def test_eval_mode(self):
lik = GaussianLikelihood()
lik.noise = self.noise_var
model = OnlineWoodburyGP(self.xs, self.labels, lik, self.kernel,
self.mean_module)
model.covar_module.base_kernel.lengthscale = self.lengthscale
model.eval()
pred_nn = model(self.test_points)
pred = model.likelihood(pred_nn)
r_lik = GaussianLikelihood()
r_lik.noise = self.noise_var
r_kernel = GridInterpolationKernelWithFantasy(RBFKernel(),
grid_size=self.grid_size,
grid_bounds=[(-4.0, 14.0)
]).double()
r_model = RegularExactGP(self.xs, self.labels, r_lik, r_kernel,
deepcopy(self.mean_module))
r_model.covar_module.base_kernel.lengthscale = self.lengthscale
r_model.eval()
r_pred_nn = r_model(self.test_points)
r_pred = r_model.likelihood(r_pred_nn)
np.testing.assert_allclose(r_pred.mean.detach().numpy(),
pred.mean.detach().numpy())
np.testing.assert_allclose(
r_pred.covariance_matrix.detach().numpy(),
pred.covariance_matrix.detach().numpy(),
)
def test_online_eval_mode(self):
r_lik = GaussianLikelihood()
r_lik.noise = 1.0
# r_kernel = RBFKernel().double()
r_kernel = GridInterpolationKernelWithFantasy(RBFKernel(),
grid_size=self.grid_size,
grid_bounds=[(-4.0, 14.0)
]).double()
r_model = RegularExactGP(self.xs, self.labels, r_lik, r_kernel,
deepcopy(self.mean_module))
r_model.eval()
r_model(self.new_points).mean
r_model = r_model.get_fantasy_model(self.new_points, self.new_targets)
r_test_output = r_lik(r_model(self.test_points))
lik = GaussianLikelihood()
lik.noise = 1.0
model = OnlineWoodburyGP(self.xs, self.labels, lik, self.kernel,
self.mean_module)
model.eval()
model(self.new_points).mean
model = model.get_fantasy_model(self.new_points, self.new_targets)
test_output = lik(model(self.test_points))
np.testing.assert_allclose(r_test_output.mean.detach().numpy(),
test_output.mean.detach().numpy())
np.testing.assert_allclose(
r_test_output.covariance_matrix.detach().numpy(),
test_output.covariance_matrix.detach().numpy(),
)
def setUp(self):
self.xs = torch.tensor([0.20, 0.30, 0.40, 0.10, 0.70],
dtype=torch.double)
self.kernel = GridInterpolationKernel(RBFKernel(),
grid_size=4,
grid_bounds=[(-0.4, 1.4)
]).double()
self.mean_vec = torch.sin(self.xs).double() * 0.0
self.labels = torch.sin(self.xs) + torch.tensor(
[0.1, 0.2, -0.1, -0.2, -0.2], dtype=torch.double)
self.lik = GaussianLikelihood().double()
self.lik.noise = 0.1
self.train_train_covar = self.kernel(self.xs).evaluate_kernel()
self.distr = MultivariateNormal(self.mean_vec, self.train_train_covar)
e_distr = MultivariateNormal(self.mean_vec, self.train_train_covar)
e_lik = GaussianLikelihood().double()
e_lik.noise = 0.1
self.expected_strategy = InterpolatedPredictionStrategyWithFantasy(
self.xs, e_distr, self.labels, e_lik)
self.strategy = ShermanMorrisonOnlineStrategy(self.xs, self.distr,
self.labels, self.lik)
self.new_points = torch.tensor([0.5, 0.8], dtype=torch.double)
self.test_mean = torch.sin(self.new_points) * 0.0
self.test_train_covar = self.kernel(self.new_points,
self.xs).evaluate_kernel()
self.test_test_covar = self.kernel(self.new_points,
self.new_points).evaluate_kernel()
def test_init(self):
# mostly make sure it doesn't crash
lik = GaussianLikelihood()
lik.noise = 1.0
model = OnlineWoodburyGP(self.xs, self.labels, lik, self.kernel,
self.mean_module)
