def train(model_to_train, model_base, lookup, oov_embedding, train_path, dev_path, window_size, data_name, overwrite):
logger.info("Loading CoNLL data.")
word_sentences_train, pos_sentences_train, word_alphabet, pos_alphabet = processor.read_conll(train_path)
training_alphabet_output = os.path.join(model_base, data_name)
ensure_dir(training_alphabet_output)
word_alphabet.save(training_alphabet_output, 'training_words')
logger.info("Sliding window on the data.")
x_train = processor.slide_all_sentences(word_sentences_train, word_alphabet, window_size)
y_train = processor.get_all_one_hots(pos_sentences_train, pos_alphabet)
if oov_embedding == 'random':
logger.info("Dev set word vectors are not added to alphabet.")
word_alphabet.stop_auto_grow()
x_dev, y_dev = None, None
if dev_path:
word_sentences_dev, pos_sentences_dev, _, _ = processor.read_conll(dev_path)
x_dev = processor.slide_all_sentences(word_sentences_dev, word_alphabet, window_size)
y_dev = processor.get_all_one_hots(pos_sentences_dev, pos_alphabet)
# Alphabet stop growing.
word_alphabet.stop_auto_grow()
# A embedding subset from the word alphabet.
embeddings = lookup.w2v_lookup(word_alphabet)
logger.info("Training data dimension is %s, here is a sample:" % (str(x_train.shape)))
logger.info(x_train[0])
logger.info("Label data dimension is %s, here is a sample:" % (str(y_train.shape)))
logger.info(y_train[0])
models = {}
if model_to_train == 'vanilla' or model_to_train == 'all':
model_output = get_model_directory(model_base, data_name, 'vanilla')
mlp = VanillaLabelingMlp(embeddings=embeddings, pos_dim=pos_alphabet.size(),
vocabulary_size=word_alphabet.size(), window_size=window_size)
train_model(mlp, x_train, y_train, x_dev, y_dev, pos_alphabet, word_alphabet, model_output, overwrite)
models['vanilla'] = (mlp, pos_alphabet, word_alphabet)
if model_to_train == 'auto' or model_to_train == 'all':
model_output = get_model_directory(model_base, data_name, 'auto')
mlp = AutoEmbeddingMlp(embeddings=embeddings, pos_dim=pos_alphabet.size(),
vocabulary_size=word_alphabet.size(), window_size=window_size)
train_model(mlp, x_train, y_train, x_dev, y_dev, pos_alphabet, word_alphabet, model_output, overwrite)
models['auto'] = (mlp, pos_alphabet, word_alphabet)
return models
def test(trained_models, lookup, oov_embedding, test_conll, window_size):
logger.info("Testing condition - [OOV Vector] : %s ; [Test Data] : %s ." % (oov_embedding, test_conll))
for model_name, (model, pos_alphabet, train_alphabet) in trained_models.iteritems():
alphabet_for_test = train_alphabet.get_copy()
if oov_embedding == "pretrained":
alphabet_for_test.restart_auto_grow()
elif oov_embedding == "random":
alphabet_for_test.stop_auto_grow()
original_alphabet_size = alphabet_for_test.size()
logger.info("Original alphabet size is %d" % original_alphabet_size)
word_sentences_test, pos_sentences_test, _, _ = processor.read_conll(test_conll)
x_test = processor.slide_all_sentences(word_sentences_test, alphabet_for_test, window_size)
y_test = processor.get_all_one_hots(pos_sentences_test, pos_alphabet)
test_model = model
if oov_embedding == "pretrained":
logger.info("New alphabet size is %d" % alphabet_for_test.size())
# A new embedding using the extended word alphabet.
new_embeddings = lookup.w2v_lookup(alphabet_for_test)
additional_embeddings = new_embeddings[original_alphabet_size:]
logger.info("New embedding size is %d" % len(additional_embeddings))
test_model = model.augment_embedding(additional_embeddings)
evaluate_result = test_model.test(x_test, y_test)
try:
result_str = ", ".join("%.4f" % f for f in evaluate_result)
except TypeError:
result_str = "%.4f" % evaluate_result
logger.info("Direct test results are [%s] by model %s." % (result_str, model_name))
def test(trained_models, label_alphabet, lookup, oov_embedding, test_conll,
window_size):
logger.info("Testing condition - [OOV Vector] : %s ; [Test Data] : %s ." %
(oov_embedding, test_conll))
for model_name, (model, embedding_alphabet) in trained_models.iteritems():
alphabet_for_test = embedding_alphabet.get_copy()
original_alphabet_size = alphabet_for_test.size()
logger.info(
"Original alphabet used to train the model is of size %d ." %
original_alphabet_size)
if oov_embedding == "pretrained":
alphabet_for_test.restart_auto_grow()
word_sentences_test, pos_sentences_test, _, _ = processor.read_conll(
test_conll)
x_test = processor.slide_all_sentences(word_sentences_test,
alphabet_for_test, window_size)
y_test = processor.get_all_one_hots(pos_sentences_test, label_alphabet)
logger.info("New alphabet size is %d" % alphabet_for_test.size())
# TODO we seems need to make a copy of the model.
test_model = model
if oov_embedding == "pretrained":
additional_embeddings = lookup.load_additional_embeddings(
embedding_alphabet, alphabet_for_test)
if additional_embeddings:
logger.info("New embedding size is %d" %
len(additional_embeddings))
test_model = model.augment_embedding(additional_embeddings)
evaluate_result = test_model.test(x_test, y_test)
try:
result_str = ", ".join("%.4f" % f for f in evaluate_result)
except TypeError:
result_str = "%.4f" % evaluate_result
logger.info("Direct test results are [%s] by model %s." %
(result_str, model_name))
def train(models_to_train, model_base, lookup, oov_handling, train_path,
dev_path, window_size, data_name, overwrite):
logger.info("Loading CoNLL data.")
word_sentences_train, pos_sentences_train, word_alphabet, label_alphabet = processor.read_conll(
train_path)
# Take a snapshot of the current alphabet, which only contains training words. This is useful in fine tuning.
train_alphabet = word_alphabet.get_copy()
logger.info("Sliding window on the data.")
x_train = processor.slide_all_sentences(word_sentences_train,
word_alphabet, window_size)
y_train = processor.get_all_one_hots(pos_sentences_train, label_alphabet)
label_alphabet.stop_auto_grow()
if oov_handling == 'random':
logger.info("Dev set word vectors are not added to alphabet.")
word_alphabet.stop_auto_grow()
else:
# We will add development word embeddings to the alphabet so that their weights can be used.
logger.info("Dev set word vectors will be added to alphabet.")
x_dev, y_dev = None, None
if dev_path:
word_sentences_dev, pos_sentences_dev, _, _ = processor.read_conll(
dev_path)
x_dev = processor.slide_all_sentences(word_sentences_dev,
word_alphabet, window_size)
y_dev = processor.get_all_one_hots(pos_sentences_dev, label_alphabet)
# Alphabet stop growing now anyways.
word_alphabet.stop_auto_grow()
logger.info("Training data dimension is %s, here is a sample:" %
(str(x_train.shape)))
logger.info(x_train[0])
logger.info("Training label data dimension is %s, here is a sample:" %
(str(y_train.shape)))
logger.info(y_train[0])
models = {}
for model_name in models_to_train:
lookup.initail_lookup(word_alphabet)
if model_name == 'vanilla':
model_output = os.path.join(model_base, data_name, 'vanilla')
train_x = {ExperimentConfig.main_input_name: x_train}
train_y = {ExperimentConfig.main_output_name: y_train}
dev_data = ({
ExperimentConfig.main_input_name: x_dev
}, {
ExperimentConfig.main_output_name: y_dev
})
for fix_embedding in MLPConfig.fix_embedding:
mlp = VanillaLabelingMlp(embeddings=lookup.table,
pos_dim=label_alphabet.size(),
vocabulary_size=word_alphabet.size(),
window_size=window_size,
fix_embedding=fix_embedding)
train_model(mlp, train_x, train_y, dev_data, label_alphabet,
word_alphabet, model_output, overwrite)
actual_model_name = model_name + "%s" % fix_embedding
models[actual_model_name] = (mlp, word_alphabet)
elif model_name == 'auto':
if oov_handling == 'random':
logger.info(
"We do not train the auto model when the embedding is initialized randomly."
)
continue
train_x = {ExperimentConfig.main_input_name: x_train}
y_auto_train = processor.get_center_embedding(
x_train, lookup.table)
y_auto_dev = processor.get_center_embedding(y_dev, lookup.table)
train_y = {
ExperimentConfig.main_output_name: y_train,
AutoConfig.auto_output_name: y_auto_train
}
dev_data = ({
ExperimentConfig.main_input_name: x_dev
}, {
ExperimentConfig.main_output_name: y_dev,
AutoConfig.auto_output_name: y_auto_dev
})
for auto_option in AutoConfig.auto_options:
model_output = os.path.join(model_base, data_name, 'auto',
auto_option)
mlp = AutoEmbeddingMlp(
embeddings=lookup.full_table,
pos_dim=label_alphabet.size(),
vocabulary_size=lookup.full_alphabet.size(),
window_size=window_size,
auto_option=auto_option)
train_model(mlp, train_x, train_y, dev_data, label_alphabet,
lookup.full_alphabet, model_output, overwrite)
models[model_name + "_" + auto_option] = (mlp,
lookup.full_alphabet)
else:
logger.warn("Unknown model name %s." % model_name)
continue
return models, train_alphabet, label_alphabet
