class TestHigherOrderGP(BotorchTestCase):
def setUp(self):
super().setUp()
torch.random.manual_seed(0)
train_x = torch.rand(2, 10, 1, device=self.device)
train_y = torch.randn(2, 10, 3, 5, device=self.device)
self.model = HigherOrderGP(train_x, train_y)
# check that we can assign different kernels and likelihoods
model_2 = HigherOrderGP(
train_X=train_x,
train_Y=train_y,
covar_modules=[RBFKernel(), RBFKernel(),
RBFKernel()],
likelihood=GaussianLikelihood(),
)
model_3 = HigherOrderGP(
train_X=train_x,
train_Y=train_y,
covar_modules=[RBFKernel(), RBFKernel(),
RBFKernel()],
likelihood=GaussianLikelihood(),
latent_init="gp",
)
for m in [self.model, model_2, model_3]:
mll = ExactMarginalLogLikelihood(m.likelihood, m)
fit_gpytorch_torch(mll, options={"maxiter": 1, "disp": False})
def test_num_output_dims(self):
for dtype in [torch.float, torch.double]:
train_x = torch.rand(2, 10, 1, device=self.device, dtype=dtype)
train_y = torch.randn(2, 10, 3, 5, device=self.device, dtype=dtype)
model = HigherOrderGP(train_x, train_y)
# check that it correctly inferred that this is a batched model
self.assertEqual(model._num_outputs, 2)
train_x = torch.rand(10, 1, device=self.device, dtype=dtype)
train_y = torch.randn(10, 3, 5, 2, device=self.device, dtype=dtype)
model = HigherOrderGP(train_x, train_y)
# non-batched case
self.assertEqual(model._num_outputs, 1)
train_x = torch.rand(3, 2, 10, 1, device=self.device, dtype=dtype)
train_y = torch.randn(3,
2,
10,
3,
5,
device=self.device,
dtype=dtype)
# check the error when using multi-dim batch_shape
with self.assertRaises(NotImplementedError):
model = HigherOrderGP(train_x, train_y)
def test_posterior(self):
for dtype in [torch.float, torch.double]:
for mcs in [800, 10]:
torch.random.manual_seed(0)
with max_cholesky_size(mcs):
test_x = torch.rand(2, 12, 1).to(device=self.device,
dtype=dtype)
self.model.to(dtype)
# clear caches
self.model.train()
self.model.eval()
# test the posterior works
posterior = self.model.posterior(test_x)
self.assertIsInstance(posterior, GPyTorchPosterior)
# test the posterior works with observation noise
posterior = self.model.posterior(test_x,
observation_noise=True)
self.assertIsInstance(posterior, GPyTorchPosterior)
# test the posterior works with no variances
# some funkiness in MVNs registration so the variance is non-zero.
with skip_posterior_variances():
posterior = self.model.posterior(test_x)
self.assertIsInstance(posterior, GPyTorchPosterior)
self.assertLessEqual(posterior.variance.max(), 1e-6)
def test_transforms(self):
for dtype in [torch.float, torch.double]:
train_x = torch.rand(10, 3, device=self.device, dtype=dtype)
train_y = torch.randn(10, 4, 5, device=self.device, dtype=dtype)
# test handling of Standardize
with self.assertWarns(RuntimeWarning):
model = HigherOrderGP(train_X=train_x,
train_Y=train_y,
outcome_transform=Standardize(m=5))
self.assertIsInstance(model.outcome_transform,
FlattenedStandardize)
self.assertEqual(model.outcome_transform.output_shape,
train_y.shape[1:])
self.assertEqual(model.outcome_transform.batch_shape, torch.Size())
model = HigherOrderGP(
train_X=train_x,
train_Y=train_y,
input_transform=Normalize(d=3),
outcome_transform=FlattenedStandardize(train_y.shape[1:]),
)
mll = ExactMarginalLogLikelihood(model.likelihood, model)
fit_gpytorch_torch(mll, options={"maxiter": 1, "disp": False})
test_x = torch.rand(2, 5, 3, device=self.device, dtype=dtype)
test_y = torch.randn(2, 5, 4, 5, device=self.device, dtype=dtype)
with mock.patch.object(HigherOrderGP,
"transform_inputs",
wraps=model.transform_inputs) as mock_intf:
posterior = model.posterior(test_x)
mock_intf.assert_called_once()
self.assertIsInstance(posterior, TransformedPosterior)
conditioned_model = model.condition_on_observations(test_x, test_y)
self.assertIsInstance(conditioned_model, HigherOrderGP)
self.check_transform_forward(model, dtype)
self.check_transform_untransform(model, dtype)
def check_transform_forward(self, model, dtype):
train_y = torch.randn(2, 10, 4, 5, device=self.device, dtype=dtype)
train_y_var = torch.rand(2, 10, 4, 5, device=self.device, dtype=dtype)
output, output_var = model.outcome_transform.forward(train_y)
self.assertEqual(output.shape, torch.Size((2, 10, 4, 5)))
self.assertEqual(output_var, None)
output, output_var = model.outcome_transform.forward(
train_y, train_y_var)
self.assertEqual(output.shape, torch.Size((2, 10, 4, 5)))
self.assertEqual(output_var.shape, torch.Size((2, 10, 4, 5)))
def check_transform_untransform(self, model, dtype):
output, output_var = model.outcome_transform.untransform(
torch.randn(2, 2, 4, 5, device=self.device, dtype=dtype))
self.assertEqual(output.shape, torch.Size((2, 2, 4, 5)))
self.assertEqual(output_var, None)
output, output_var = model.outcome_transform.untransform(
torch.randn(2, 2, 4, 5, device=self.device, dtype=dtype),
torch.rand(2, 2, 4, 5, device=self.device, dtype=dtype),
)
self.assertEqual(output.shape, torch.Size((2, 2, 4, 5)))
self.assertEqual(output_var.shape, torch.Size((2, 2, 4, 5)))
def test_condition_on_observations(self):
for dtype in [torch.float, torch.double]:
torch.random.manual_seed(0)
test_x = torch.rand(2, 5, 1, device=self.device, dtype=dtype)
test_y = torch.randn(2, 5, 3, 5, device=self.device, dtype=dtype)
self.model.to(dtype)
if dtype == torch.double:
# need to clear float caches
self.model.train()
self.model.eval()
# dummy call to ensure caches have been computed
_ = self.model.posterior(test_x)
conditioned_model = self.model.condition_on_observations(
test_x, test_y)
self.assertIsInstance(conditioned_model, HigherOrderGP)
def test_fantasize(self):
for dtype in [torch.float, torch.double]:
torch.random.manual_seed(0)
test_x = torch.rand(2, 5, 1, device=self.device, dtype=dtype)
sampler = IIDNormalSampler(num_samples=32)
self.model.to(dtype)
if dtype == torch.double:
# need to clear float caches
self.model.train()
self.model.eval()
_ = self.model.posterior(test_x)
fantasy_model = self.model.fantasize(test_x, sampler=sampler)
self.assertIsInstance(fantasy_model, HigherOrderGP)
self.assertEqual(fantasy_model.train_inputs[0].shape[:2],
torch.Size((32, 2)))
def test_initialize_latents(self):
for dtype in [torch.float, torch.double]:
torch.random.manual_seed(0)
train_x = torch.rand(10, 1, device=self.device, dtype=dtype)
train_y = torch.randn(10, 3, 5, device=self.device, dtype=dtype)
for latent_dim_sizes, latent_init in itertools.product(
[[1, 1], [2, 3]],
["gp", "default"],
):
self.model = HigherOrderGP(
train_x,
train_y,
num_latent_dims=latent_dim_sizes,
latent_init=latent_init,
)
self.assertEqual(
self.model.latent_parameters[0].shape,
torch.Size((3, latent_dim_sizes[0])),
)
self.assertEqual(
self.model.latent_parameters[1].shape,
torch.Size((5, latent_dim_sizes[1])),
)
class TestHigherOrderGP(BotorchTestCase):
def setUp(self):
super().setUp()
manual_seed(0)
train_x = rand(2, 10, 1)
train_y = randn(2, 10, 3, 5)
train_x = train_x.to(device=self.device)
train_y = train_y.to(device=self.device)
self.model = HigherOrderGP(train_x, train_y, first_dim_is_batch=True)
# check that we can assign different kernels and likelihoods
model_2 = HigherOrderGP(
train_x,
train_y,
first_dim_is_batch=True,
covar_modules=[RBFKernel(), RBFKernel(),
RBFKernel()],
likelihood=GaussianLikelihood(),
)
for m in [self.model, model_2]:
mll = ExactMarginalLogLikelihood(m.likelihood, m)
fit_gpytorch_torch(mll, options={"maxiter": 1, "disp": False})
def test_posterior(self):
manual_seed(0)
test_x = rand(2, 30, 1).to(device=self.device)
# test the posterior works
posterior = self.model.posterior(test_x)
self.assertIsInstance(posterior, GPyTorchPosterior)
# test the posterior works with observation noise
posterior = self.model.posterior(test_x, observation_noise=True)
self.assertIsInstance(posterior, GPyTorchPosterior)
# test the posterior works with no variances
# some funkiness in MVNs registration so the variance is non-zero.
with skip_posterior_variances():
posterior = self.model.posterior(test_x)
self.assertIsInstance(posterior, GPyTorchPosterior)
self.assertLessEqual(posterior.variance.max(), 1e-6)
def test_transforms(self):
train_x = rand(10, 3, device=self.device)
train_y = randn(10, 4, 5, device=self.device)
model = HigherOrderGP(
train_x,
train_y,
input_transform=Normalize(d=3),
outcome_transform=FlattenedStandardize(train_y.shape[1:]),
)
mll = ExactMarginalLogLikelihood(model.likelihood, model)
fit_gpytorch_torch(mll, options={"maxiter": 1, "disp": False})
test_x = rand(2, 5, 3, device=self.device)
test_y = randn(2, 5, 4, 5, device=self.device)
posterior = model.posterior(test_x)
self.assertIsInstance(posterior, TransformedPosterior)
conditioned_model = model.condition_on_observations(test_x, test_y)
self.assertIsInstance(conditioned_model, HigherOrderGP)
self.check_transform_forward(model)
self.check_transform_untransform(model)
def check_transform_forward(self, model):
train_y = randn(2, 10, 4, 5, device=self.device)
train_y_var = rand(2, 10, 4, 5, device=self.device)
output, output_var = model.outcome_transform.forward(train_y)
self.assertEqual(output.shape, Size((2, 10, 4, 5)))
self.assertEqual(output_var, None)
output, output_var = model.outcome_transform.forward(
train_y, train_y_var)
self.assertEqual(output.shape, Size((2, 10, 4, 5)))
self.assertEqual(output_var.shape, Size((2, 10, 4, 5)))
def check_transform_untransform(self, model):
output, output_var = model.outcome_transform.untransform(
randn(2, 2, 4, 5, device=self.device))
self.assertEqual(output.shape, Size((2, 2, 4, 5)))
self.assertEqual(output_var, None)
output, output_var = model.outcome_transform.untransform(
randn(2, 2, 4, 5, device=self.device),
rand(2, 2, 4, 5, device=self.device),
)
self.assertEqual(output.shape, Size((2, 2, 4, 5)))
self.assertEqual(output_var.shape, Size((2, 2, 4, 5)))
def test_condition_on_observations(self):
manual_seed(0)
test_x = rand(2, 5, 1, device=self.device)
test_y = randn(2, 5, 3, 5, device=self.device)
# dummy call to ensure caches have been computed
_ = self.model.posterior(test_x)
conditioned_model = self.model.condition_on_observations(
test_x, test_y)
self.assertIsInstance(conditioned_model, HigherOrderGP)
def test_fantasize(self):
manual_seed(0)
test_x = rand(2, 5, 1, device=self.device)
sampler = IIDNormalSampler(num_samples=32).to(self.device)
_ = self.model.posterior(test_x)
fantasy_model = self.model.fantasize(test_x, sampler=sampler)
self.assertIsInstance(fantasy_model, HigherOrderGP)
self.assertEqual(fantasy_model.train_inputs[0].shape[:2], Size(
(32, 2)))
def test_initialize_latents(self):
manual_seed(0)
train_x = rand(10, 1, device=self.device)
train_y = randn(10, 3, 5, device=self.device)
for latent_dim_sizes, latent_init in itertools.product(
[[1, 1], [2, 3]],
["gp", "default"],
):
self.model = HigherOrderGP(
train_x,
train_y,
num_latent_dims=latent_dim_sizes,
latent_init=latent_init,
)
self.assertEqual(
self.model.latent_parameters[0].shape,
Size((3, latent_dim_sizes[0])),
)
self.assertEqual(
self.model.latent_parameters[1].shape,
Size((5, latent_dim_sizes[1])),
)