def main():
start_time = time.time()
random.seed(42)
# os.environ["CUDA_VISIBLE_DEVICES"] = '-1'
parser = argparse.ArgumentParser()
parser.add_argument('--mode', type=int, help='Preprocess or execute the data.', default=None)
args = vars(parser.parse_args()) # Convert the arguments to a dict
if args['mode'] == 1:
train = pd.read_csv('../data/proppy_1.0.train.tsv', sep='\t', header=None)
train_processed = Preprocessing.pipeline(train[train.columns[0]])
train_processed_df = pd.DataFrame(columns=['text_stem', 'text_join', 'text', 'label'])
train_processed_df['text_stem'], train_processed_df['text_join'] = train_processed
train_processed_df['label'] = train[train.columns[len(train.columns) - 1]]
train_processed_df['text'] = Preprocessing.pipeline_simple(train[train.columns[0]])
# train_processed_df['embedding'] = train_embeddings
train_processed_df.to_csv('../data/train_preprocessed.tsv', sep='\t', index=False,
index_label=False)
test = pd.read_csv('../data/proppy_1.0.test.tsv', sep='\t', header=None)
test_processed = Preprocessing.pipeline(test[test.columns[0]])
test_processed_df = pd.DataFrame(columns=['text_stem', 'text_join', 'text', 'label'])
test_processed_df['text_stem'], test_processed_df['text_join'] = test_processed
test_processed_df['label'] = test[test.columns[len(test.columns) - 1]]
test_processed_df['text'] = Preprocessing.pipeline_simple(test[test.columns[0]])
# train_processed_df['embedding'] = train_embeddings
test_processed_df.to_csv('../data/test_preprocessed.tsv', sep='\t', index=False,
index_label=False)
dev = pd.read_csv('../data/proppy_1.0.dev.tsv', sep='\t', header=None)
dev_processed = Preprocessing.pipeline(dev[dev.columns[0]])
dev_processed_df = pd.DataFrame(columns=['text_stem', 'text_join', 'text', 'label'])
dev_processed_df['text_stem'], dev_processed_df['text_join'] = dev_processed
dev_processed_df['label'] = dev[dev.columns[len(dev.columns) - 1]]
dev_processed_df['text'] = Preprocessing.pipeline_simple(dev[dev.columns[0]])
# train_processed_df['embedding'] = train_embeddings
dev_processed_df.to_csv('../data/dev_preprocessed.tsv', sep='\t', index=False,
index_label=False)
elif args['mode'] == 2:
# Creación del modelo con embeddings de fasttext o glove
config = ModelConfig.AttentionConfig.value
model = AttentionModel(batch_size=config['batch_size'], epochs=config['epochs'],
vocab_size=config['vocab_size'],
max_len=config['max_len'], filters=config['filters'], kernel_size=config['kernel_size'],
optimizer=config['optimizer'], learning_rate=config['learning_rate'],
max_sequence_len=config['max_sequence_len'], lstm_units=config['lstm_units'],
embedding_size=config['embedding_size'], load_embeddings=config['load_embeddings'],
pool_size=config['pool_size'], path_train=config['path_train'],
path_test=config['path_test'], path_dev=config['path_dev'], emb_type=config['emb_type'],
buffer_size=config['buffer_size'], rate=config['rate'],
length_type=config['length_type'],
dense_units=config['dense_units'],
att_units=config['att_units']
)
model.prepare_data_as_tensors()
print('Building the model.')
model.call()
print('Previo a fit')
model.fit_as_tensors(with_validation=False)
print('Previo a predict')
model.predict_test_dev()
elif args['mode'] == 3:
# Creación del modelo con embeddings de fasttext o glove
config = ModelConfig.TrainEmbeddings.value
model = BiLSTMModel(batch_size=config['batch_size'], epochs=config['epochs'], vocab_size=config['vocab_size'],
max_len=config['max_len'], filters=config['filters'], kernel_size=config['kernel_size'],
optimizer=config['optimizer'], learning_rate=config['learning_rate'],
max_sequence_len=config['max_sequence_len'], lstm_units=config['lstm_units'],
embedding_size=config['embedding_size'], load_embeddings=config['load_embeddings'],
pool_size=config['pool_size'], path_train=config['path_train'],
path_test=config['path_test'], path_dev=config['path_dev'], emb_type=config['emb_type'],
buffer_size=config['buffer_size'], rate=config['rate'], length_type=config['length_type'],
dense_units=config['dense_units'], concat=config['concat']
)
model.prepare_data_as_tensors()
print('Building the model.')
model.call()
print('Previo a fit')
# model.fit_as_tensors(with_validation=False)
print('Previo a predict')
# model.predict_test_dev()
# print('Se guarda historial del loss:')
# model.save_plot_history()
elif args['mode'] == 4:
config = ModelConfig.SecondExperiment.value
model = BiLSTMModel(batch_size=config['batch_size'], epochs=config['epochs'], vocab_size=config['vocab_size'],
max_len=config['max_len'], filters=config['filters'], kernel_size=config['kernel_size'],
optimizer=config['optimizer'], learning_rate=config['learning_rate'],
max_sequence_len=config['max_sequence_len'], lstm_units=config['lstm_units'],
embedding_size=config['embedding_size'], load_embeddings=config['load_embeddings'],
pool_size=config['pool_size'], path_train=config['path_train'],
path_test=config['path_test'], path_dev=None, emb_type=config['emb_type'],
buffer_size=config['buffer_size'], rate=config['rate'], length_type=config['length_type'],
dense_units=config['dense_units'], concat=config['concat']
)
model.prepare_data_as_tensors()
print('Building the model.')
model.call()
print('Previo a fit')
model.fit_as_tensors(with_validation=False)
print('Previo a predict')
model.predict()
elif args['mode'] == 5:
config = ModelConfig.BertConfig.value
model = BertModel(max_len=config['max_len'], path_train=config['path_train'], path_test=config['path_test'],
path_dev=config['path_dev'], epochs=config['epochs'], optimizer=config['optimizer'],
load_embeddings=False, batch_size=config['batch_size'],
max_sequence_len=config['max_sequence_len'],
rate=config['rate'], learning_rate=config['learning_rate'], length_type=config['length_type']
)
print('Loading the data.')
model.load_data()
print('Creating the model.')
model.call()
print('Fitting the model.')
# model.fit(with_validation=True)
print('Predict the test set.')
# model.predict()
elif args['mode'] == 6:
config = ModelConfig.TransformerConfig.value
model = TransformerModel(batch_size=config['batch_size'], epochs=config['epochs'],
vocab_size=config['vocab_size'],
max_len=config['max_len'], filters=config['filters'],
kernel_size=config['kernel_size'],
optimizer=config['optimizer'], learning_rate=config['learning_rate'],
max_sequence_len=config['max_sequence_len'], lstm_units=config['lstm_units'],
embedding_size=config['embedding_size'], load_embeddings=config['load_embeddings'],
pool_size=config['pool_size'], path_train=config['path_train'],
path_test=config['path_test'], path_dev=config['path_dev'],
emb_type=config['emb_type'],
buffer_size=config['buffer_size'], rate=config['rate'],
length_type=config['length_type'], dense_units=config['dense_units'],
attheads=config['attheads'], att_layers=config['att_layers']
)
model.prepare_data_as_tensors()
print('Building the model.')
model.call()
print('Previo a fit')
model.fit_as_tensors(with_validation=True)
print('Previo a predict')
model.predict()
elif args['mode'] == 7:
# Creación del modelo con embeddings de fasttext o glove
config = ModelConfig.MeanModelConfig.value
model = LocalAttentionModel(batch_size=config['batch_size'], epochs=config['epochs'],
vocab_size=config['vocab_size'],
max_len=config['max_len'], filters=config['filters'],
kernel_size=config['kernel_size'],
optimizer=config['optimizer'], learning_rate=config['learning_rate'],
max_sequence_len=config['max_sequence_len'], lstm_units=config['lstm_units'],
embedding_size=config['embedding_size'], load_embeddings=config['load_embeddings'],
pool_size=config['pool_size'], path_train=config['path_train'],
path_test=config['path_test'], path_dev=config['path_dev'],
emb_type=config['emb_type'],
buffer_size=config['buffer_size'], rate=config['rate'],
length_type=config['length_type'],
dense_units=config['dense_units']
)
model.prepare_data_as_tensors()
print('Building the model.')
model.call()
print('Previo a fit')
model.fit_as_tensors(with_validation=False)
print('Previo a predict')
model.predict_test_dev()
print('Se muestra la atención:')
# model.plot_attention()
elif args['mode'] == 8:
config = ModelConfig.SecondExperiment.value
model = AttentionModel(batch_size=config['batch_size'], epochs=config['epochs'],
vocab_size=config['vocab_size'],
max_len=config['max_len'], filters=config['filters'], kernel_size=config['kernel_size'],
optimizer=config['optimizer'], learning_rate=config['learning_rate'],
max_sequence_len=config['max_sequence_len'], lstm_units=config['lstm_units'],
embedding_size=config['embedding_size'], load_embeddings=config['load_embeddings'],
pool_size=config['pool_size'], path_train=config['path_train'],
path_test=config['path_test'], path_dev=None, emb_type=config['emb_type'],
buffer_size=config['buffer_size'], rate=config['rate'],
length_type=config['length_type'],
dense_units=config['dense_units']
)
model.prepare_data_as_tensors()
print('Building the model.')
model.call()
print('Previo a fit')
model.fit_as_tensors(with_validation=True)
print('Previo a predict')
model.predict()
elif args['mode'] == 9:
config = ModelConfig.AttentionConfig.value
model = AttentionModel(batch_size=config['batch_size'], epochs=config['epochs'],
vocab_size=config['vocab_size'],
max_len=config['max_len'], filters=config['filters'], kernel_size=config['kernel_size'],
optimizer=config['optimizer'], learning_rate=config['learning_rate'],
max_sequence_len=config['max_sequence_len'], lstm_units=config['lstm_units'],
embedding_size=config['embedding_size'], load_embeddings=config['load_embeddings'],
pool_size=config['pool_size'], path_train=config['path_train'],
path_test=config['path_test'], path_dev=config['path_dev'], emb_type=config['emb_type'],
buffer_size=config['buffer_size'], rate=config['rate'],
length_type=config['length_type'],
dense_units=config['dense_units'], both_embeddings=config['both_embeddings'],
att_units=config['att_units']
)
model.prepare_data_as_tensors()
print('Building the model.')
model.call()
print('Previo a fit')
model.fit_as_tensors(with_validation=False)
print('Previo a predict')
model.predict_test_dev()
elif args['mode'] == 10:
config = ModelConfig.BertConfigSecondExp.value
model = BertModel(max_len=config['max_len'], path_train=config['path_train'], path_test=config['path_test'],
epochs=config['epochs'], optimizer=config['optimizer'],
load_embeddings=False, batch_size=config['batch_size'],
max_sequence_len=config['max_sequence_len'],
rate=config['rate'], learning_rate=config['learning_rate'], length_type=config['length_type']
)
print('Loading the data.')
model.load_data()
print('Creating the model.')
model.call()
print('Fitting the model.')
model.fit(with_validation=False)
print('Predict the test set.')
model.predict()
elif args['mode'] == 11:
config = ModelConfig.SecondExperiment.value
model = LocalAttentionModel(batch_size=config['batch_size'], epochs=config['epochs'],
vocab_size=config['vocab_size'],
max_len=config['max_len'], filters=config['filters'],
kernel_size=config['kernel_size'],
optimizer=config['optimizer'], learning_rate=config['learning_rate'],
max_sequence_len=config['max_sequence_len'], lstm_units=config['lstm_units'],
embedding_size=config['embedding_size'], load_embeddings=config['load_embeddings'],
pool_size=config['pool_size'], path_train=config['path_train'],
path_test=config['path_test'], path_dev=None, emb_type=config['emb_type'],
buffer_size=config['buffer_size'], rate=config['rate'],
length_type=config['length_type'],
dense_units=config['dense_units']
)
model.prepare_data_as_tensors()
print('Building the model.')
model.call()
print('Previo a fit')
model.fit_as_tensors(with_validation=False)
print('Previo a predict')
model.predict()
elif args['mode'] == 12:
config = ModelConfig.MeanModelConfig.value
model = LocalAttentionModelNela(batch_size=config['batch_size'], epochs=config['epochs'],
vocab_size=config['vocab_size'],
max_len=config['max_len'], filters=config['filters'],
kernel_size=config['kernel_size'],
optimizer=config['optimizer'], learning_rate=config['learning_rate'],
max_sequence_len=config['max_sequence_len'], lstm_units=config['lstm_units'],
embedding_size=config['embedding_size'],
load_embeddings=config['load_embeddings'],
pool_size=config['pool_size'], path_train=config['path_train'],
path_test=config['path_test'], path_dev=config['path_dev'],
emb_type=config['emb_type'],
buffer_size=config['buffer_size'], rate=config['rate'],
length_type=config['length_type'],
dense_units=config['dense_units']
)
model.prepare_data()
print('Building the model.')
model.call()
print('Previo a fit')
model.fit(with_validation=False)
print('Previo a predict')
model.predict_test_dev()
else:
print('No other mode implemented yed.')
elapsed_time = time.time() - start_time
print('The execution took: ' + str(elapsed_time) + ' seconds.')
print('End of execution.')
start_index = random.randint(0, len(raw_text) - seq_length - 1)
for diversity in [0.2, 0.5, 1.0, 1.2]:
print('----- diversity:', diversity)
generated = ''
sentence = raw_text[start_index:start_index + seq_length]
generated += sentence
print('----- Generating with seed: "' + sentence + '"')
sys.stdout.write(generated)
for i in range(400):
x_pred = np.zeros((1, seq_length, n_vocab))
for t, char in enumerate(sentence):
x_pred[0, t, char_to_int[char]] = 1.
preds = model.predict(x_pred, verbose=0)[0]
next_index = sample(preds, diversity)
next_char = int_to_char[next_index]
sentence = sentence[1:] + next_char
sys.stdout.write(next_char)
sys.stdout.flush()
print()
# # reshape X to be [samples, time steps, features]
# X = np.reshape(dataX, (n_patterns, seq_length, 1))
# # normalize
# X = X / float(n_vocab)
# # one hot encode the output variable
# y = np_utils.to_categorical(dataY)