def test_save_and_load(self):
fp = 'vocab_save_test.txt'
try:
# check word2idx没变，no_create_entry正常
words = list('abcdefaddfdkjfe')
no_create_entry = list('12342331')
unk = '[UNK]'
vocab = Vocabulary(unknown=unk, max_size=500)
vocab.add_word_lst(words)
vocab.add_word_lst(no_create_entry, no_create_entry=True)
vocab.save(fp)
new_vocab = Vocabulary.load(fp)
for word, index in vocab:
self.assertEqual(new_vocab.to_index(word), index)
for word in no_create_entry:
self.assertTrue(new_vocab._is_word_no_create_entry(word))
for word in words:
self.assertFalse(new_vocab._is_word_no_create_entry(word))
for idx in range(len(vocab)):
self.assertEqual(vocab.to_word(idx), new_vocab.to_word(idx))
self.assertEqual(vocab.unknown, new_vocab.unknown)
# 测试vocab中包含None的padding和unk
vocab = Vocabulary(padding=None, unknown=None)
words = list('abcdefaddfdkjfe')
no_create_entry = list('12342331')
vocab.add_word_lst(words)
vocab.add_word_lst(no_create_entry, no_create_entry=True)
vocab.save(fp)
new_vocab = Vocabulary.load(fp)
for word, index in vocab:
self.assertEqual(new_vocab.to_index(word), index)
for word in no_create_entry:
self.assertTrue(new_vocab._is_word_no_create_entry(word))
for word in words:
self.assertFalse(new_vocab._is_word_no_create_entry(word))
for idx in range(len(vocab)):
self.assertEqual(vocab.to_word(idx), new_vocab.to_word(idx))
self.assertEqual(vocab.unknown, new_vocab.unknown)
finally:
import os
if os.path.exists(fp):
os.remove(fp)
task.test_set.apply(lambda x: [vocab.to_index(w) for w in x['words']],
new_field_name='words_idx')
task.train_set.set_input('task_id', 'words_idx', flag=True)
task.train_set.set_target('label', flag=True)
task.dev_set.set_input('task_id', 'words_idx', flag=True)
task.dev_set.set_target('label', flag=True)
task.test_set.set_input('task_id', 'words_idx', flag=True)
task.test_set.set_target('label', flag=True)
logger.info('Finished. Dumping vocabulary to data/vocab.txt')
with open('data/vocab.txt', mode='w', encoding='utf-8') as f:
for i in range(len(vocab)):
f.write(vocab.to_word(i) + '\n')
logger.info('Testing data...')
for task in task_lst:
logger.info(str(task.task_id) + ' ' + task.task_name)
logger.info(task.train_set[0])
logger.info(task.dev_set[0])
logger.info(task.test_set[0])
logger.info('Dumping data...')
data = {'task_lst': task_lst}
save_file = open('data/data.pkl', 'wb')
pickle.dump(data, save_file)
save_file.close()
logger.info('Finished. Looking up for word embeddings...')
embed_path = '/remote-home/txsun/data/word-embedding/glove/glove.840B.300d.txt'
def test_to_word(self):
vocab = Vocabulary()
vocab.update(text)
self.assertEqual(
text, [vocab.to_word(idx) for idx in [vocab[w] for w in text]])
def prepare_dataInfo(mode,
vocab_size,
config,
train_data_path=None,
dev_data_path=None,
test_data_path=None):
def sent_to_words(sents):
result = []
for sent in sents:
result.extend([
word.strip() for word in sent.split(" ")
if len(word.strip()) != 0
])
return result
# dataloader = Cnn_dailymailLodaer()
# 适用于输入是json的文件，每个json必须有field ：text和summary，二者都是tokenized
dataloader = JsonLoader(fields={
"text": "words",
"summary": "abstract_sentences"
})
if mode == 'train':
if train_data_path is None or dev_data_path is None:
print("training with no train data path or dev data path! ")
paths = {"train": train_data_path, "dev": dev_data_path}
else:
if test_data_path is None:
print("testing with no test data path ! ")
paths = {"train": train_data_path, "test": test_data_path}
# dataInfo = dataloader.process(paths, vocab_path, vocab_size)
print("=" * 10)
print(paths)
dataInfo = dataloader.load(paths)
for key, _dataset in dataInfo.datasets.items():
_dataset.apply(lambda ins: " ".join(ins['words']),
new_field_name='article')
_dataset.apply(lambda ins: sent_to_words(ins['words']),
new_field_name='words')
_dataset.apply(
lambda ins: sent_tokenize(" ".join(ins['abstract_sentences'])),
new_field_name='abstract_sentences')
vocab = Vocabulary(max_size=vocab_size - 2,
padding=PAD_TOKEN,
unknown=UNKNOWN_TOKEN)
vocab.from_dataset(dataInfo.datasets['train'], field_name='words')
vocab.add(START_DECODING)
vocab.add(STOP_DECODING)
print(vocab.to_word(0))
print(len(vocab))
assert vocab_size == len(vocab), "vocab_size error!!!"
dataInfo.set_vocab(vocab, "train")
for key, dataset in dataInfo.datasets.items():
data_dict = {
"enc_len": [],
"enc_input": [],
"dec_input": [],
"target": [],
"dec_len": [],
"article_oovs": [],
"enc_input_extend_vocab": []
}
for instance in dataset:
article = instance["article"]
abstract_sentences = instance["abstract_sentences"]
enc_len, enc_input, dec_input, target, dec_len, article_oovs, enc_input_extend_vocab = getting_full_info(
article, abstract_sentences, dataInfo.vocabs['train'], config)
data_dict["enc_len"].append(enc_len)
data_dict["enc_input"].append(enc_input)
data_dict["dec_input"].append(dec_input)
data_dict["target"].append(target)
data_dict["dec_len"].append(dec_len)
data_dict["article_oovs"].append(article_oovs)
data_dict["enc_input_extend_vocab"].append(enc_input_extend_vocab)
logger.info("-----prepare_dataInfo for dataset " + key + "-----")
logger.info(
str(len(data_dict["enc_len"])) + " " +
str(len(data_dict["enc_input"])) + " " +
str(len(data_dict["dec_input"])) + " " +
str(len(data_dict["target"])) + " " +
str(len(data_dict["dec_len"])) + " " +
str(len(data_dict["article_oovs"])) + " " +
str(len(data_dict["enc_input_extend_vocab"])))
dataset.add_field("enc_len", data_dict["enc_len"])
dataset.add_field("enc_input", data_dict["enc_input"])
dataset.add_field("dec_input", data_dict["dec_input"])
dataset.add_field("target", data_dict["target"])
dataset.add_field("dec_len", data_dict["dec_len"])
dataset.add_field("article_oovs", data_dict["article_oovs"])
dataset.add_field("enc_input_extend_vocab",
data_dict["enc_input_extend_vocab"])
dataset.set_input("enc_len", "enc_input", "dec_input", "dec_len",
"article_oovs", "enc_input_extend_vocab")
dataset.set_target("target", "article_oovs", "abstract_sentences")
'''
for name, dataset in dataInfo.datasets.items():
for field_name in dataset.get_field_names():
dataset.apply_field(convert_list_to_ndarray, field_name=field_name, new_field_name=field_name)
'''
return dataInfo