def test_probability_of_improvement_batch(self):
for dtype in (torch.float, torch.double):
mean = torch.tensor([0.0, 0.67449], device=self.device, dtype=dtype).view(
2, 1, 1
)
variance = torch.ones_like(mean)
mm = MockModel(MockPosterior(mean=mean, variance=variance))
module = ProbabilityOfImprovement(model=mm, best_f=0.0)
X = torch.zeros(2, 1, 1, device=self.device, dtype=dtype)
pi = module(X)
pi_expected = torch.tensor([0.5, 0.75], device=self.device, dtype=dtype)
self.assertTrue(torch.allclose(pi, pi_expected, atol=1e-4))
# check for proper error if multi-output model
mean2 = torch.rand(3, 1, 2, device=self.device, dtype=dtype)
variance2 = torch.ones_like(mean2)
mm2 = MockModel(MockPosterior(mean=mean2, variance=variance2))
with self.assertRaises(UnsupportedError):
ProbabilityOfImprovement(model=mm2, best_f=0.0)
def test_acquisition_functions(self):
tkwargs = {"device": self.device, "dtype": torch.double}
train_X, train_Y, train_Yvar, model = self._get_data_and_model(
infer_noise=True, **tkwargs
)
fit_fully_bayesian_model_nuts(
model, warmup_steps=8, num_samples=5, thinning=2, disable_progbar=True
)
sampler = IIDNormalSampler(num_samples=2)
acquisition_functions = [
ExpectedImprovement(model=model, best_f=train_Y.max()),
ProbabilityOfImprovement(model=model, best_f=train_Y.max()),
PosteriorMean(model=model),
UpperConfidenceBound(model=model, beta=4),
qExpectedImprovement(model=model, best_f=train_Y.max(), sampler=sampler),
qNoisyExpectedImprovement(model=model, X_baseline=train_X, sampler=sampler),
qProbabilityOfImprovement(
model=model, best_f=train_Y.max(), sampler=sampler
),
qSimpleRegret(model=model, sampler=sampler),
qUpperConfidenceBound(model=model, beta=4, sampler=sampler),
qNoisyExpectedHypervolumeImprovement(
model=ModelListGP(model, model),
X_baseline=train_X,
ref_point=torch.zeros(2, **tkwargs),
sampler=sampler,
),
qExpectedHypervolumeImprovement(
model=ModelListGP(model, model),
ref_point=torch.zeros(2, **tkwargs),
sampler=sampler,
partitioning=NondominatedPartitioning(
ref_point=torch.zeros(2, **tkwargs), Y=train_Y.repeat([1, 2])
),
),
]
for acqf in acquisition_functions:
for batch_shape in [[5], [6, 5, 2]]:
test_X = torch.rand(*batch_shape, 1, 4, **tkwargs)
self.assertEqual(acqf(test_X).shape, torch.Size(batch_shape))
