def get_some_instance(file_path=MIMICP.mimic_data_path, seq_num=MIMICP.seq_num, num=10):
train_x = CsvUtility.read_sparse_array_from_csv(file_path, 'sparse_formal_train_x_seq.npz')
train_x = train_x.reshape((train_x.shape[0], seq_num, -1))
valid_x = CsvUtility.read_sparse_array_from_csv(file_path, 'sparse_formal_valid_x_seq.npz')
valid_x = valid_x.reshape((valid_x.shape[0], seq_num, -1))
test_x = CsvUtility.read_sparse_array_from_csv(file_path, 'sparse_formal_test_x_seq.npz')
test_x = test_x.reshape((test_x.shape[0], seq_num, -1))
train_y = CsvUtility.read_array_from_csv(file_path, 'formal_train_y_seq.csv')
valid_y = CsvUtility.read_array_from_csv(file_path, 'formal_valid_y_seq.csv')
test_y = CsvUtility.read_array_from_csv(file_path, 'formal_test_y_seq.csv')
x_data = np.concatenate((train_x, valid_x, test_x), axis=0)
y_data = np.concatenate((train_y, valid_y, test_y), axis=0)
idx = np.random.permutation(x_data.shape[0])
x_data = x_data[idx]
y_data = y_data[idx]
return x_data[:num], y_data[:num]
def reload_mimic_seq(train_percent=MIMICP.train_percent, valid=False, file_path=MIMICP.mimic_data_path, seq_num=MIMICP.seq_num):
train_x = CsvUtility.read_sparse_array_from_csv(file_path, 'sparse_formal_train_x_seq.npz')
train_x = train_x.reshape((train_x.shape[0], seq_num, -1))
valid_x = CsvUtility.read_sparse_array_from_csv(file_path, 'sparse_formal_valid_x_seq.npz')
valid_x = valid_x.reshape((valid_x.shape[0], seq_num, -1))
test_x = CsvUtility.read_sparse_array_from_csv(file_path, 'sparse_formal_test_x_seq.npz')
test_x = test_x.reshape((test_x.shape[0], seq_num, -1))
train_y = CsvUtility.read_array_from_csv(file_path, 'formal_train_y_seq.csv')
valid_y = CsvUtility.read_array_from_csv(file_path, 'formal_valid_y_seq.csv')
test_y = CsvUtility.read_array_from_csv(file_path, 'formal_test_y_seq.csv')
if valid:
test_x = valid_x
test_y = valid_y
else:
train_x = np.concatenate((train_x, valid_x), axis=0)
train_y = np.concatenate((train_y, valid_y), axis=0)
if train_percent < 0.8:
new_training_size = int((train_x.shape[0] + test_x.shape[0]) * train_percent)
train_x = train_x[:new_training_size]
train_y = train_y[:new_training_size]
return train_x, train_y, test_x, test_y
