Function test_models_with_mean_functions_changes

tests/gpflow/test_functions.py:291–374

Simply check that all models have a higher prediction with a constant mean function than with a zero mean function. For compositions of mean functions check that multiplication/ addition of a constant results in a higher prediction, whereas addition of zero/ mutliplication with

(model_class: Type[Any])

@pytest.mark.parametrize("model_class", _model_classes)
def test_models_with_mean_functions_changes(model_class: Type[Any]) -> None:
    """
    Simply check that all models have a higher prediction with a constant mean
    function than with a zero mean function.

    For compositions of mean functions check that multiplication/ addition of
    a constant results in a higher prediction, whereas addition of zero/
    mutliplication with one does not.
    """
    data = rng.randn(Datum.N, Datum.input_dim), rng.randn(Datum.N, 1)
    Xnew = rng.randn(Datum.Ntest, Datum.input_dim)
    inducing_variable = InducingPoints(Z=rng.randn(Datum.M, Datum.input_dim))
    kernel = gpflow.kernels.Matern32()
    likelihood = gpflow.likelihoods.Gaussian()
    zero_mean = Zero()
    non_zero_mean = Constant(c=np.ones(1) * 10)

    if model_class in [gpflow.models.GPR]:
        model_zero_mean = model_class(data, kernel=kernel, mean_function=zero_mean)
        model_non_zero_mean = model_class(data, kernel=kernel, mean_function=non_zero_mean)
    elif model_class in [gpflow.models.VGP]:
        model_zero_mean = model_class(
            data, likelihood=likelihood, kernel=kernel, mean_function=zero_mean
        )
        model_non_zero_mean = model_class(
            data, likelihood=likelihood, kernel=kernel, mean_function=non_zero_mean
        )
    elif model_class in [gpflow.models.SVGP]:
        model_zero_mean = model_class(
            kernel=kernel,
            likelihood=likelihood,
            inducing_variable=inducing_variable,
            mean_function=zero_mean,
        )
        model_non_zero_mean = model_class(
            kernel=kernel,
            likelihood=likelihood,
            inducing_variable=inducing_variable,
            mean_function=non_zero_mean,
        )
    elif model_class in [gpflow.models.SGPR, gpflow.models.GPRFITC]:
        model_zero_mean = model_class(
            data,
            kernel=kernel,
            inducing_variable=inducing_variable,
            mean_function=zero_mean,
        )
        model_non_zero_mean = model_class(
            data,
            kernel=kernel,
            inducing_variable=inducing_variable,
            mean_function=non_zero_mean,
        )
    elif model_class in [gpflow.models.SGPMC]:
        model_zero_mean = model_class(
            data,
            kernel=kernel,
            likelihood=likelihood,