def test_create_precomputed_info(self):
num_samples = 20
x = np.array([[1, 2], [4, 5], [6, 7], [8, 9], [10, 11]])
noise_prior = Gaussian(mu=np.log(0.01), sigma=1)
builder = HellingerDistanceBuilder(noise_prior,
num_samples,
max_num_hyperparameters=40,
max_num_kernels=10,
active_models=MagicMock(),
initial_model_indices=MagicMock(),
data_X=x)
cov = Covariance(RBF(1))
p1 = LogGaussian(20, 1)
p2 = LogGaussian(0, 1.1)
cov.raw_kernel.variance.set_prior(p1, warning=False)
cov.raw_kernel.lengthscale.set_prior(p2, warning=False)
result = builder.create_precomputed_info(cov, x)
self.assertIsInstance(result, tuple)
self.assertEqual(len(result), 2)
self.assertIsInstance(result[0], np.ndarray)
self.assertIsInstance(result[1], np.ndarray)
self.assertEqual(result[0].shape, (num_samples, ))
self.assertEqual(result[1].shape,
(x.shape[0], x.shape[0], num_samples))
def test_hellinger_distance(self):
cov_i = Covariance(RBF(1))
p1 = LogGaussian(20, 1)
p2 = LogGaussian(0, 1.1)
cov_i.raw_kernel.variance.set_prior(p1, warning=False)
cov_i.raw_kernel.lengthscale.set_prior(p2, warning=False)
cov_j = Covariance(RationalQuadratic(1))
p3 = LogGaussian(11, 2)
p4 = LogGaussian(2, 1.12)
cov_j.raw_kernel.variance.set_prior(p3, warning=False)
cov_j.raw_kernel.lengthscale.set_prior(p4, warning=False)
cov_j.raw_kernel.power.set_prior(p2, warning=False)
x = np.array([[1, 2], [4, 5], [6, 7], [8, 9], [10, 11]])
noise_prior = Gaussian(mu=np.log(0.01), sigma=1)
builder = HellingerDistanceBuilder(noise_prior,
num_samples=20,
max_num_hyperparameters=40,
max_num_kernels=10,
active_models=MagicMock(),
initial_model_indices=MagicMock(),
data_X=x)
log_det_i, mini_gram_matrices_i = builder.create_precomputed_info(
cov_i, x)
log_det_j, mini_gram_matrices_j = builder.create_precomputed_info(
cov_j, x)
result = HellingerDistanceBuilder.hellinger_distance(
log_det_i, mini_gram_matrices_i, log_det_j, mini_gram_matrices_j)
self.assertIsInstance(result, float)
def test_prior_log_gaussian_fast(self):
prior_1 = LogGaussian(mu=0.5, sigma=2)
prior_2 = LogGaussian(mu=0.7, sigma=2.2)
prior_list = np.array([prior_1, prior_2])
samples = probability_samples()
result = prior_sample_log_gaussian(prior_list, samples)
self.assertIsInstance(result, np.ndarray)
self.assertEqual(result.shape, (samples.shape[0], prior_list.size))
def setUp(self) -> None:
self.x = np.array([[1, 2], [4, 5], [6, 7], [8, 9], [10, 11]])
self.noise_prior = Gaussian(mu=np.log(0.01), sigma=1)
self.cov_i = RBF(1)
p1 = LogGaussian(20, 1)
p2 = LogGaussian(0, 1.1)
self.cov_i.variance.set_prior(p1, warning=False)
self.cov_i.lengthscale.set_prior(p2, warning=False)
models = [GPModel(self.cov_i)]
self.active_models = ActiveSet(max_n_models=3)
self.active_models.models = models
self.ind_init = [0]
def setUp(self) -> None:
self.x = np.array([[1, 2], [4, 5], [6, 7], [8, 9], [10, 11]])
self.noise_prior = Gaussian(mu=np.log(0.01), sigma=1)
cov_1 = Covariance(RBF(1))
p1 = LogGaussian(20, 1)
p2 = LogGaussian(0, 1.1)
cov_1.raw_kernel.variance.set_prior(p1, warning=False)
cov_1.raw_kernel.lengthscale.set_prior(p2, warning=False)
cov_2 = Covariance(RBF(1))
p3 = LogGaussian(11, 1)
p4 = LogGaussian(1, 1.21)
cov_2.raw_kernel.variance.set_prior(p3, warning=False)
cov_2.raw_kernel.lengthscale.set_prior(p4, warning=False)
cov_3 = Covariance(RationalQuadratic(1))
p5 = LogGaussian(4, 1)
p6 = LogGaussian(1.2, 1.21)
p7 = LogGaussian(13, 1.21)
cov_3.raw_kernel.variance.set_prior(p5, warning=False)
cov_3.raw_kernel.lengthscale.set_prior(p6, warning=False)
cov_3.raw_kernel.power.set_prior(p7, warning=False)
models = [GPModel(cov_1), GPModel(cov_2), GPModel(cov_3)]
self.active_models = ActiveSet(max_n_models=3)
self.active_models.models = models
self.ind_init = [0, 2]
def test_get_priors(self):
# Test assertRaises ValueError if not all parameters have priors set
c = RBF(1)
self.assertRaises(ValueError, get_priors, c)
c = RBF(1) * RationalQuadratic(1)
p1 = LogGaussian(2, 2.21)
p2 = LogGaussian(1, 2.1)
p3 = LogGaussian(1, 2)
c.parameters[0].variance.set_prior(p1, warning=False)
c.parameters[1].lengthscale.set_prior(p2, warning=False)
c.parameters[0].lengthscale.set_prior(p3, warning=False)
c.parameters[1].power.set_prior(p2, warning=False)
c.parameters[1].variance.set_prior(p1, warning=False)
result = get_priors(c)
self.assertIsInstance(result, np.ndarray)
self.assertEqual(result.shape, (5, ))
np.testing.assert_array_equal(result, [p1, p3, p1, p2, p2])