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
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_])
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)
"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)