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