class Sequence(object):
def __init__(self,
cell_type="lstm",
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,
initial_vocab=None,
optimizer='adam'):
self.model = None
self.p = None
self.tagger = None
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.embeddings = embeddings
self.use_char = use_char
self.use_crf = use_crf
self.initial_vocab = initial_vocab
self.optimizer = optimizer
self.cell_type = cell_type
def fit(self,
x_train,
y_train,
x_valid=None,
y_valid=None,
epochs=1,
batch_size=32,
verbose=1,
callbacks=None,
shuffle=True):
"""Fit the model for a fixed number of epochs.
Args:
x_train: list of training data.
y_train: list of training target (label) data.
x_valid: list of validation data.
y_valid: list of validation target (label) data.
batch_size: Integer.
Number of samples per gradient update.
If unspecified, `batch_size` will default to 32.
epochs: Integer. Number of epochs to train the model.
verbose: Integer. 0, 1, or 2. Verbosity mode.
0 = silent, 1 = progress bar, 2 = one line per epoch.
callbacks: List of `keras.callbacks.Callback` instances.
List of callbacks to apply during training.
shuffle: Boolean (whether to shuffle the training data
before each epoch). `shuffle` will default to True.
"""
p = IndexTransformer(initial_vocab=self.initial_vocab,
use_char=self.use_char)
p.fit(x_train, y_train)
embeddings = filter_embeddings(self.embeddings, p._word_vocab.vocab,
self.word_embedding_dim)
model = BiRNNCRF(cell_type=self.cell_type,
char_vocab_size=p.char_vocab_size,
word_vocab_size=p.word_vocab_size,
num_labels=p.label_size,
word_embedding_dim=self.word_embedding_dim,
char_embedding_dim=self.char_embedding_dim,
word_lstm_size=self.word_lstm_size,
char_lstm_size=self.char_lstm_size,
fc_dim=self.fc_dim,
dropout=self.dropout,
embeddings=embeddings,
use_char=self.use_char,
use_crf=self.use_crf)
model, loss = model.build()
model.compile(loss=loss, optimizer=self.optimizer)
# print parameters
import numpy as np
params = model.trainable_weights
total_size = 0
for v in params:
if 'word_embedding' in v.name: continue
v_size = np.prod(np.array(v.shape.as_list())).tolist()
total_size += v_size
print("Total trainable variables size: %d" % total_size)
return
trainer = Trainer(model, preprocessor=p)
trainer.train(x_train,
y_train,
x_valid,
y_valid,
epochs=epochs,
batch_size=batch_size,
verbose=verbose,
callbacks=callbacks,
shuffle=shuffle)
self.p = p
self.model = model
def predict(self, x_test):
"""Returns the prediction of the model on the given test data.
Args:
x_test : array-like, shape = (n_samples, sent_length)
Test samples.
Returns:
y_pred : array-like, shape = (n_smaples, sent_length)
Prediction labels for x.
"""
if self.model:
lengths = map(len, x_test)
x_test = self.p.transform(x_test)
y_pred = self.model.predict(x_test)
y_pred = self.p.inverse_transform(y_pred, lengths)
return y_pred
else:
raise OSError('Could not find a model. Call load(dir_path).')
def score(self, x_test, y_test):
"""Returns the f1-micro score on the given test data and labels.
Args:
x_test : array-like, shape = (n_samples, sent_length)
Test samples.
y_test : array-like, shape = (n_samples, sent_length)
True labels for x.
Returns:
score : float, f1-micro score.
"""
if self.model:
x_test = self.p.transform(x_test)
lengths = map(len, y_test)
y_pred = self.model.predict(x_test)
y_pred = self.p.inverse_transform(y_pred, lengths)
score = f1_score(y_test, y_pred)
return score
else:
raise OSError('Could not find a model. Call load(dir_path).')
def analyze(self, text, tokenizer=str.split):
"""Analyze text and return pretty format.
Args:
text: string, the input text.
tokenizer: Tokenize input sentence. Default tokenizer is `str.split`.
Returns:
res: dict.
"""
if not self.tagger:
self.tagger = Tagger(self.model,
preprocessor=self.p,
tokenizer=tokenizer)
return self.tagger.analyze(text)
def save(self, weights_file, params_file, preprocessor_file):
self.p.save(preprocessor_file)
save_model(self.model, weights_file, params_file)
@classmethod
def load(cls, weights_file, params_file, preprocessor_file):
self = cls()
self.p = IndexTransformer.load(preprocessor_file)
self.model = load_model(weights_file, params_file)
return self
def extract_named_entities(self, str):
tagger = Tagger(self._model, preprocessor=self.dataset.pp)
return tagger.analyze(str.split())
