def test_identity_encoder_unknown():
sample = [
'people/deceased_person/place_of_death',
'symbols/name_source/namesakes'
]
encoder = IdentityEncoder(sample)
input_ = 'symbols/namesake/named_after'
output = encoder.encode(input_)
assert len(output) == 1
assert encoder.decode(output) == UNKNOWN_TOKEN
def test_identity_encoder_known():
input_ = 'symbols/namesake/named_after'
sample = [
'people/deceased_person/place_of_death',
'symbols/name_source/namesakes'
]
sample.append(input_)
encoder = IdentityEncoder(sample)
output = encoder.encode(input_)
assert len(output) == 1
assert encoder.decode(output) == input_
def __init__(self, is_char=False):
self.mapped_data = dict()
if not is_char:
self.train, self.valid, self.test = ptb(train=True,
dev=True,
test=True)
else:
self.train, self.valid, self.test = ptb(
train=True,
dev=True,
test=True,
train_filename="ptb.char.train.txt",
dev_filename="ptb.char.valid.txt",
test_filename="ptb.char.test.txt")
self._map_data(self.train + self.valid + self.test)
#encodeer data
encoder = IdentityEncoder(self.train + self.valid + self.test)
self.train = torch.LongTensor(encoder.encode(self.train))
self.valid = torch.LongTensor(encoder.encode(self.valid))
self.test = torch.LongTensor(encoder.encode(self.test))
self.ntoken = encoder.vocab_size
print('hello')
def test_identity_encoder_sequence():
input_ = [
'symbols/namesake/named_after', 'people/deceased_person/place_of_death'
]
sample = [
'people/deceased_person/place_of_death',
'symbols/name_source/namesakes'
]
encoder = IdentityEncoder(sample)
output = encoder.encode(input_)
assert len(output) == 2
assert encoder.decode(output) == [
UNKNOWN_TOKEN, 'people/deceased_person/place_of_death'
]
with open(fn, 'rb') as f:
model, criterion, optimizer = torch.load(f)
from torchnlp import datasets
from torchnlp.text_encoders import IdentityEncoder
from torchnlp.samplers import BPTTBatchSampler
print('Producing dataset...')
train, val, test = getattr(datasets, args.data)(train=True,
dev=True,
test=True)
encoder = IdentityEncoder(train + val + test)
train_data = encoder.encode(train)
val_data = encoder.encode(val)
test_data = encoder.encode(test)
eval_batch_size = 10
test_batch_size = 1
train_source_sampler, val_source_sampler, test_source_sampler = tuple([
BPTTBatchSampler(d, args.bptt, args.batch_size, True, 'source')
for d in (train, val, test)
])
train_target_sampler, val_target_sampler, test_target_sampler = tuple([
BPTTBatchSampler(d, args.bptt, args.batch_size, True, 'target')
for d in (train, val, test)
])
row['premise'] = row['premise'].lower()
row['hypothesis'] = row['hypothesis'].lower()
# Make Encoders
sentence_corpus = [row['premise'] for row in datasets_iterator(train, dev, test)]
sentence_corpus += [row['hypothesis'] for row in datasets_iterator(train, dev, test)]
sentence_encoder = WhitespaceEncoder(sentence_corpus)
label_corpus = [row['label'] for row in datasets_iterator(train, dev, test)]
label_encoder = IdentityEncoder(label_corpus)
# Encode
for row in datasets_iterator(train, dev, test):
row['premise'] = sentence_encoder.encode(row['premise'])
row['hypothesis'] = sentence_encoder.encode(row['hypothesis'])
row['label'] = label_encoder.encode(row['label'])
config = args
config.n_embed = sentence_encoder.vocab_size
config.d_out = label_encoder.vocab_size
config.n_cells = config.n_layers
# double the number of cells for bidirectional networks
if config.birnn:
config.n_cells *= 2
if args.resume_snapshot:
model = torch.load(
args.resume_snapshot, map_location=lambda storage, location: storage.cuda(args.gpu))
else:
model = SNLIClassifier(config)
def load_data(data_type,
preprocessing=False,
fine_grained=False,
verbose=False,
text_length=5000,
encode=True,
load_SLE=False):
if data_type == 'imdb':
train_data, test_data = imdb_dataset(preprocessing=preprocessing,
verbose=verbose,
text_length=text_length)
elif data_type == 'newsgroups':
train_data, test_data = newsgroups_dataset(preprocessing=preprocessing,
verbose=verbose,
text_length=text_length)
elif data_type == 'reuters':
train_data, test_data = reuters_dataset(preprocessing=preprocessing,
fine_grained=fine_grained,
verbose=verbose,
text_length=text_length)
elif data_type == 'webkb':
train_data, test_data = webkb_dataset(preprocessing=preprocessing,
verbose=verbose,
text_length=text_length)
elif data_type == 'cade':
train_data, test_data = cade_dataset(preprocessing=preprocessing,
verbose=verbose,
text_length=text_length)
elif data_type == 'dbpedia':
train_data, test_data = dbpedia_dataset(preprocessing=preprocessing,
verbose=verbose,
text_length=text_length)
elif data_type == 'agnews':
train_data, test_data = agnews_dataset(preprocessing=preprocessing,
verbose=verbose,
text_length=text_length)
elif data_type == 'yahoo':
train_data, test_data = yahoo_dataset(preprocessing=preprocessing,
verbose=verbose,
text_length=text_length)
elif data_type == 'sogou':
train_data, test_data = sogou_dataset(preprocessing=preprocessing,
verbose=verbose,
text_length=text_length)
elif data_type == 'yelp':
train_data, test_data = yelp_dataset(preprocessing=preprocessing,
fine_grained=fine_grained,
verbose=verbose,
text_length=text_length)
elif data_type == 'amazon':
train_data, test_data = amazon_dataset(preprocessing=preprocessing,
fine_grained=fine_grained,
verbose=verbose,
text_length=text_length)
elif data_type == 'custom':
test_data = custom_dataset(preprocessing=preprocessing,
fine_grained=fine_grained,
verbose=verbose,
text_length=text_length)
sentence_encoder = pickle.load(open('epochs/sentence_encoder', 'rb'))
label_encoder = pickle.load(open('epochs/label_encoder', 'rb'))
for row in datasets_iterator(test_data):
row['text'] = sentence_encoder.encode(' '.join(row['text']))
row['label'] = label_encoder.encode(row['label'])
return sentence_encoder.vocab_size, label_encoder.vocab_size, test_data
else:
raise ValueError('{} data type not supported.'.format(data_type))
if encode:
if load_SLE:
sentence_encoder = pickle.load(
open('epochs/sentence_encoder', 'rb'))
label_encoder = pickle.load(open('epochs/label_encoder', 'rb'))
else:
sentence_corpus = [
row['text'] for row in datasets_iterator(train_data, )
]
label_corpus = [
row['label'] for row in datasets_iterator(train_data, )
]
sentence_encoder = WhitespaceEncoder(
sentence_corpus,
reserved_tokens=[PADDING_TOKEN, UNKNOWN_TOKEN])
label_encoder = IdentityEncoder(label_corpus, reserved_tokens=[])
with open('epochs/sentence_encoder', 'wb') as f:
pickle.dump(sentence_encoder, f)
with open('epochs/label_encoder', 'wb') as f:
pickle.dump(label_encoder, f)
# Encode
for row in datasets_iterator(train_data, test_data):
row['text'] = sentence_encoder.encode(row['text'])
row['label'] = label_encoder.encode(row['label'])
return sentence_encoder.vocab_size, label_encoder.vocab_size, train_data, test_data
else:
return train_data, test_data