def test_pairwise_gp(self):
for batch_shape, dtype in itertools.product(
(torch.Size(), torch.Size([2])), (torch.float, torch.double)):
tkwargs = {"device": self.device, "dtype": dtype}
X_dim = 2
model, model_kwargs = self._get_model_and_data(
batch_shape=batch_shape, X_dim=X_dim, **tkwargs)
train_X = model_kwargs["datapoints"]
train_comp = model_kwargs["comparisons"]
# test training
# regular training
mll = PairwiseLaplaceMarginalLogLikelihood(model).to(**tkwargs)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=OptimizationWarning)
fit_gpytorch_model(mll, options={"maxiter": 2}, max_retries=1)
# prior training
prior_m = PairwiseGP(None, None)
with self.assertRaises(RuntimeError):
prior_m(train_X)
# forward in training mode with non-training data
custom_m = PairwiseGP(**model_kwargs)
other_X = torch.rand(batch_shape + torch.Size([3, X_dim]),
**tkwargs)
other_comp = train_comp.clone()
with self.assertRaises(RuntimeError):
custom_m(other_X)
custom_mll = PairwiseLaplaceMarginalLogLikelihood(custom_m).to(
**tkwargs)
post = custom_m(train_X)
with self.assertRaises(RuntimeError):
custom_mll(post, other_comp)
# setting jitter = 0 with a singular covar will raise error
sing_train_X = torch.ones(batch_shape + torch.Size([10, X_dim]),
**tkwargs)
with self.assertRaises(RuntimeError):
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=RuntimeWarning)
custom_m = PairwiseGP(sing_train_X, train_comp, jitter=0)
custom_m.posterior(sing_train_X)
# test init
self.assertIsInstance(model.mean_module, ConstantMean)
self.assertIsInstance(model.covar_module, RBFKernel)
self.assertIsInstance(model.covar_module.lengthscale_prior,
GammaPrior)
self.assertEqual(model.num_outputs, 1)
# test custom noise prior
custom_noise_prior = GammaPrior(concentration=2.0, rate=1.0)
custom_noise_module = HomoskedasticNoise(
noise_prior=custom_noise_prior)
custom_m = PairwiseGP(**model_kwargs,
noise_module=custom_noise_module)
self.assertEqual(custom_m.noise_module.noise_prior.concentration,
torch.tensor(2.0))
self.assertEqual(custom_m.noise_module.noise_prior.rate,
torch.tensor(1.0))
# test custom models
custom_m = PairwiseGP(**model_kwargs, covar_module=LinearKernel())
self.assertIsInstance(custom_m.covar_module, LinearKernel)
# std_noise setter
custom_m.std_noise = 123
self.assertTrue(torch.all(custom_m.std_noise == 123))
# prior prediction
prior_m = PairwiseGP(None, None)
prior_m.eval()
post = prior_m.posterior(train_X)
self.assertIsInstance(post, GPyTorchPosterior)
# test methods that are not commonly or explicitly used
# _calc_covar with observation noise
no_noise_cov = model._calc_covar(train_X,
train_X,
observation_noise=False)
noise_cov = model._calc_covar(train_X,
train_X,
observation_noise=True)
diag_diff = (noise_cov - no_noise_cov).diagonal(dim1=-2, dim2=-1)
self.assertTrue(
torch.allclose(
diag_diff,
model.std_noise.expand(diag_diff.shape),
rtol=1e-4,
atol=1e-5,
))
# test trying adding jitter
pd_mat = torch.eye(2, 2)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=RuntimeWarning)
jittered_pd_mat = model._add_jitter(pd_mat)
diag_diff = (jittered_pd_mat - pd_mat).diagonal(dim1=-2, dim2=-1)
self.assertTrue(
torch.allclose(
diag_diff,
torch.full_like(diag_diff, model._jitter),
atol=model._jitter / 10,
))
# test initial utility val
util_comp = torch.topk(model.utility, k=2,
dim=-1).indices.unsqueeze(-2)
self.assertTrue(torch.all(util_comp == train_comp))
# test posterior
# test non batch evaluation
X = torch.rand(batch_shape + torch.Size([3, X_dim]), **tkwargs)
expected_shape = batch_shape + torch.Size([3, 1])
posterior = model.posterior(X)
self.assertIsInstance(posterior, GPyTorchPosterior)
self.assertEqual(posterior.mean.shape, expected_shape)
self.assertEqual(posterior.variance.shape, expected_shape)
# expect to raise error when output_indices is not None
with self.assertRaises(RuntimeError):
model.posterior(X, output_indices=[0])
# test re-evaluating utility when it's None
model.utility = None
posterior = model.posterior(X)
self.assertIsInstance(posterior, GPyTorchPosterior)
# test adding observation noise
posterior_pred = model.posterior(X, observation_noise=True)
self.assertIsInstance(posterior_pred, GPyTorchPosterior)
self.assertEqual(posterior_pred.mean.shape, expected_shape)
self.assertEqual(posterior_pred.variance.shape, expected_shape)
pvar = posterior_pred.variance
reshaped_noise = model.std_noise.unsqueeze(-2).expand(
posterior.variance.shape)
pvar_exp = posterior.variance + reshaped_noise
self.assertTrue(
torch.allclose(pvar, pvar_exp, rtol=1e-4, atol=1e-5))
# test batch evaluation
X = torch.rand(2, *batch_shape, 3, X_dim, **tkwargs)
expected_shape = torch.Size([2]) + batch_shape + torch.Size([3, 1])
posterior = model.posterior(X)
self.assertIsInstance(posterior, GPyTorchPosterior)
self.assertEqual(posterior.mean.shape, expected_shape)
# test adding observation noise in batch mode
posterior_pred = model.posterior(X, observation_noise=True)
self.assertIsInstance(posterior_pred, GPyTorchPosterior)
self.assertEqual(posterior_pred.mean.shape, expected_shape)
pvar = posterior_pred.variance
reshaped_noise = model.std_noise.unsqueeze(-2).expand(
posterior.variance.shape)
pvar_exp = posterior.variance + reshaped_noise
self.assertTrue(
torch.allclose(pvar, pvar_exp, rtol=1e-4, atol=1e-5))
def test_pairwise_gp(self):
for batch_shape, dtype in itertools.product(
(torch.Size(), torch.Size([2])), (torch.float, torch.double)):
tkwargs = {"device": self.device, "dtype": dtype}
X_dim = 2
model, model_kwargs = self._get_model_and_data(
batch_shape=batch_shape, X_dim=X_dim, **tkwargs)
train_X = model_kwargs["datapoints"]
train_comp = model_kwargs["comparisons"]
# test training
# regular training
mll = PairwiseLaplaceMarginalLogLikelihood(model).to(**tkwargs)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=OptimizationWarning)
fit_gpytorch_model(mll, options={"maxiter": 2}, max_retries=1)
# prior training
prior_m = PairwiseGP(None, None).to(**tkwargs)
with self.assertRaises(RuntimeError):
prior_m(train_X)
# forward in training mode with non-training data
custom_m = PairwiseGP(**model_kwargs)
other_X = torch.rand(batch_shape + torch.Size([3, X_dim]),
**tkwargs)
other_comp = train_comp.clone()
with self.assertRaises(RuntimeError):
custom_m(other_X)
custom_mll = PairwiseLaplaceMarginalLogLikelihood(custom_m).to(
**tkwargs)
post = custom_m(train_X)
with self.assertRaises(RuntimeError):
custom_mll(post, other_comp)
# setting jitter = 0 with a singular covar will raise error
sing_train_X = torch.ones(batch_shape + torch.Size([10, X_dim]),
**tkwargs)
with self.assertRaises(RuntimeError):
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=RuntimeWarning)
custom_m = PairwiseGP(sing_train_X, train_comp, jitter=0)
custom_m.posterior(sing_train_X)
# test init
self.assertIsInstance(model.mean_module, ConstantMean)
self.assertIsInstance(model.covar_module, ScaleKernel)
self.assertIsInstance(model.covar_module.base_kernel, RBFKernel)
self.assertIsInstance(
model.covar_module.base_kernel.lengthscale_prior, GammaPrior)
self.assertIsInstance(model.covar_module.outputscale_prior,
SmoothedBoxPrior)
self.assertEqual(model.num_outputs, 1)
self.assertEqual(model.batch_shape, batch_shape)
# test custom models
custom_m = PairwiseGP(**model_kwargs, covar_module=LinearKernel())
self.assertIsInstance(custom_m.covar_module, LinearKernel)
# prior prediction
prior_m = PairwiseGP(None, None).to(**tkwargs)
prior_m.eval()
post = prior_m.posterior(train_X)
self.assertIsInstance(post, GPyTorchPosterior)
# test trying adding jitter
pd_mat = torch.eye(2, 2)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=RuntimeWarning)
jittered_pd_mat = model._add_jitter(pd_mat)
diag_diff = (jittered_pd_mat - pd_mat).diagonal(dim1=-2, dim2=-1)
self.assertTrue(
torch.allclose(
diag_diff,
torch.full_like(diag_diff, model._jitter),
atol=model._jitter / 10,
))
# test initial utility val
util_comp = torch.topk(model.utility, k=2,
dim=-1).indices.unsqueeze(-2)
self.assertTrue(torch.all(util_comp == train_comp))
# test posterior
# test non batch evaluation
X = torch.rand(batch_shape + torch.Size([3, X_dim]), **tkwargs)
expected_shape = batch_shape + torch.Size([3, 1])
posterior = model.posterior(X)
self.assertIsInstance(posterior, GPyTorchPosterior)
self.assertEqual(posterior.mean.shape, expected_shape)
self.assertEqual(posterior.variance.shape, expected_shape)
# expect to raise error when output_indices is not None
with self.assertRaises(RuntimeError):
model.posterior(X, output_indices=[0])
# test re-evaluating utility when it's None
model.utility = None
posterior = model.posterior(X)
self.assertIsInstance(posterior, GPyTorchPosterior)
# test batch evaluation
X = torch.rand(2, *batch_shape, 3, X_dim, **tkwargs)
expected_shape = torch.Size([2]) + batch_shape + torch.Size([3, 1])
posterior = model.posterior(X)
self.assertIsInstance(posterior, GPyTorchPosterior)
self.assertEqual(posterior.mean.shape, expected_shape)
# test input_transform
# the untransfomed one should be stored
normalize_tf = Normalize(d=2,
bounds=torch.tensor([[0, 0], [0.5, 1.5]]))
model = PairwiseGP(**model_kwargs, input_transform=normalize_tf)
self.assertTrue(torch.all(model.datapoints == train_X))
# test set_train_data strict mode
model = PairwiseGP(**model_kwargs)
changed_train_X = train_X.unsqueeze(0)
changed_train_comp = train_comp.unsqueeze(0)
# expect to raise error when set data to something different
with self.assertRaises(RuntimeError):
model.set_train_data(changed_train_X,
changed_train_comp,
strict=True)
# the same datapoints but changed comparison will also raise error
with self.assertRaises(RuntimeError):
model.set_train_data(train_X, changed_train_comp, strict=True)