def _get_model_and_data(self, batch_shape, X_dim=2, **tkwargs):
train_X, train_Y, train_comp = self._make_rand_mini_data(
batch_shape=batch_shape, X_dim=X_dim, **tkwargs)
model_kwargs = {"datapoints": train_X, "comparisons": train_comp}
model = PairwiseGP(**model_kwargs)
return model, model_kwargs
def test_learned_preference_objective(self):
X_dim = 2
train_X = torch.rand(2, X_dim)
train_comps = torch.LongTensor([[0, 1]])
pref_model = PairwiseGP(train_X, train_comps)
og_sample_shape = 3
batch_size = 2
n = 8
test_X = torch.rand(torch.Size(
(og_sample_shape, batch_size, n, X_dim)))
# test default setting where sampler = IIDNormalSampler(num_samples=1)
pref_obj = LearnedObjective(pref_model=pref_model)
self.assertEqual(
pref_obj(test_X).shape,
torch.Size([og_sample_shape, batch_size, n]))
# test when sampler has num_samples = 16
num_samples = 16
pref_obj = LearnedObjective(
pref_model=pref_model,
sampler=SobolQMCNormalSampler(num_samples=num_samples),
)
self.assertEqual(
pref_obj(test_X).shape,
torch.Size([num_samples * og_sample_shape, batch_size, n]),
)
# test posterior mean
mean_pref_model = PosteriorMeanModel(model=pref_model)
pref_obj = LearnedObjective(pref_model=mean_pref_model)
self.assertEqual(
pref_obj(test_X).shape,
torch.Size([og_sample_shape, batch_size, n]))
# cannot use a deterministic model together with a sampler
with self.assertRaises(AssertionError):
LearnedObjective(
pref_model=mean_pref_model,
sampler=SobolQMCNormalSampler(num_samples=num_samples),
)
def test_analytic_eubo(self):
twargs = {"dtype": torch.double}
X_dim = 3
Y_dim = 2
X = torch.rand(2, X_dim, **twargs)
Y = torch.rand(2, Y_dim, **twargs)
comps = torch.tensor([[1, 0]], dtype=torch.long)
standard_bounds = torch.zeros(2, X.shape[-1])
standard_bounds[1] = 1
model = SingleTaskGP(X, Y)
pref_model = PairwiseGP(Y, comps)
# Test with an outcome model and a preference model
one_sample_outcome_model = FixedSingleSampleModel(model=model)
eubo = AnalyticExpectedUtilityOfBestOption(
pref_model=pref_model, outcome_model=one_sample_outcome_model)
# test forward with different number of points
good_X = torch.rand(2, X_dim, **twargs)
eubo(good_X)
bad_X = torch.rand(3, X_dim, **twargs)
with self.assertRaises(UnsupportedError):
eubo(bad_X)
good_X = torch.rand(1, X_dim, **twargs)
previous_winner = torch.rand(1, Y_dim, **twargs)
eubo_with_winner = AnalyticExpectedUtilityOfBestOption(
pref_model=pref_model,
outcome_model=one_sample_outcome_model,
previous_winner=previous_winner,
)
eubo_with_winner(good_X)
# Test model=None
AnalyticExpectedUtilityOfBestOption(pref_model=pref_model,
outcome_model=None)
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_condition_on_observations(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"]
# evaluate model
model.posterior(torch.rand(torch.Size([4, X_dim]), **tkwargs))
# test condition_on_observations
# test condition_on_observations with prior mode
prior_m = PairwiseGP(None, None)
cond_m = prior_m.condition_on_observations(train_X, train_comp)
self.assertTrue(cond_m.datapoints is train_X)
self.assertTrue(cond_m.comparisons is train_comp)
# fantasize at different input points
fant_shape = torch.Size([2])
X_fant, Y_fant, comp_fant = self._make_rand_mini_data(
batch_shape=fant_shape + batch_shape, X_dim=X_dim, **tkwargs)
# cannot condition on non-pairwise Ys
with self.assertRaises(RuntimeError):
model.condition_on_observations(X_fant, comp_fant[..., 0])
cm = model.condition_on_observations(X_fant, comp_fant)
# make sure it's a deep copy
self.assertTrue(model is not cm)
# fantasize at same input points (check proper broadcasting)
cm_same_inputs = model.condition_on_observations(
X_fant[0], comp_fant)
test_Xs = [
# test broadcasting single input across fantasy and model batches
torch.rand(4, X_dim, **tkwargs),
# separate input for each model batch and broadcast across
# fantasy batches
torch.rand(batch_shape + torch.Size([4, X_dim]), **tkwargs),
# separate input for each model and fantasy batch
torch.rand(fant_shape + batch_shape + torch.Size([4, X_dim]),
**tkwargs),
]
for test_X in test_Xs:
posterior = cm.posterior(test_X)
self.assertEqual(posterior.mean.shape,
fant_shape + batch_shape + torch.Size([4, 1]))
posterior_same_inputs = cm_same_inputs.posterior(test_X)
self.assertEqual(
posterior_same_inputs.mean.shape,
fant_shape + batch_shape + torch.Size([4, 1]),
)
# check that fantasies of batched model are correct
if len(batch_shape) > 0 and test_X.dim() == 2:
state_dict_non_batch = {
key: (val[0] if val.numel() > 1 else val)
for key, val in model.state_dict().items()
}
model_kwargs_non_batch = {
"datapoints": model_kwargs["datapoints"][0],
"comparisons": model_kwargs["comparisons"][0],
}
model_non_batch = model.__class__(**model_kwargs_non_batch)
model_non_batch.load_state_dict(state_dict_non_batch)
model_non_batch.eval()
model_non_batch.posterior(
torch.rand(torch.Size([4, X_dim]), **tkwargs))
cm_non_batch = model_non_batch.condition_on_observations(
X_fant[0][0], comp_fant[:, 0, :])
non_batch_posterior = cm_non_batch.posterior(test_X)
self.assertTrue(
torch.allclose(
posterior_same_inputs.mean[:, 0, ...],
non_batch_posterior.mean,
atol=1e-3,
))
self.assertTrue(
torch.allclose(
posterior_same_inputs.mvn.
covariance_matrix[:, 0, :, :],
non_batch_posterior.mvn.covariance_matrix,
atol=1e-3,
))
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)