def METHOD_NAME(problem_type, data_type, get_test_data_from_configuration):
    X, y = get_test_data_from_configuration(
        data_type,
        problem_type,
        column_names=["numerical"],
        scale=10,
    )
    problem_configuration = None
    if is_time_series(problem_type):
        problem_configuration = {"gap": 1, "max_delay": 7, "time_index": "date"}
        test_pct = 0.1
    else:
        test_pct = 0.2
    X_train, X_test, y_train, y_test = split_data(
        X,
        y,
        problem_type=problem_type,
        problem_configuration=problem_configuration,
    )
    test_size = len(X) * test_pct
    train_size = len(X) - test_size
    assert len(X_train) == train_size
    assert len(X_test) == test_size
    assert len(y_train) == train_size
    assert len(y_test) == test_size
    if is_time_series(problem_type):
        if not isinstance(X, pd.DataFrame):
            X = pd.DataFrame(X)
            y = pd.Series(y)
        pd.testing.assert_frame_equal(X_test, X[int(train_size) :], check_dtype=False)
        pd.testing.assert_series_equal(y_test, y[int(train_size) :], check_dtype=False)