def test_multiclass_train_val_data(self, get_multiclass_data):
    (X, y) = get_multiclass_data
    y = np.random.choice(['hat', 'bowl', 'junky', 'riding', 'happy'], size=y.shape[0])
    data_processor = DataProcessors.multiclass
    data = data_processor().process_train_val_data(X, y, val_size=0.3)
    check_add_bias(X, data['X_train']['data'], test=self._test)
    check_add_bias(X, data['X_val']['data'], test=self._test)
    check_split(X, y, data['X_train']['data'], data['y_train']['data'], data['X_val']['data'], data['y_val']['data'], test=self._test)
    check_one_hot_label_encoder(data['y_train']['data'], test=self._test)
    check_one_hot_label_encoder(data['y_val']['data'], test=self._test)
def test_multiclass_train_val_data(self, get_multiclass_data):
    (X, y) = get_multiclass_data
    y = np.random.choice(['hat', 'bowl', 'junky', 'riding', 'happy'], size=y.shape[0])
    data_processor = DataProcessors.multiclass
    data = data_processor().process_train_val_data(X, y, val_size=0.3)
    check_add_bias(X, data['X_train']['data'], test=self._test)
    check_add_bias(X, data['X_val']['data'], test=self._test)
    check_split(X, y, data['X_train']['data'], data['y_train']['data'], data['X_val']['data'], data['y_val']['data'], test=self._test)
    check_one_hot_label_encoder(data['y_train']['data'], test=self._test)
    check_one_hot_label_encoder(data['y_val']['data'], test=self._test)