(cls_method, dataset_name, params))
print("Loading data...")
start = time.time()
dataset_manager = DatasetManager(dataset_name)
data = dataset_manager.read_dataset()
train, test = dataset_manager.split_data(data,
train_ratio,
split="temporal")
train = dataset_manager.get_train_sample(train, sample_size)
#train, val = dataset_manager.get_train_val_data(train, sample_size, val_sample_size)
print("Done: %s" % (time.time() - start))
print('Encoding data...')
start = time.time()
dt_train = dataset_manager.encode_data(train)
#dt_val = dataset_manager.encode_data(val)
dt_test = dataset_manager.encode_data(test)
#X, y = dataset_manager.generate_3d_data(dt_train, max_len)
#X_val, y_val = dataset_manager.generate_3d_data(dt_val, max_len)
#X_test, y_test = dataset_manager.generate_3d_data(dt_test, max_len)
#y = y[:,0,0].reshape(y.shape[0])
#y_test = y_test[:,0,0].reshape(y_test.shape[0])
print("Done: %s" % (time.time() - start))
print('Evaluating...')
start = time.time()
with open(results_file, 'w') as fout:
csv_writer = csv.writer(fout,
delimiter=';',
quotechar='"',
