def main(args):
print('Loading dataset...')
x_train, y_train = load_data_and_labels(args.train_data)
x_valid, y_valid = load_data_and_labels(args.valid_data)
print('Transforming datasets...')
p = IndexTransformer(use_char=args.no_char_feature)
p.fit(x_train, y_train)
print('Building a model.')
model = BiLSTMCRF(char_embedding_dim=args.char_emb_size,
word_embedding_dim=args.word_emb_size,
char_lstm_size=args.char_lstm_units,
word_lstm_size=args.word_lstm_units,
char_vocab_size=p.char_vocab_size,
word_vocab_size=p.word_vocab_size,
num_labels=p.label_size,
dropout=args.dropout,
use_char=args.no_char_feature,
use_crf=args.no_use_crf)
model, loss = model.build()
model.compile(loss=loss, optimizer='adam')
print('Training the model...')
trainer = Trainer(model, preprocessor=p)
trainer.train(x_train, y_train, x_valid, y_valid)
print('Saving the model...')
model.save(args.weights_file, args.params_file)
p.save(args.preprocessor_file)
def main(args):
print('Loading datasets...')
X, y = load_data_and_labels(args.data_path)
x_train, x_valid, y_train, y_valid = train_test_split(X,
y,
test_size=0.1,
random_state=42)
embeddings = KeyedVectors.load(args.embedding_path).wv
print('Transforming datasets...')
p = IndexTransformer()
p.fit(X, y)
embeddings = filter_embeddings(embeddings, p._word_vocab,
embeddings.vector_size)
print('Building a model...')
model = BiLSTMCRF(char_vocab_size=p.char_vocab_size,
word_vocab_size=p.word_vocab_size,
num_labels=p.label_size,
embeddings=embeddings,
char_embedding_dim=50)
model.build()
print('Training the model...')
trainer = Trainer(model, preprocessor=p)
trainer.train(x_train, y_train, x_valid, y_valid)
print('Saving the model...')
model.save(args.weights_file, args.params_file)
p.save(args.preprocessor_file)
def test_save_and_load(self):
it = IndexTransformer(lower=False)
x1, y1 = it.fit_transform(self.x, self.y)
x1_word, x1_char, x1_length = x1
self.assertFalse(os.path.exists(self.preprocessor_file))
it.save(self.preprocessor_file)
self.assertTrue(os.path.exists(self.preprocessor_file))
it = IndexTransformer.load(self.preprocessor_file)
x2, y2 = it.transform(self.x, self.y)
x2_word, x2_char, x2_length = x2
np.testing.assert_array_equal(x1_word, x2_word)
np.testing.assert_array_equal(x1_char, x2_char)
np.testing.assert_array_equal(y1, y2)
class BiLstmCrfNER(NERModel):
def __init__(self,
word_embedding_dim=100,
char_embedding_dim=25,
word_lstm_size=100,
char_lstm_size=25,
fc_dim=100,
dropout=0.5,
embeddings=None,
use_char=True,
use_crf=True,
batch_size=16,
learning_rate=0.001,
max_iter=10):
""" Construct a BiLSTM-CRF NER model. Model is augmented with character
level embeddings as well as word embeddings by default. Implementation
is provided by the Anago project.
Parameters
----------
word_embedding_dim : int, optional, default 100
word embedding dimensions.
char_embedding_dim : int, optional, default 25
character embedding dimensions.
word_lstm_size : int, optional, default 100
character LSTM feature extractor output dimensions.
char_lstm_size : int, optional, default 25
word tagger LSTM output dimensions.
fc_dim : int, optional, default 100
output fully-connected layer size.
dropout : float, optional, default 0.5
dropout rate.
embeddings : numpy array
word embedding matrix.
use_char : bool, optional, default True
add char feature.
use_crf : bool, optional, default True
use crf as last layer.
batch_size : int, optional, default 16
training batch size.
learning_rate : float, optional, default 0.001
learning rate for Adam optimizer
max_iter : int
number of epochs of training
Attributes
----------
preprocessor_ : reference to preprocessor
model_ : reference to generated model
trainer_ : internal reference to Anago Trainer (model)
tagger_ : internal reference to Anago Tagger (predictor)
"""
super().__init__()
self.word_embedding_dim = word_embedding_dim
self.char_embedding_dim = char_embedding_dim
self.word_lstm_size = word_lstm_size
self.char_lstm_size = char_lstm_size
self.fc_dim = fc_dim
self.dropout = dropout
self.embedding = None
self.use_char = True
self.use_crf = True
self.batch_size = batch_size
self.learning_rate = learning_rate
self.max_iter = max_iter
# populated by fit() and load(), expected by save() and transform()
self.preprocessor_ = None
self.model_ = None
self.trainer_ = None
self.tagger_ = None
def fit(self, X, y):
""" Trains the NER model. Input is list of list of tokens and tags.
Parameters
----------
X : list(list(str))
list of list of tokens
y : list(list(str))
list of list of BIO tags
Returns
-------
self
"""
log.info("Preprocessing dataset...")
self.preprocessor_ = IndexTransformer(use_char=self.use_char)
self.preprocessor_.fit(X, y)
log.info("Building model...")
self.model_ = BiLSTMCRF(
char_embedding_dim=self.char_embedding_dim,
word_embedding_dim=self.word_embedding_dim,
char_lstm_size=self.char_lstm_size,
word_lstm_size=self.word_lstm_size,
char_vocab_size=self.preprocessor_.char_vocab_size,
word_vocab_size=self.preprocessor_.word_vocab_size,
num_labels=self.preprocessor_.label_size,
dropout=self.dropout,
use_char=self.use_char,
use_crf=self.use_crf)
self.model_, loss = self.model_.build()
optimizer = Adam(lr=self.learning_rate)
self.model_.compile(loss=loss, optimizer=optimizer)
self.model_.summary()
log.info('Training the model...')
self.trainer_ = Trainer(self.model_, preprocessor=self.preprocessor_)
x_train, x_valid, y_train, y_valid = train_test_split(X, y,
test_size=0.1, random_state=42)
self.trainer_.train(x_train, y_train, x_valid=x_valid, y_valid=y_valid,
batch_size=self.batch_size, epochs=self.max_iter)
self.tagger_ = Tagger(self.model_, preprocessor=self.preprocessor_)
return self
def predict(self, X):
""" Predicts using the NER model.
Parameters
----------
X : list(list(str))
list of list of tokens.
Returns
-------
y : list(list(str))
list of list of predicted BIO tags.
"""
if self.tagger_ is None:
raise ValueError("No tagger found, either run fit() to train or load() a trained model")
log.info("Predicting from model...")
ypreds = [self.tagger_.predict(" ".join(x)) for x in X]
return ypreds
def save(self, dirpath):
""" Saves model to local disk, given a dirpath
Parameters
----------
dirpath : str
a directory where model artifacts will be saved.
Model saves a weights.h5 weights file, a params.json parameter
file, and a preprocessor.pkl preprocessor file.
Returns
-------
None
"""
if self.model_ is None or self.preprocessor_ is None:
raise ValueError("No model artifacts to save, either run fit() to train or load() a trained model")
if not os.path.exists(dirpath):
os.makedirs(dirpath)
weights_file = os.path.join(dirpath, "weights.h5")
params_file = os.path.join(dirpath, "params.json")
preprocessor_file = os.path.join(dirpath, "preprocessor.pkl")
save_model(self.model_, weights_file, params_file)
self.preprocessor_.save(preprocessor_file)
write_param_file(self.get_params(), os.path.join(dirpath, "params.yaml"))
def load(self, dirpath):
""" Loads a trained model from local disk, given the dirpath
Parameters
----------
dirpath : str
a directory where model artifacts are saved.
Returns
-------
self
"""
if not os.path.exists(dirpath):
raise ValueError("Model directory not found: {:s}".format(dirpath))
weights_file = os.path.join(dirpath, "weights.h5")
params_file = os.path.join(dirpath, "params.json")
preprocessor_file = os.path.join(dirpath, "preprocessor.pkl")
if not (os.path.exists(weights_file) or
os.path.exists(params_file) or
os.path.exists(preprocessor_file)):
raise ValueError("Model files may be corrupted, exiting")
self.model_ = load_model(weights_file, params_file)
self.preprocessor_ = IndexTransformer.load(preprocessor_file)
self.tagger_ = Tagger(self.model_, preprocessor=self.preprocessor_)
return self
class TestTrainer(unittest.TestCase):
@classmethod
def setUpClass(cls):
if not os.path.exists(LOG_ROOT):
os.mkdir(LOG_ROOT)
if not os.path.exists(SAVE_ROOT):
os.mkdir(SAVE_ROOT)
cls.weights_file = os.path.join(SAVE_ROOT, 'weights.h5')
cls.params_file = os.path.join(SAVE_ROOT, 'params.json')
cls.preprocessor_file = os.path.join(SAVE_ROOT, 'preprocessor.pickle')
def setUp(self):
# Load datasets.
train_path = os.path.join(DATA_ROOT, 'train.txt')
valid_path = os.path.join(DATA_ROOT, 'valid.txt')
self.x_train, self.y_train = load_data_and_labels(train_path)
self.x_valid, self.y_valid = load_data_and_labels(valid_path)
# Fit transformer.
self.p = IndexTransformer()
self.p.fit(self.x_train, self.y_train)
# Build a model.
self.model = BiLSTMCRF(char_vocab_size=self.p.char_vocab_size,
word_vocab_size=self.p.word_vocab_size,
num_labels=self.p.label_size)
self.model, loss = self.model.build()
self.model.compile(loss=loss, optimizer='adam')
def test_train(self):
trainer = Trainer(self.model, preprocessor=self.p)
trainer.train(self.x_train,
self.y_train,
x_valid=self.x_valid,
y_valid=self.y_valid)
def test_train_no_valid(self):
trainer = Trainer(self.model, preprocessor=self.p)
trainer.train(self.x_train, self.y_train)
def test_train_no_crf(self):
model = BiLSTMCRF(char_vocab_size=self.p.char_vocab_size,
word_vocab_size=self.p.word_vocab_size,
num_labels=self.p.label_size,
use_crf=False)
model, loss = model.build()
model.compile(loss=loss, optimizer='adam')
trainer = Trainer(model, preprocessor=self.p)
trainer.train(self.x_train,
self.y_train,
x_valid=self.x_valid,
y_valid=self.y_valid)
def test_train_no_character(self):
p = IndexTransformer(use_char=False)
p.fit(self.x_train, self.y_train)
model = BiLSTMCRF(word_vocab_size=p.word_vocab_size,
num_labels=p.label_size,
use_crf=False,
use_char=False)
model, loss = model.build()
model.compile(loss=loss, optimizer='adam')
trainer = Trainer(model, preprocessor=p)
trainer.train(self.x_train,
self.y_train,
x_valid=self.x_valid,
y_valid=self.y_valid)
def test_save(self):
# Train the model.
trainer = Trainer(self.model, preprocessor=self.p)
trainer.train(self.x_train, self.y_train)
# Save the model.
save_model(self.model, self.weights_file, self.params_file)
self.p.save(self.preprocessor_file)
def training(train, test, fold):
x_train = [x.split() for x in train['sentence'].tolist()]
y_train = train['tag'].tolist()
x_test = [x.split() for x in test['sentence'].tolist()]
print('Transforming datasets...')
p = IndexTransformer(use_char=True)
p.fit(x_train + x_test, y_train)
skf = KFold(n_splits=config.nfolds, random_state=config.seed, shuffle=True)
embeddings = load_glove(config.glove_file)
# embeddings_fast = load_glove(config.glove_file)
embeddings_wang = load_glove(config.wang_file)
embeddings = filter_embeddings(embeddings, p._word_vocab.vocab,
config.glove_size)
# embeddings_fast = filter_embeddings(embeddings_fast, p._word_vocab.vocab, config.fasttext_size)
embeddings_wang = filter_embeddings(embeddings_wang, p._word_vocab.vocab,
config.wang_size)
embeddings = np.concatenate((embeddings, embeddings_wang), axis=1)
for n_fold, (train_indices, val_indices) in enumerate(skf.split(x_train)):
if n_fold >= fold:
print("Training fold: ", n_fold)
x_val = list(np.array(x_train)[val_indices])
y_val = list(np.array(y_train)[val_indices])
x_train_spl = list(np.array(x_train)[train_indices])
y_train_spl = list(np.array(y_train)[train_indices])
model = BiLSTMCRF(char_vocab_size=p.char_vocab_size,
word_vocab_size=p.word_vocab_size,
num_labels=p.label_size,
word_embedding_dim=1200,
char_embedding_dim=50,
word_lstm_size=300,
char_lstm_size=300,
fc_dim=50,
dropout=0.5,
embeddings=embeddings,
use_char=True,
use_crf=True)
opt = Adam(lr=0.001)
model, loss = model.build()
model.compile(loss=loss,
optimizer=opt,
metrics=[crf_viterbi_accuracy])
es = EarlyStopping(monitor='val_crf_viterbi_accuracy',
patience=3,
verbose=1,
mode='max',
restore_best_weights=True)
rlr = ReduceLROnPlateau(monitor='val_crf_viterbi_accuracy',
factor=0.2,
patience=2,
verbose=1,
mode='max')
callbacks = [es, rlr]
train_seq = NERSequence(x_train_spl, y_train_spl,
config.batch_size, p.transform)
if x_val and y_val:
valid_seq = NERSequence(x_val, y_val, config.batch_size,
p.transform)
f1 = F1score(valid_seq, preprocessor=p, fold=n_fold)
callbacks.append(f1)
model.fit_generator(generator=train_seq,
validation_data=valid_seq,
epochs=config.nepochs,
callbacks=callbacks,
verbose=True,
shuffle=True,
use_multiprocessing=True,
workers=12)
p.save('../models/best_transform.it')
model.load_weights('../models/best_model_' + str(n_fold) + '.h5')
predict(model, p, x_test, n_fold)