def __init__(
self, is_training: bool, dataset: Dataset, gaz_vocabulary_dir: str,
gaz_pretrained_word_embedding_loader: PretrainedWordEmbeddingLoader
):
"""
构建 Gaz 词汇表
:param is_training: 当前是否 Training 状态
:param dataset: 数据集
:param gaz_vocabulary_dir: gaz 词汇表存放目录
:param gaz_pretrained_word_embedding_loader: gaz 预训练 word embedding 载入器
"""
super().__init__(is_training=is_training)
# gazetter 理论上来说，应该支持持久化的，这里并没有做
gazetteer = Gazetteer(gaz_pretrained_word_embedding_loader=
gaz_pretrained_word_embedding_loader)
if is_training:
gaz_vocabulary_collate = GazVocabularyCollate(gazetteer=gazetteer)
data_loader = DataLoader(dataset=dataset,
batch_size=100,
shuffle=False,
num_workers=0,
collate_fn=gaz_vocabulary_collate)
gaz_words = list()
for batch_gaz_words in data_loader:
gaz_words.extend(batch_gaz_words)
gaz_vocabulary = Vocabulary(tokens=gaz_words,
padding=Vocabulary.PADDING,
unk=Vocabulary.UNK,
special_first=True)
gaz_vocabulary = PretrainedVocabulary(
vocabulary=gaz_vocabulary,
pretrained_word_embedding_loader=
gaz_pretrained_word_embedding_loader)
gaz_vocabulary.save_to_file(gaz_vocabulary_dir)
else:
gaz_vocabulary = Vocabulary.from_file(gaz_vocabulary_dir)
self.gaz_vocabulary = gaz_vocabulary
self.gazetteer = gazetteer
def test_save_and_load():
"""
测试存储和载入 vocabulary
:return:
"""
batch_tokens = [["我", "和", "你"], ["在", "我"], ["newline\nnewline"]]
vocabulary = Vocabulary(batch_tokens,
padding=Vocabulary.PADDING,
unk=Vocabulary.UNK,
special_first=True,
other_special_tokens=["<Start>", "<End>"],
min_frequency=1,
max_size=None)
ASSERT.assertEqual(vocabulary.size, 9)
ASSERT.assertEqual(vocabulary.padding, vocabulary.PADDING)
ASSERT.assertEqual(vocabulary.unk, vocabulary.UNK)
ASSERT.assertEqual(vocabulary.index(vocabulary.padding), 0)
ASSERT.assertEqual(vocabulary.index(vocabulary.unk), 1)
ASSERT.assertEqual(vocabulary.index("<Start>"), 2)
ASSERT.assertEqual(vocabulary.index("<End>"), 3)
ASSERT.assertEqual(vocabulary.index("我"), 4)
ASSERT.assertEqual(vocabulary.index("newline\nnewline"), 8)
ASSERT.assertEqual(vocabulary.index("哈哈"),
vocabulary.index(vocabulary.unk))
vocab_dir = os.path.join(ROOT_PATH, "data/easytext/tests")
if not os.path.isdir(vocab_dir):
os.makedirs(vocab_dir, exist_ok=True)
vocabulary.save_to_file(vocab_dir)
loaded_vocab = Vocabulary.from_file(directory=vocab_dir)
ASSERT.assertEqual(vocabulary.size, 9)
ASSERT.assertEqual(loaded_vocab.padding, vocabulary.PADDING)
ASSERT.assertEqual(loaded_vocab.unk, vocabulary.UNK)
ASSERT.assertEqual(loaded_vocab.index(vocabulary.padding), 0)
ASSERT.assertEqual(loaded_vocab.index(vocabulary.unk), 1)
ASSERT.assertEqual(loaded_vocab.index("<Start>"), 2)
ASSERT.assertEqual(loaded_vocab.index("<End>"), 3)
ASSERT.assertEqual(vocabulary.index("我"), 4)
ASSERT.assertEqual(vocabulary.index("newline\nnewline"), 8)
ASSERT.assertEqual(vocabulary.index("哈哈"),
vocabulary.index(vocabulary.unk))
def __init__(self,
is_training: bool,
dataset: Dataset,
vocabulary_collate,
token_vocabulary_dir: str,
label_vocabulary_dir: str,
is_build_token_vocabulary: bool,
pretrained_word_embedding_loader: PretrainedWordEmbeddingLoader):
"""
词汇表构建器
:param is_training: 因为在 train 和 非 train, 词汇表的构建行为有所不同;
如果是 train, 则一般需要重新构建; 而对于 非train, 使用先前构建好的即可。
:param dataset: 数据集
:param vocabulary_collate: 词汇表 collate
:param token_vocabulary_dir: token vocabulary 存放目录
:param label_vocabulary_dir: label vocabulary 存放目录
:param is_build_token_vocabulary: 是否构建 token vocabulary, 因为在使用 Bert 或者 其他模型作为预训练的 embedding,
则没有必要构建 token vocabulary.
:param pretrained_word_embedding_loader: 预训练词汇表
"""
super().__init__(is_training=is_training)
token_vocabulary = None
label_vocabulary = None
if is_training:
data_loader = DataLoader(dataset=dataset,
batch_size=100,
shuffle=False,
num_workers=0,
collate_fn=vocabulary_collate)
batch_tokens = list()
batch_sequence_labels = list()
for collate_dict in data_loader:
batch_tokens.extend(collate_dict["tokens"])
batch_sequence_labels.extend(collate_dict["sequence_labels"])
if is_build_token_vocabulary:
token_vocabulary = Vocabulary(tokens=batch_tokens,
padding=Vocabulary.PADDING,
unk=Vocabulary.UNK,
special_first=True)
if pretrained_word_embedding_loader is not None:
token_vocabulary = \
PretrainedVocabulary(vocabulary=token_vocabulary,
pretrained_word_embedding_loader=pretrained_word_embedding_loader)
if token_vocabulary_dir:
token_vocabulary.save_to_file(token_vocabulary_dir)
label_vocabulary = LabelVocabulary(labels=batch_sequence_labels,
padding=LabelVocabulary.PADDING)
if label_vocabulary_dir:
label_vocabulary.save_to_file(label_vocabulary_dir)
else:
if is_build_token_vocabulary and token_vocabulary_dir:
token_vocabulary = Vocabulary.from_file(token_vocabulary_dir)
if label_vocabulary_dir:
label_vocabulary = LabelVocabulary.from_file(label_vocabulary_dir)
self.token_vocabulary = token_vocabulary
self.label_vocabulary = label_vocabulary
def __call__(self, config: Dict, train_type: int):
serialize_dir = config["serialize_dir"]
vocabulary_dir = config["vocabulary_dir"]
pretrained_embedding_file_path = config["pretrained_embedding_file_path"]
word_embedding_dim = config["word_embedding_dim"]
pretrained_embedding_max_size = config["pretrained_embedding_max_size"]
is_fine_tuning = config["fine_tuning"]
word_vocab_dir = os.path.join(vocabulary_dir, "vocabulary", "word_vocabulary")
event_type_vocab_dir = os.path.join(vocabulary_dir, "vocabulary", "event_type_vocabulary")
entity_tag_vocab_dir = os.path.join(vocabulary_dir, "vocabulary", "entity_tag_vocabulary")
if train_type == Train.NEW_TRAIN:
if os.path.isdir(serialize_dir):
shutil.rmtree(serialize_dir)
os.makedirs(serialize_dir)
if os.path.isdir(vocabulary_dir):
shutil.rmtree(vocabulary_dir)
os.makedirs(vocabulary_dir)
os.makedirs(word_vocab_dir)
os.makedirs(event_type_vocab_dir)
os.makedirs(entity_tag_vocab_dir)
elif train_type == Train.RECOVERY_TRAIN:
pass
else:
assert False, f"train_type: {train_type} error!"
train_dataset_file_path = config["train_dataset_file_path"]
validation_dataset_file_path = config["validation_dataset_file_path"]
num_epoch = config["epoch"]
batch_size = config["batch_size"]
if train_type == Train.NEW_TRAIN:
# 构建词汇表
ace_dataset = ACEDataset(train_dataset_file_path)
vocab_data_loader = DataLoader(dataset=ace_dataset,
batch_size=10,
shuffle=False, num_workers=0,
collate_fn=EventVocabularyCollate())
tokens: List[List[str]] = list()
event_types: List[List[str]] = list()
entity_tags: List[List[str]] = list()
for colleta_dict in vocab_data_loader:
tokens.extend(colleta_dict["tokens"])
event_types.extend(colleta_dict["event_types"])
entity_tags.extend(colleta_dict["entity_tags"])
word_vocabulary = Vocabulary(tokens=tokens,
padding=Vocabulary.PADDING,
unk=Vocabulary.UNK,
special_first=True)
glove_loader = GloveLoader(embedding_dim=word_embedding_dim,
pretrained_file_path=pretrained_embedding_file_path,
max_size=pretrained_embedding_max_size)
pretrained_word_vocabulary = PretrainedVocabulary(vocabulary=word_vocabulary,
pretrained_word_embedding_loader=glove_loader)
pretrained_word_vocabulary.save_to_file(word_vocab_dir)
event_type_vocabulary = Vocabulary(tokens=event_types,
padding="",
unk="Negative",
special_first=True)
event_type_vocabulary.save_to_file(event_type_vocab_dir)
entity_tag_vocabulary = LabelVocabulary(labels=entity_tags,
padding=LabelVocabulary.PADDING)
entity_tag_vocabulary.save_to_file(entity_tag_vocab_dir)
else:
pretrained_word_vocabulary = PretrainedVocabulary.from_file(word_vocab_dir)
event_type_vocabulary = Vocabulary.from_file(event_type_vocab_dir)
entity_tag_vocabulary = Vocabulary.from_file(entity_tag_vocab_dir)
model = EventModel(alpha=0.5,
activate_score=True,
sentence_vocab=pretrained_word_vocabulary,
sentence_embedding_dim=word_embedding_dim,
entity_tag_vocab=entity_tag_vocabulary,
entity_tag_embedding_dim=50,
event_type_vocab=event_type_vocabulary,
event_type_embedding_dim=300,
lstm_hidden_size=300,
lstm_encoder_num_layer=1,
lstm_encoder_droupout=0.4)
trainer = Trainer(
serialize_dir=serialize_dir,
num_epoch=num_epoch,
model=model,
loss=EventLoss(),
optimizer_factory=EventOptimizerFactory(is_fine_tuning=is_fine_tuning),
metrics=EventF1MetricAdapter(event_type_vocabulary=event_type_vocabulary),
patient=10,
num_check_point_keep=5,
devices=None
)
train_dataset = EventDataset(dataset_file_path=train_dataset_file_path,
event_type_vocabulary=event_type_vocabulary)
validation_dataset = EventDataset(dataset_file_path=validation_dataset_file_path,
event_type_vocabulary=event_type_vocabulary)
event_collate = EventCollate(word_vocabulary=pretrained_word_vocabulary,
event_type_vocabulary=event_type_vocabulary,
entity_tag_vocabulary=entity_tag_vocabulary,
sentence_max_len=512)
train_data_loader = DataLoader(dataset=train_dataset,
batch_size=batch_size,
num_workers=0,
collate_fn=event_collate)
validation_data_loader = DataLoader(dataset=validation_dataset,
batch_size=batch_size,
num_workers=0,
collate_fn=event_collate)
if train_type == Train.NEW_TRAIN:
trainer.train(train_data_loader=train_data_loader,
validation_data_loader=validation_data_loader)
else:
trainer.recovery_train(train_data_loader=train_data_loader,
validation_data_loader=validation_data_loader)
