tensor, word_lengths,
char_seq_recover)
batch_loss = self.model.NLL_loss(label_score, mask_tensor,
label_tensor)
train_loss.append(batch_loss.data.tolist()[0])
self.model.zero_grad()
batch_loss.backward()
if clip_rate > 0:
torch.nn.utils.clip_grad_norm(self.model.parameters(),
clip_rate)
self.optimizer.step()
prog.update(i + 1, [("Train loss", batch_loss.data.tolist()[0])])
return np.mean(train_loss)
def predict_null(classifier, sent, asp, i2l):
from utils.data_utils import Data2tensor, seqPAD
wl = classifier.args.vocab.wl
## set model in eval model
classifier.model.eval()
fake_label = [0]
words, asp_loc = classifier.word2idx(sent, asp)
word_ids, sequence_lengths = seqPAD.pad_sequences([words],
pad_tok=0,
wthres=wl)
data_tensors = Data2tensor.sort_tensors(fake_label, [asp_loc], word_ids,
sequence_lengths,
classifier.device)
fake_label_tensor, aspect_tensor, word_tensor, sequence_lengths, word_seq_recover = data_tensors
arange_tensor = Data2tensor.idx2tensor(list(range(word_tensor.size(0))),
classifier.device)
word_h_n = classifier.model.rnn.get_all_hiddens(word_tensor,
sequence_lengths).mean(1)
label_score = classifier.model.hidden2tag(word_h_n)
label_score = classifier.model.dropfinal(label_score)
label_prob, label_pred = classifier.model.inference(label_score, len(i2l))
return label_prob, label_pred
def predict(self, sent, k=1):
cl = self.args.vocab.cl
## set model in eval model
self.model.eval()
fake_label = [0]
words = self.word2idx(sent)
word_ids, sequence_lengths = seqPAD.pad_sequences([words], pad_tok=0, wthres=cl)
data_tensors = Data2tensor.sort_tensors(fake_label, word_ids,sequence_lengths, volatile_flag=True)
fake_label_tensor, word_tensor, sequence_lengths, word_seq_recover = data_tensors
label_score = self.model(word_tensor, sequence_lengths)
label_prob, label_pred = self.model.inference(label_score, k)
return label_prob, label_pred
def evaluate_batch(self, eva_data):
with torch.no_grad():
wl = self.args.vocab.wl
batch_size = self.args.batch_size
## set model in eval model
self.model.eval()
start = time.time()
y_true = Data2tensor.idx2tensor([], self.device)
y_pred = Data2tensor.idx2tensor([], self.device)
for i, (words, asp_locs, label_ids) in enumerate(
self.args.vocab.minibatches(eva_data,
batch_size=batch_size)):
word_ids, sequence_lengths = seqPAD.pad_sequences(words,
pad_tok=0,
wthres=wl)
data_tensors = Data2tensor.sort_tensors(
label_ids, asp_locs, word_ids, sequence_lengths,
self.device)
label_tensor, aspect_tensor, word_tensor, sequence_lengths, word_seq_recover = data_tensors
arange_tensor = Data2tensor.idx2tensor(
list(range(word_tensor.size(0))), self.device)
y_true = torch.cat([y_true, label_tensor])
label_score = self.model(word_tensor, sequence_lengths,
aspect_tensor, arange_tensor)
label_prob, label_pred = self.model.inference(label_score, k=1)
y_pred = torch.cat([y_pred, label_pred])
# acc = metrics.accuracy_score(y_true, y_pred)
# print(y_pred.size())
# print(y_true.size())
measures = Classifier.class_metrics(y_true, y_pred.squeeze())
end = time.time() - start
speed = len(y_true) / end
# print("Gradient flag: ", label_score.requires_grad)
return measures, speed
def train_batch(self, train_data, epoch=0):
total_loss = 0.
total_word = 0
total_seq = 0
start_time = time.time()
train_batch = self.args.vocab.minibatches(
train_data, batch_size=self.args.batch_size)
# Turn on training mode which enables dropout.
self.model.train()
for batch, seq_batch in enumerate(train_batch):
word_pad_ids, seq_lens = seqPAD.pad_sequences(
seq_batch, pad_tok=self.args.vocab.w2i[PAD])
seq_tensor = Data2tensor.idx2tensor(word_pad_ids, self.device)
# seq_tensor = [batch_size, seq_len]
total_seq += seq_tensor.size(0)
hidden = self.model.init_hidden(seq_tensor.size(0))
for i in range(0, seq_tensor.size(1) - 1, self.args.bptt):
# data = [batch_size, bptt]
# target = [batch_size, bptt]
data, target = self.bptt_batch(seq_tensor, i)
mask_target = target > 0
# Starting each batch, we detach the hidden state from how it was previously produced.
# If we didn't, the model would try backpropagating all the way to start of the dataset.
hidden = self.repackage_hidden(hidden)
self.model.zero_grad()
output, hidden = self.model(data, hidden)
loss = self.model.NLL_loss(output, target)
loss.backward()
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
torch.nn.utils.clip_grad_norm_(self.model.parameters(),
self.args.clip)
for p in self.model.parameters():
p.data.add_(-self.args.lr, p.grad.data)
total_loss += loss.item()
total_word = total_word + mask_target.sum().item()
cur_loss = total_loss / total_word
elapsed = time.time() - start_time
print('-' * 89)
print(
'| TRAINING | epoch {:3d} | batch {:5d} | sequences {:5d} | words {:5d} | lr {:02.2f} | '
'words/s {:5.2f} | loss {:5.2f} | ppl {:8.2f}'.format(
epoch, batch + 1, total_seq, total_word, self.args.lr,
total_word / elapsed, cur_loss, math.exp(cur_loss)))
print('-' * 89)
def wd_pred(model, vocab, sentence):
""" Predict next word
"""
with torch.no_grad():
words = sentence.split(' ')
for i, word in enumerate(words):
# transform word to tensor
word_idx = vocab.w2i[word]
word_tensor = Data2tensor.idx2tensor([[word_idx]])
if i == 0:
hidden = model.init_hidden(word_tensor.size(0))
output, hidden = model(word_tensor, hidden)
label_prob, label_pred = model.inference(output)
word_idx = label_pred.data[0][0].data.numpy()[0]
return vocab.i2w[word_idx]
def rev_gen(model, vocab, start_word=SOS):
""" Generate a review starts with 'start_word', ends with '</s>'
"""
print('Generating sample review .....................')
with torch.no_grad():
word_idx = vocab.w2i[start_word]
all_words = []
all_words.append(start_word)
while word_idx != vocab.w2i[EOS]:
word_tensor = Data2tensor.idx2tensor([[word_idx]])
hidden = model.init_hidden(word_tensor.size(0))
output, hidden = model(word_tensor, hidden)
label_prob, label_pred = model.inference(output)
word_idx = label_pred.data[0][0].data.numpy()[0]
all_words.append(vocab.i2w[word_idx])
return ' '.join(all_words)
def scoring(sent, args, classifier):
cl = args.vocab.cl
## set model in eval model
classifier.model.eval()
fake_label = [0]
words = classifier.word2idx(sent)
word_ids, sequence_lengths = seqPAD.pad_sequences([words],
pad_tok=0,
wthres=cl)
data_tensors = Data2tensor.sort_tensors(fake_label,
word_ids,
sequence_lengths,
volatile_flag=True)
fake_label_tensor, word_tensor, sequence_lengths, word_seq_recover = data_tensors
label_score = classifier.model(word_tensor, sequence_lengths)
# label_prob, label_pred = classifier.model.inference(label_score)
return label_score
def predict_null(self, sent, asp):
wl = self.classifier.args.vocab.wl
## set model in eval model
self.classifier.model.eval()
fake_label = [0]
words, asp_loc = self.classifier.word2idx(sent, asp)
word_ids, sequence_lengths = seqPAD.pad_sequences([words],
pad_tok=0,
wthres=wl)
data_tensors = Data2tensor.sort_tensors(fake_label, [asp_loc],
word_ids, sequence_lengths,
self.classifier.device)
fake_label_tensor, aspect_tensor, word_tensor, sequence_lengths, word_seq_recover = data_tensors
word_h_n = self.classifier.model.rnn.get_all_hiddens(
word_tensor, sequence_lengths).mean(1)
label_score = self.classifier.model.hidden2tag(word_h_n)
label_score = self.classifier.model.dropfinal(label_score)
label_prob, label_pred = self.classifier.model.inference(
label_score, len(self.i2l))
return label_prob, label_pred
def predict(self, sent):
numtags = len(self.args.vocab.l2i)
wl = self.args.vocab.wl
cl = self.args.vocab.cl
## set model in eval model
self.model.eval()
words = self.word2idx(sent)
char_ids, word_ids = zip(*words)
fake_label = [[0] * len(word_ids)]
char_ids, word_ids = zip(*words)
word_ids, sequence_lengths = seqPAD.pad_sequences([word_ids],
pad_tok=0,
wthres=wl,
cthres=cl)
char_ids, word_lengths = seqPAD.pad_sequences([char_ids],
pad_tok=0,
nlevels=2,
wthres=wl,
cthres=cl)
data_tensors = Data2tensor.sort_tensors(fake_label,
word_ids,
sequence_lengths,
char_ids,
word_lengths,
volatile_flag=True)
fake_label_tensor, word_tensor, sequence_lengths, word_seq_recover, char_tensor, word_lengths, char_seq_recover = data_tensors
label_score = self.model(word_tensor, sequence_lengths, char_tensor,
word_lengths, char_seq_recover)
if numtags > 2:
label_prob, label_pred = label_score.data.max(1)
else:
label_prob = F.sigmoid(label_score.squeeze())
label_pred = (label_prob >= 0.5).data.long()
return label_prob, label_pred
def evaluate_batch(self, eva_data):
cl = self.args.vocab.cl
batch_size = self.args.batch_size
## set model in eval model
self.model.eval()
num_label = 0
num_correct = 0
for i,(words, label_ids) in enumerate(self.args.vocab.minibatches(eva_data, batch_size=batch_size)):
word_ids, sequence_lengths = seqPAD.pad_sequences(words, pad_tok=0, wthres=cl)
data_tensors = Data2tensor.sort_tensors(label_ids, word_ids,sequence_lengths, volatile_flag=True)
label_tensor, word_tensor, sequence_lengths, word_seq_recover = data_tensors
label_score = self.model(word_tensor, sequence_lengths)
label_prob, label_pred = self.model.inference(label_score, k=1)
assert len(label_pred)==len(label_tensor)
correct_pred = (label_pred.squeeze()==label_tensor.data).sum()
assert correct_pred <=batch_size
num_label += len(label_tensor)
num_correct += correct_pred
acc = num_correct/num_label
return acc
def evaluate_batch(self, eva_data):
wl = self.args.vocab.wl
cl = self.args.vocab.cl
batch_size = self.args.batch_size
## set model in eval model
self.model.eval()
correct_preds = 0.
total_preds = 0.
total_correct = 0.
accs = []
pred_results = []
gold_results = []
for i, (words, label_ids) in enumerate(
self.args.vocab.minibatches(eva_data, batch_size=batch_size)):
char_ids, word_ids = zip(*words)
word_ids, sequence_lengths = seqPAD.pad_sequences(word_ids,
pad_tok=0,
wthres=wl,
cthres=cl)
char_ids, word_lengths = seqPAD.pad_sequences(char_ids,
pad_tok=0,
nlevels=2,
wthres=wl,
cthres=cl)
label_ids, _ = seqPAD.pad_sequences(label_ids,
pad_tok=0,
wthres=wl,
cthres=cl)
data_tensors = Data2tensor.sort_tensors(label_ids,
word_ids,
sequence_lengths,
char_ids,
word_lengths,
volatile_flag=True)
label_tensor, word_tensor, sequence_lengths, word_seq_recover, char_tensor, word_lengths, char_seq_recover = data_tensors
mask_tensor = word_tensor > 0
label_score = self.model(word_tensor, sequence_lengths,
char_tensor, word_lengths,
char_seq_recover)
label_prob, label_pred = self.model.inference(
label_score, mask_tensor)
pred_label, gold_label = recover_label(label_pred, label_tensor,
mask_tensor,
self.args.vocab.l2i,
word_seq_recover)
pred_results += pred_label
gold_results += gold_label
acc, p, r, f = get_ner_fmeasure(gold_results, pred_results)
# label_pred = label_pred.cpu().data.numpy()
# label_tensor = label_tensor.cpu().data.numpy()
# sequence_lengths = sequence_lengths.cpu().data.numpy()
#
# for lab, lab_pred, length in zip(label_tensor, label_pred, sequence_lengths):
# lab = lab[:length]
# lab_pred = lab_pred[:length]
# accs += [a==b for (a, b) in zip(lab, lab_pred)]
#
# lab_chunks = set(NERchunks.get_chunks(lab, self.args.vocab.l2i))
# lab_pred_chunks = set(NERchunks.get_chunks(lab_pred, self.args.vocab.l2i))
#
# correct_preds += len(lab_chunks & lab_pred_chunks)
# total_preds += len(lab_pred_chunks)
# total_correct += len(lab_chunks)
#
# p = correct_preds / total_preds if correct_preds > 0 else 0
# r = correct_preds / total_correct if correct_preds > 0 else 0
# f = 2 * p * r / (p + r) if correct_preds > 0 else 0
# acc = np.mean(accs)
return acc, f
# coding: utf-8
import argparse
import time
import math
import torch
import os
import torch.onnx
import torch.optim as optim
from sklearn import metrics
from utils.data_utils import Vocab, Txtfile, Data2tensor, SaveloadHP, seqPAD, PAD
from utils.core_nns_emb import UniLSTMModel, BiLSTMModel
# Set the random seed manually for reproducibility.
Data2tensor.set_randseed(1234)
class Sentimentmodel(object):
def __init__(self, args):
self.args = args
self.device = torch.device("cuda" if self.args.use_cuda else "cpu")
self.word2idx = self.args.vocab.wd2idx(self.args.vocab.w2i,
allow_unk=self.args.allow_unk,
start_end=self.args.se_words)
self.label2idx = self.args.vocab.tag2idx(self.args.vocab.l2i)
self.ntokens = len(self.args.vocab.w2i)
self.nlabels = len(self.args.vocab.l2i)
if args.bidirect:
self.model = BiLSTMModel(args.model,
self.ntokens,
args.emb_size,
args.hidden_size,
