Exemple #1
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def test_compound_kernel_input_type():
    kernel = CompoundKernel([WhiteKernel(noise_level=3.0)])
    assert not kernel.requires_vector_input

    kernel = CompoundKernel([WhiteKernel(noise_level=3.0),
                             RBF(length_scale=2.0)])
    assert kernel.requires_vector_input
Exemple #2
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 def kernel_(self):
     if self.n_classes_ == 2:
         return self.base_estimator_.kernel_
     else:
         return CompoundKernel(
             [estimator.kernel_
              for estimator in self.base_estimator_.estimators_])
Exemple #3
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def test_guess_priors():
    """Construct a complicated kernel and check if priors are constructed
    correctly."""
    kernel = Exponentiation(
        ConstantKernel(constant_value_bounds="fixed") * Matern() +
        WhiteKernel() + CompoundKernel([RBF(), Matern()]),
        2.0,
    )

    priors = guess_priors(kernel)

    assert len(priors) == 4
    expected = [
        -1.737085713764618,
        -4.107091211892862,
        -1.737085713764618,
        -1.737085713764618,
    ]
    for p, v in zip(priors, expected):
        assert_almost_equal(p(0.0), v)
Exemple #4
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        "Increasing the bound and calling "
        "fit again may find a better value."
    )

    assert (
        record[1].message.args[0]
        == "The optimal value found for "
        "dimension 1 of parameter "
        "length_scale is close to the "
        "specified upper bound 100.0. "
        "Increasing the bound and calling "
        "fit again may find a better value."
    )


@pytest.mark.parametrize(
    "params, error_type, err_msg",
    [
        (
            {"kernel": CompoundKernel(0)},
            ValueError,
            "kernel cannot be a CompoundKernel",
        )
    ],
)
def test_gpc_fit_error(params, error_type, err_msg):
    """Check that expected error are raised during fit."""
    gpc = GaussianProcessClassifier(**params)
    with pytest.raises(error_type, match=err_msg):
        gpc.fit(X, y)