Repository: scikit-learn/scikit-learn
File: sklearn/gaussian_process/tests/test_gpc.py (Python)

Code before:


@pytest.mark.parametrize("kernel", non_fixed_kernels)
def test_converged_to_local_maximum(kernel):
    # Test that we are in local maximum after hyperparameter-optimization.
    gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)

    lml, lml_gradient = gpc.log_marginal_likelihood(gpc.kernel_.theta, True)

    assert np.all(
        (np.abs(lml_gradient) < 1e-4)
        | (gpc.kernel_.theta == gpc.kernel_.bounds[:, 0])
        | (gpc.kernel_.theta == gpc.kernel_.bounds[:, 1])
    )


@pytest.mark.xfail(raises=AssertionError)
@pytest.mark.parametrize("kernel", non_fixed_kernels[:-1])
def test_lml_gradient(kernel):
    # Clone the kernel object prior to mutating it to avoid any side effects between
    # GP tests:
    kernel = clone(kernel)
    # Compare analytic and numeric gradient of log marginal likelihood.
    gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)

    length_scales = np.logspace(0, 2, 100)

    def evaluate_grad_at_length_scales(length_scales):
        result = np.zeros_like(length_scales)
        for i, length_scale in enumerate(length_scales):
            kernel.length_scale = length_scale
            result[i] = (
                gpc.log_marginal_likelihood(kernel.theta)
                if len(kernel.theta) == 1
                else [gpc.log_marginal_likelihood([theta]) for theta in kernel.theta]
            )
        return result

    lml_gradient = np.zeros_like(length_scales)

    for i, length_scale in enumerate(length_scales):
        kernel.length_scale = length_scale
        lml_gradient[i] = gpc.log_marginal_likelihood(kernel.theta, eval_gradient=True)[
            1
        ][0]

    lml_gradient_approx = derivative(
        evaluate_grad_at_length_scales, length_scales, maxiter=20
    ).df

    assert_almost_equal(lml_gradient, lml_gradient_approx, 3)

Review comment (suggestion):
```suggestion
    # XXX: try to make this test pass without raising an error with scales
    # lower than 1.0: np.logspace(-3, 3, 100)
    length_scales = np.logspace(0, 2, 100)
```

Code after:
    input_theta = np.ones(gpc.kernel_.theta.shape, dtype=np.float64)

    gpc.log_marginal_likelihood(input_theta, clone_kernel=False)
    assert_almost_equal(gpc.kernel_.theta, input_theta, 7)


@pytest.mark.parametrize("kernel", non_fixed_kernels)
def test_converged_to_local_maximum(kernel):
    # Test that we are in local maximum after hyperparameter-optimization.
    gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)

    lml, lml_gradient = gpc.log_marginal_likelihood(gpc.kernel_.theta, True)

    assert np.all(
        (np.abs(lml_gradient) < 1e-4)
        | (gpc.kernel_.theta == gpc.kernel_.bounds[:, 0])
        | (gpc.kernel_.theta == gpc.kernel_.bounds[:, 1])
    )


@pytest.mark.xfail(
    raises=AssertionError,
    reason="https://github.com/scikit-learn/scikit-learn/issues/31366",
)
@pytest.mark.parametrize("kernel", non_fixed_kernels)
@pytest.mark.parametrize("length_scale", np.logspace(-3, 3, 13))
def test_lml_gradient(kernel, length_scale):
    # Clone the kernel object prior to mutating it to avoid any side effects between
    # GP tests:
    kernel = clone(kernel)
    length_scale_param_name = next(
        name for name in kernel.get_params() if name.endswith("length_scale")
    )
    kernel.set_params(**{length_scale_param_name: length_scale})

    # Compare analytic and numeric gradient of log marginal likelihood.
    gpr = GaussianProcessClassifier(kernel=kernel).fit(X, y)
    _, lml_gradient = gpr.log_marginal_likelihood(kernel.theta, eval_gradient=True)
    epsilon = 1e-9
    lml_gradient_approx = approx_fprime(
        kernel.theta.copy(),
        lambda theta: gpr.log_marginal_likelihood(theta, False),
        epsilon=epsilon,
    )
    assert_allclose(lml_gradient, lml_gradient_approx, rtol=1e-4, atol=epsilon * 100)


def test_random_starts(global_random_seed):
    # Test that an increasing number of random-starts of GP fitting only
    # increases the log marginal likelihood of the chosen theta.
    n_samples, n_features = 25, 2