def load_train_data(data_path, batch_size, max_src_len, max_trg_len, use_cuda=False):
# Note: sequential=False, use_vocab=False, since we use preprocessed inputs.
src_field = Field(sequential=True, use_vocab=False, include_lengths=True, batch_first=True,
pad_token=PAD, unk_token=UNK, init_token=None, eos_token=None,)
trg_field = Field(sequential=True, use_vocab=False, include_lengths=True, batch_first=True,
pad_token=PAD, unk_token=UNK, init_token=BOS, eos_token=EOS,)
fields = (src_field, trg_field)
device = None if use_cuda else -1
def filter_pred(example):
if len(example.src) <= max_src_len and len(example.trg) <= max_trg_len:
return True
return False
dataset = torch.load(data_path)
train_src, train_tgt = dataset['train_src'], dataset['train_tgt']
dev_src, dev_tgt = dataset['dev_src'], dataset['dev_tgt']
train_data = ParallelDataset(train_src, train_tgt, fields=fields, filter_pred=filter_pred,)
train_iter = Iterator(dataset=train_data, batch_size=batch_size, train=True, # Variable(volatile=False)
sort_key=lambda x: data.interleave_keys(len(x.src), len(x.trg)),
repeat=False, shuffle=True, device=device)
dev_data = ParallelDataset(dev_src, dev_tgt, fields=fields,)
dev_iter = Iterator(dataset=dev_data, batch_size=batch_size, train=False, # Variable(volatile=True)
repeat=False, device=device, shuffle=False, sort=False,)
return src_field, trg_field, train_iter, dev_iter
def load_dataset(data_path, train_batch_size=4096, dev_batch_size=1, max_len=100):
"""
This assumes that the data is already pre-processed using Moses Tokenizer
Returns iterators for the training/dev dataset
Arguments:
data_path: path of the dataset
train_batch_size: batch size of the training data (defined in terms of number of tokens or sentences, depending on the model_type)
dev_batch_size: batch size of the dev data (usually one)
max_len: max length of sequeences in a batch
"""
SRC = Field(tokenize=lambda s: s.split(), init_token="<s>",
eos_token="</s>", batch_first=True, include_lengths=True)
TRG = Field(tokenize=lambda s: s.split(), init_token="<s>",
eos_token="</s>", batch_first=True, include_lengths=True)
# create a TranslationDataset for both the train and dev set
train_data = datasets.TranslationDataset(exts=("train.de", "train.en"), fields=(
SRC, TRG), path=data_path, filter_pred=lambda x: len(vars(x)['src']) <= max_len and len(vars(x)['trg']) <= max_len)
dev_data = datasets.TranslationDataset(
exts=("dev.de", "dev.en"), fields=(SRC, TRG), path=data_path)
# load in the Test Set
test_examples = []
with open(data_path + "test.de", "r") as f:
for test_example in f.readlines():
example = data.Example()
setattr(example, "src", test_example.split())
test_examples.append(example)
test_data = data.Dataset(test_examples, fields=[("src", SRC)])
# build he vocab using the training data
SRC.build_vocab(train_data.src, train_data.trg)
TRG.build_vocab(train_data.src, train_data.trg)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# use custom DataIterator in order to minimize padding in a sequence
# and inoder to `pack` a batch fully inorder to maximmize the computation
# in a GPU
train_iterator = DataIterator(train_data, batch_size=train_batch_size, device=device,
repeat=False, sort_key=lambda x: (len(x.src), len(x.trg)),
batch_size_fn=batch_size_fn, train=True, sort_within_batch=True, shuffle=True)
# use a regular Iterator since we want to be able to compare
# our translations to a gold standard file. If we use a
# `DataIterator` then we will get our translations in shuffled/random
# order
dev_iterator = Iterator(dev_data, batch_size=dev_batch_size,
train=False, sort=False, repeat=False, device=device)
# create Test Iterator for the test data
test_iterator = Iterator(
test_data, batch_size=1, train=False, sort=False, repeat=False, device=device)
print(len(test_iterator))
return train_iterator, dev_iterator, test_iterator, SRC, TRG
def tokenize(self):
ENGLISH = Field(sequential=True,
use_vocab=True,
tokenize=str.split,
lower=True,
init_token="<sos>",
eos_token="<eos>")
FRENCH = Field(sequential=True,
use_vocab=True,
tokenize=str.split,
lower=True,
init_token="<sos>",
eos_token="<eos>")
"""
in order for this to work, change
"csv.field_size_limit(sys.maxsize)" in torchtext/utils.py to "csv.field_size_limit(maxInt)"
"""
train, test = TabularDataset.splits(path='./data/', train='train.csv', test='test.csv',
format='csv', fields=[('en',ENGLISH),('fr',FRENCH)])
ENGLISH.build_vocab(train, test)
FRENCH.build_vocab(train, test)
self.en_vocab = ENGLISH
self.fr_vocab = FRENCH
self.en_vocabsize = len(ENGLISH.vocab)
self.fr_vocabsize = len(FRENCH.vocab)
if self.config.debug :
train_loader, test_loader = Iterator.splits((train, test), batch_size=2, device="cuda", shuffle=False,
sort_key=lambda x : len(x.en), sort_within_batch=False)
else :
train_loader, test_loader = Iterator.splits((train, test), batch_size=self.config.batchsize, device="cuda", shuffle=False,
sort_key=lambda x : len(x.en), sort_within_batch=False)
return train_loader, test_loader
def dataset2iter(workpath=WORK_PATH,
train_path=FILE_TRAIN,
validation_path=FILE_VALID,
test_path=FILE_TEST):
fields = [('sentence', SENTENCE), ('wxx', LABEL), ('char', CHAR)]
######## 当你的数据行以 " 开头时，csv.reader 会认为此行包含分隔符，请额外传入以下参数。
######## torchtext相关文档： https://pytorch.org/text/stable/data.html#torchtext.data.TabularDataset.__init__
######## csv库相关文档： https://docs.python.org/3/library/csv.html#dialects-and-formatting-parameters
csv_reader_params = {
'doublequote': False,
'quoting': csv.QUOTE_NONE,
}
data_train = TabularDataset(workpath + train_path,
format="tsv",
fields=fields,
skip_header=True,
csv_reader_params=csv_reader_params)
data_valid = TabularDataset(workpath + validation_path,
format="tsv",
fields=fields,
skip_header=True,
csv_reader_params=csv_reader_params)
data_test = TabularDataset(workpath + test_path,
format="tsv",
fields=fields,
skip_header=True,
csv_reader_params=csv_reader_params)
pretrained_vectors = Vectors(name=GLOVE_PATH + TRAINED_VECTORS + '.txt',
cache=GLOVE_PATH)
SENTENCE.build_vocab(
data_train,
vectors=pretrained_vectors,
unk_init=lambda x: torch.nn.init.uniform_(x, a=-0.25, b=0.25))
LABEL.build_vocab(data_train)
CHAR.build_vocab(data_train)
# debug
#return data_train,data_valid,data_test
iter_train = Iterator(data_train,
batch_size=BATCH_SIZE,
train=True,
sort_key=lambda x: len(x.sentence),
shuffle=True,
device=DEVICE)
iter_valid = Iterator(data_valid,
batch_size=BATCH_SIZE,
train=False,
sort=False,
shuffle=True,
device=DEVICE)
iter_test = Iterator(data_test,
batch_size=BATCH_SIZE,
train=False,
sort=False,
shuffle=True,
device=DEVICE)
return iter_train, iter_valid, iter_test
def data_split(text_field, label_field, dataset, mode=False):
if mode == 'init':
for index, c in enumerate(dataset):
partial = NLPDataLoader(c,
text_field=text_field,
label_field=label_field,
test=False)
if index == 0:
text_field.build_vocab(partial)
label_field.build_vocab(list(range(13)))
else:
text_counter = text_field.vocab.freqs
for example in partial.examples:
text_counter.update(example.text)
text_field.vocab = text_field.vocab_cls(
text_counter, specials=['<unk>', '<pad>'])
return
elif mode is False:
dataset = NLPDataLoader(dataset,
text_field=text_field,
label_field=label_field,
test=False)
return Iterator.splits((dataset, ), batch_size=20)
elif mode is True:
dataset = NLPDataLoader(dataset,
text_field=text_field,
label_field=label_field,
test=True)
return Iterator.splits((dataset, ), batch_size=20, shuffle=False)
def fit(self, filepath, train_dev_ratio=0.8, batch_size=64, nepoch=10):
""" Feed training data to train the model
Args:
filepath: a string, the path of dataset
train_dev_ratio: a float, the ratio to split train and dev dataset
batch_size: a integer, the size of batch when training
nepoch: a integer, the number of training epochs
TODO:
1) support early stopping
2) support customized delimiter
3) support callback function
4) support fit_generator
"""
train, dev = self._process_data(filepath, train_dev_ratio)
self.text_field.build_vocab(train, vectors="glove.6B.50d")
self.label_field.build_vocab(train)
self._build_network()
train_iter = Iterator(train, batch_size=batch_size, shuffle=True)
dev_iter = Iterator(dev, batch_size=batch_size, shuffle=True)
optimizer = torch.optim.Adam(self.network.parameters(), lr=self.lr)
loss_fn = nn.BCELoss()
self.network.train()
best_acc = 0
for epoch in range(1, nepoch + 1):
for i, batch in enumerate(train_iter):
feature, target = batch.text, batch.label
target = target.type(torch.FloatTensor)
feature.data.t_(), target.data.sub_(1)
optimizer.zero_grad()
y_pred = self.network(feature).reshape(-1)
loss = loss_fn(y_pred, target)
loss.backward()
optimizer.step()
label_pred = (np.array(y_pred.data) > 0.5).astype(int)
label_true = np.array(target)
train_acc = accuracy_score(label_true, label_pred)
output_str = '\rEpoch:{} batch:{} loss:{:.6f} acc:{:.2f}'
sys.stdout.write(
output_str.format(epoch, i, loss.item(), train_acc))
dev_acc, dev_p, dev_r, dev_f1 = self.evaluate(dev_iter)
if dev_acc > best_acc:
best_acc = dev_acc
print('Saving best model:')
output_str = '\nBest - acc:{:.2f} p:{:.2f} r:{:.2f} f1:{:.2f} \n \n'
print(output_str.format(dev_acc, dev_p, dev_r, dev_f1))
self._save_weights(self.network)
return
def __init__(self, module_name, train_bs, eval_bs, device, log):
self.module_name = module_name
# split_chars = lambda x: list("".join(x.split()))
split_chars = lambda x: list(x) # keeps whitespaces
source = Field(tokenize=split_chars,
init_token='<sos>',
eos_token='<eos>',
batch_first=True)
target = Field(tokenize=split_chars,
init_token='<sos>',
eos_token='<eos>',
batch_first=True)
log("Loading FULL datasets ...")
folder = os.path.join(DATASET_TARGET_DIR, module_name)
train_dataset, eval_dataset, _ = TranslationDataset.splits(
path=folder,
root=folder,
exts=(INPUTS_FILE_ENDING, TARGETS_FILE_ENDING),
fields=(source, target),
train=TRAIN_FILE_NAME,
validation=EVAL_FILE_NAME,
test=EVAL_FILE_NAME)
log("Building vocab ...")
source.build_vocab(train_dataset)
target.vocab = source.vocab
log("Creating iterators ...")
train_iterator = Iterator(dataset=train_dataset,
batch_size=train_bs,
train=True,
repeat=True,
shuffle=True,
device=device)
eval_iterator = Iterator(dataset=eval_dataset,
batch_size=eval_bs,
train=False,
repeat=False,
shuffle=False,
device=device)
self.train_dataset = train_dataset
self.eval_dataset = eval_dataset
self.train_iterator = train_iterator
self.eval_iterator = eval_iterator
self.source = source
self.target = target
def create_data_iterator(batch_size, tabular_train, tabular_valid, d):
# Create the Iterator for datasets (Iterator works like dataloader)
train_iter = Iterator(tabular_train,
batch_size=batch_size,
device=d,
sort_within_batch=False,
repeat=False)
valid_iter = Iterator(tabular_valid,
batch_size=batch_size,
device=d,
sort_within_batch=False,
repeat=False)
return train_iter, valid_iter
def get_dataloader(self,
proc_id=0,
n_gpus=1,
device=torch.device('cpu'),
batch_size=64):
def _distribute_dataset(dataset):
n = len(dataset)
part = dataset[n * proc_id // n_gpus:n * (proc_id + 1) // n_gpus]
return torchtext.data.Dataset(part, dataset.fields)
train_ds = _distribute_dataset(self.train_ds)
self.verbose = self.verbose and (proc_id == 0)
train_iter, valid_iter = BucketIterator.splits(
(train_ds, self.valid_ds),
batch_sizes=(batch_size, batch_size),
sort_within_batch=True,
sort_key=lambda x: len(x.input),
device=device,
repeat=False,
)
test_iter = Iterator(
self.test_ds,
batch_size=1,
sort=False,
sort_within_batch=False,
device=device,
repeat=False,
)
train_dl = BatchWrapper(train_iter)
valid_dl = BatchWrapper(valid_iter)
test_dl = BatchWrapper(test_iter)
return train_dl, valid_dl, test_dl
def build_prediction_iterator(self, df):
dataset = base.CommentsDataset(df, self.fields)
pred_id = list(df['id'].values)
pred_iter = Iterator(
dataset, batch_size=self.params['batch_size'],
repeat=False, shuffle=False, sort=False)
return pred_id, pred_iter
def eval(self, tasknum, total:int, trainloss:float=0, epoch:int=0):
iter = Iterator(self.task[tasknum].te_dataset, batch_size=self.batch_size, repeat=False,
sort_key=lambda x: len(x.syllable_contents), train=False,
device=self.device)
tq_iter = tqdm(enumerate(iter), total=math.ceil(total / self.batch_size),
unit_scale=self.batch_size, bar_format='{r_bar}')
pred_lst = list()
truth_lst = list()
acc_lst = list()
label_lst = list()
self.model.eval()
for i, batch in tq_iter:
preds = self.model(batch.syllable_contents, tasknum)
label = torch.tensor(batch.label, dtype=torch.long, device=self.device)
if(self.isbinary):
accs = torch.eq(preds > 0.5, batch.label > 0.5).to(torch.float)
else:
accs = torch.eq(torch.argmax(preds, dim=1), label).to(torch.long)
label_lst += label.tolist()
acc_lst += accs.tolist()
pred_lst += preds.tolist()
prec,recall,f1score,f1s,rocauc = self.getscore(pred_lst,label_lst)
accuracy = sum(acc_lst)/total
self.log_to_c3dl(json.dumps(
{'type': 'test', 'epoch': epoch, 'accuracy': accuracy, 'precision': prec, 'recall': recall, 'f1score': f1score, 'ROC-AUC':rocauc}))
wandb.log({'Epoch':epoch,'Accuracy':accuracy,'Precision':prec,'Recall':recall,'F1Score':f1score, 'Trainloss':trainloss, 'ROC-AUC':rocauc})
if(len(f1s)!=0):
for i in range(len(f1s)):
wandb.log({f'Class {i} F1Score':f1s[i]})
return acc_lst, total, prec, recall, f1score, f1s, rocauc
def predict_and_save(dataset=None,
model=None,
dataset_path='dev.conll',
out_path='predict.conll',
**kwargs):
"""Combine original CONLL-X file with predictions.
This is required since the iterator might have changed certain fields
(e.g. lowercasing).
We read the dataset_path separately and replace the fields we predicted.
"""
device = torch.device(type='cuda') if use_cuda else torch.device(
type='cpu')
data_iter = Iterator(dataset,
1,
train=False,
sort=False,
shuffle=False,
device=device)
start_time = time.time()
i2pos = dataset.fields['pos'].vocab.itos
with open(out_path, mode='w', encoding='utf-8') as f:
with open(dataset_path, mode='r', encoding='utf-8') as data_f:
with torch.no_grad():
if "ud" in dataset_path:
original_iter = read_conllu(data_f)
ud = True
i2upos = dataset.fields['upos'].vocab.itos
else:
original_iter = read_conllx(data_f)
ud = False
for pred in predict(data_iter=data_iter, model=model):
tokens = next(original_iter)
pred_tags = [-1] * len(tokens)
pred_utags = [-1] * len(tokens)
write_tag, write_utag = False, False
if len(pred["pos"]) > 0:
pred_tags = pred["pos"].data.view(-1).tolist()
write_tag = True
if len(pred["upos"]) > 0:
pred_utags = pred["upos"].data.view(-1).tolist()
write_utag = True
for tok, pred_tag, pred_utag in \
zip(tokens, pred_tags, pred_utags):
if write_tag:
if ud:
tok.xpos = i2pos[pred_tag]
else:
tok.pos = i2pos[pred_tag]
if write_utag:
tok.upos = i2upos[pred_utag]
f.write(str(tok) + '\n')
f.write('\n')
def get_iterator(dataset, device, batch_size, shuffle=True, repeat=False):
train, val, test = dataset
train_iter, val_iter = BucketIterator.splits(
(train, val),
batch_size=batch_size,
device=device,
sort_key=lambda x: len(x.comment_text),
sort_within_batch=False,
shuffle=shuffle,
repeat=repeat)
test_iter = Iterator(test,
batch_size=batch_size,
device=device,
sort_within_batch=False,
repeat=repeat,
sort=False)
train_dl = BatchWrapper(train_iter, "comment_text", [
"toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"
])
valid_dl = BatchWrapper(val_iter, "comment_text", [
"toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"
])
test_dl = BatchWrapper(test_iter, "comment_text", None)
return train_dl, valid_dl, test_dl
def test_text_cnn(config):
os.environ['CUDA_VISIBLE_DEVICES'] = str(config['gpu'])
base_path = config['base_path']
save_path = os.path.join(base_path, 'text_cnn.pkl')
vocab_path = os.path.join(base_path, 'vocab.pkl')
TEXT = data.Field(sequential=True, lower=True, batch_first=True)
LABEL = data.Field(sequential=False, use_vocab=False, batch_first=True)
fields = [
('sentence', TEXT),
('label', LABEL)
]
test_data = TabularDataset(path=os.path.join(base_path, 'test.tsv'),
format='tsv', skip_header=True, fields=fields)
with open(vocab_path, 'rb') as handle:
vocab = pickle.load(handle)
TEXT.vocab = vocab
device = torch.device('cuda:0')
test_iter = Iterator(test_data, batch_size=config['batch_size'], shuffle=False, device=device)
model = torch.load(save_path)
test_accuracy = eval_text_cnn(model, test_iter)
print('test_accuracy: %.4f' % test_accuracy)
def evaluate(self, dev_data):
""" evaluate the dev dataset
Args:
dev_data: torchtext.data.Iterator or torchtext.data.Dataset
Returns:
a tuple of (accuracy, precision, recall, f1_score)
"""
if isinstance(dev_data, Iterator):
dev_iter = dev_data
else:
dev_iter = Iterator(dev_data, batch_size=32)
self.network.eval()
label_pred, label_true = [], []
for batch in dev_iter:
feature, target = batch.text, batch.label
target = target.type(torch.FloatTensor)
# since the label is {unk:0, 0: 1, 1: 2}, need subtrct 1
feature.data.t_(), target.data.sub_(1)
y_pred = self.network(feature)
y_pred = y_pred.reshape(-1)
label_pred += list((np.array(y_pred.data) > 0.5).astype(int))
label_true += list(np.array(target))
acc = accuracy_score(label_true, label_pred)
p = precision_score(label_true, label_pred)
r = recall_score(label_true, label_pred)
f1 = f1_score(label_true, label_pred)
output_str = '\nEval - acc:{:.2f} p:{:.2f} r:{:.2f} f1:{:.2f} \n'
print(output_str.format(acc, p, r, f1))
return acc, p, r, f1
def __init__(self, batch_size):
self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
self.pad_id = self.tokenizer._convert_token_to_id("[PAD]")
self.batch_size = batch_size
# Objects in which the data will be stored.
self.text = Field(sequential=True,
lower=True,
tokenize=self.tokenizer.tokenize,
batch_first=True,
pad_token='[PAD]',
unk_token='[UNK]')
self.labels = Field(sequential=False, is_target=True)
self.train, self.dev, self.test = MultiNLI.splits(
self.text, self.labels)
# Builds vocabulary for the data.
self.text.build_vocab(self.train, self.dev, self.test)
self.labels.build_vocab(self.train)
self.train_size = len(self.train)
self.val_size = len(self.dev)
self.test_size = len(self.test)
self.name = 'mnli'
# Standard torchtext iterators, these do not return input suitable for BERT.
self.train_iter, self.dev_iter, self.test_iter = Iterator.splits(
(self.train, self.dev, self.test),
batch_size=self.batch_size,
device=torch.device(
'cuda:0' if torch.cuda.is_available() else 'cpu'))
def __init__(self, root_dir='data', batch_size=64, use_vector=True):
self.TEXT = Field(sequential=True, use_vocab=True,
tokenize='spacy', lower=True, batch_first=True)
self.LABEL = LabelField(tensor_type=torch.FloatTensor)
vectors = Vectors(name='mr_vocab.txt', cache='./')
dataset_path = os.path.join(root_dir, '{}.tsv')
self.dataset = {}
self.dataloader = {}
for target in ['train', 'dev', 'test']:
self.dataset[target] = TabularDataset(
path=dataset_path.format(target),
format='tsv',
fields=[('text', self.TEXT), ('label', self.LABEL)]
)
if use_vector:
self.TEXT.build_vocab(self.dataset[target], max_size=25000, vectors=vectors)
else:
self.TEXT.build_vocab(self.dataset[target], max_size=25000)
self.LABEL.build_vocab(self.dataset[target])
self.dataloader[target] = Iterator(self.dataset[target],
batch_size=batch_size,
device=None,
repeat=False,
sort_key=lambda x: len(x.text),
shuffle=True)
def classify_from_strings(self, strings: Union[List[str], str]) -> list:
"""
method that can be used for classifying one or multiple examples with a trained classifier
:param strings: a single string or a list of strings representing the pieces of text that should be classified
:return: list containing the predictions of the models for the inputted pieces of text
"""
assert self.has_trained
if isinstance(strings, str):
strings = [strings]
if isinstance(strings, list):
strings = [[string] for string in strings]
fields = [('text', self._TEXT)]
list_of_examples = [Example.fromlist(string, fields) for string in strings]
dataset = torchtext.data.Dataset(list_of_examples, fields)
data = Iterator(dataset, batch_size=1, device=torch.device("cpu"), sort=False, sort_within_batch=False,
repeat=False, shuffle=False)
predictions = []
for item in data:
x = item.text
self.model.to(self.device)
self.model = self.model.eval()
outputs = self.model([x[0].to(self.device), x[1].to(self.device)])
predictions.extend(outputs.detach().cpu().argmax(1).tolist())
results = [self._label_names[i] for i in predictions]
return results
def __init__(self, batch_size):
self.text = Field(
lower=True,
tokenize=lambda x: [tok.text for tok in spacy_en.tokenizer(x)],
batch_first=True)
self.label = Field(sequential=False, unk_token=None, is_target=True)
self.train, self.dev, self.test = SNLI.splits(self.text, self.label)
self.sizes = {
'train': len(self.train),
'val': len(self.dev),
'test': len(self.test)
}
self.text.build_vocab(self.train, self.dev)
self.label.build_vocab(self.train)
vector_cache_loc = '.vector_cache/snli_vectors.pt'
if os.path.isfile(vector_cache_loc):
self.text.vocab.vectors = torch.load(vector_cache_loc)
else:
self.text.vocab.load_vectors('glove.840B.300d')
torch.save(self.text.vocab.vectors, vector_cache_loc)
# Batching
self.train_iter, self.dev_iter, self.test_iter = Iterator.splits(
(self.train, self.dev, self.test),
batch_size=batch_size,
device='cuda:0' if torch.cuda.is_available() else 'cpu')
self.vocab_size = len(self.text.vocab)
self.out_dim = len(self.label.vocab)
self.labels = self.label.vocab.stoi
def test_language_model(config: dict) -> None:
os.environ['CUDA_VISIBLE_DEVICES'] = str(config['gpu'])
base_path = config['base_path']
save_path = os.path.join(base_path, 'language_model.pkl')
vocab_path = os.path.join(base_path, 'vocab.pkl')
TEXT = data.Field(sequential=True,
lower=True,
batch_first=True,
init_token=SOS,
eos_token=EOS)
fields = [('sentence', TEXT)]
test_data = TabularDataset(path=os.path.join(base_path, 'test.tsv'),
format='tsv',
skip_header=True,
fields=fields)
with open(vocab_path, 'rb') as handle:
vocab = pickle.load(handle)
TEXT.vocab = vocab
device = torch.device('cuda:0')
test_iter = Iterator(test_data,
batch_size=config['batch_size'],
shuffle=False,
device=device)
model = torch.load(save_path)
criterion = nn.CrossEntropyLoss(ignore_index=PAD_INDEX)
test_loss = eval_language_model(model, test_iter, criterion)
print('test_loss: %.4f\ttest_ppl: %.4f' % (test_loss, 2**test_loss))
def __init__(self, dataset, batch_size, do_train, seed=1):
super(BatchIterator, self).__init__()
self.batch_size = batch_size
self.do_train = do_train
random.seed(seed)
# We need different iterators for train and eval
if self.do_train:
iterator = BucketIterator(dataset=dataset,
batch_size=self.batch_size,
train=True,
sort_key=lambda x: torchtext.data.
interleave_keys(len(x.src), len(x.trg)))
else:
iterator = Iterator(dataset=dataset,
batch_size=self.batch_size,
sort=False,
sort_within_batch=False,
repeat=False)
self.iterator = iterator
self.num_batches = len(iterator)
self.iter = iter(self.iterator)
def __init__(self, config):
self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
self.batch_size = config['batch_size']
self.pad_id = self.tokenizer._convert_token_to_id("[PAD]")
# Objects in which the data will be stored.
self.text = Field(sequential=True,
lower=True,
tokenize=self.tokenizer.tokenize,
batch_first=True,
pad_token='[PAD]',
unk_token='[UNK]')
self.labels = Field(sequential=False, is_target=True)
self.train, self.dev, self.test = MultiNLI.splits(
self.text, self.labels)
# Builds vocabulary for the data.
self.text.build_vocab(self.train, self.dev, self.test)
self.labels.build_vocab(self.train)
# Standard torchtext iterators, these do not return input suitable for BERT.
self.train_iter, self.dev_iter, self.test_iter = Iterator.splits(
(self.train, self.dev, self.test),
batch_size=config['batch_size'],
device=config['device'])
def create_iter(self, batch_size):
"""
构建迭代器
:param batch_size: 每批的大小
:return: iter
"""
# 定义torchtext中的Field
fields = [('english', self.english), ('chinese', self.chinese)]
examples = []
# 构建中英文example
for en, ch in zip(self.english_list, self.chinese_list):
item = [en, ch]
examples.append(data.Example().fromlist(item, fields))
# 划分训练集，测试集
train, test = Dataset(examples=examples,
fields=fields).split(split_ratio=0.8)
self.english.build_vocab(train)
self.chinese.build_vocab(train)
self.english_voca_size = len(self.english.vocab)
self.chinese_voca_size = len(self.chinese.vocab)
train_iter, test_iter = Iterator.splits(
(train, test),
batch_sizes=(batch_size, len(test)),
sort_key=lambda x: len(x.english),
sort_within_batch=True,
device=-1)
return train_iter, test_iter
def load_data():
TEXT, LABELS, LENGTH = Field(sequential=True, use_vocab=True), \
Field(sequential=True, use_vocab=False, preprocessing=lambda x: list(map(int, x)),
pad_token=-1), \
Field(sequential=False, use_vocab=False)
train_set = TabularDataset(path=DATA_DIR + 'train.tok.tsv',
format='TSV',
fields=[('text', TEXT), ('labels', LABELS),
('length', LENGTH)],
skip_header=True)
val_set = TabularDataset(path=DATA_DIR + 'dev.tok.tsv',
format='TSV',
fields=[('text', TEXT), ('labels', LABELS),
('length', LENGTH)],
skip_header=True)
train_loader = Iterator(
dataset=train_set,
batch_size=BATCH_SIZE,
sort_key=lambda x: len(x.text),
sort_within_batch=True,
shuffle=True,
)
val_loader = BucketIterator(
dataset=val_set,
batch_size=BATCH_SIZE,
sort_key=lambda x: len(x.text),
sort_within_batch=False,
shuffle=False,
)
TEXT.build_vocab(train_set)
# LABELS.build_vocab(train_set)
return (TEXT, LABELS, LENGTH), (train_set, val_set), (train_loader,
val_loader)
def train(dataset_dir, emb_file, epoch, batch_size):
(train_data, test_data, text_field, label_field) = dataset.load_data(dataset_dir, emb_file)
class_size = len(label_field.vocab)
nbow = nbow_model.NBoW(class_size, text_field.vocab.vectors)
nbow.train()
optimizer = torch.optim.Adam(nbow.parameters())
train_iter = Iterator(train_data, batch_size)
for n in range(epoch):
for batch in train_iter:
optimizer.zero_grad()
logit = nbow(batch.text.t())
loss = F.cross_entropy(logit, batch.label)
loss.backward()
optimizer.step()
nbow.eval()
(accuracy, num_correct) = compute_accuracy(nbow, test_data)
print('Epoch: {} Accuracy: {:.2f}% ({}/{})'.format(n + 1, accuracy * 100, num_correct, len(test_data)))
nbow.train()
def get_data_iter(train_csv, test_csv, fix_length):
TEXT = data.Field(sequential=True,
lower=True,
fix_length=fix_length,
batch_first=True)
LABEL = data.Field(sequential=False, use_vocab=False)
train_fields = [("label", LABEL), ("title", None), ("text", TEXT)]
train = TabularDataset(path=train_csv,
format="csv",
fields=train_fields,
skip_header=True)
train_iter = BucketIterator(train,
batch_size=batch_size,
device=-1,
sort_key=lambda x: len(x.text),
sort_within_batch=False,
repeat=False)
test_fields = [("label", LABEL), ("title", None), ("text", TEXT)]
test = TabularDataset(path=test_csv,
format="csv",
fields=test_fields,
skip_header=True)
test_iter = Iterator(test,
batch_size=batch_size,
device=-1,
sort=False,
sort_within_batch=False,
repeat=False)
# vectors = Vectors(name=word2vec_dir)
# TEXT.build_vocab(train, vectors=vectors)
TEXT.build_vocab(train)
vocab = TEXT.vocab
return train_iter, test_iter, vocab
def test(args):
train_data, dev_data, test_data, sentence_vocab, pred_arg_vocab, _ = read_data(
args.path, args.train, args.test, args.dev)
test_iter = Iterator(test_data,
1,
sort_key=lambda x: len(x.sentence),
train=False,
repeat=False)
model = torch.load(args.save_model)
model.eval()
instances_seen = 0
labels = []
outputs = []
with torch.no_grad():
for v_iteration, instance in enumerate(test_iter):
model_outputs = model(instance)
output = torch.sigmoid(model_outputs)
outputs.append(output.item())
labels.append(instance.label[0].item())
pred = lambda x: 1 if x >= 0.15 else 0
predicted = [pred(x) for x in outputs]
print(f1_score(labels, predicted))
def predict(self, model_name, filepath=Path('../data/output/sub.csv')):
"""
"""
preds = []
sub_dataset = TabularDataset(filepath,
format="CSV",
fields=self.fields,
skip_header=True)
sub_iter = Iterator(sub_dataset,
batch_size=self.batch_size,
device=self.device,
train=False,
shuffle=False,
sort=False)
self.load_checkpoint(model_name)
self.model.eval()
with torch.no_grad():
for (label, text), _ in sub_iter:
label = label.type(torch.LongTensor)
label = label.to(self.device)
text = text.type(torch.LongTensor)
text = text.to(self.device)
output = self.model(text, label)
_, output = output
preds.extend(torch.argmax(output, 1).tolist())
id_list = ["twitter_" + str(n) for n in range(1, len(sub_dataset) + 1)]
label_list = [
"SARCASM" if pred == 1 else "NOT_SARCASM" for pred in preds
]
df_sub = pd.DataFrame(list(zip(id_list, label_list)),
columns=['id', 'label'])
return df_sub
def predict_text_cnn(model_path, file_path, vocab_path, batch_size=64):
TEXT = data.Field(sequential=True, lower=True, batch_first=True)
fields = [('sentence', TEXT)]
test_data = TabularDataset(path=file_path,
format='tsv',
skip_header=True,
fields=fields)
with open(vocab_path, 'rb') as handle:
vocab = pickle.load(handle)
TEXT.vocab = vocab
device = torch.device('cuda:0')
test_iter = Iterator(test_data,
batch_size=batch_size,
shuffle=False,
device=device)
model = torch.load(model_path)
sentiments = []
model.eval()
with torch.no_grad():
for batch in test_iter:
sentence = batch.sentence
logit = model(sentence)
prob = torch.softmax(logit, dim=-1)[:, 1].tolist()
sentiments.extend(prob)
return sentiments
def get_iterator(dataset, batch_size, train=True):
return Iterator(dataset,
batch_size=batch_size,
device=device,
train=train,
shuffle=train,
sort=False)