def test_get_EHVI_input_validation_errors(self):
weights = torch.ones(2)
objective_thresholds = torch.zeros(2)
mm = MockModel(MockPosterior())
with self.assertRaisesRegex(ValueError,
"There are no feasible observed points."):
get_EHVI(
model=mm,
objective_weights=weights,
objective_thresholds=objective_thresholds,
)
def test_get_ehvi(self):
weights = torch.tensor([0.0, 1.0, 1.0])
X_observed = torch.rand(4, 3)
X_pending = torch.rand(1, 3)
constraints = (torch.tensor([1.0, 0.0, 0.0]), torch.tensor([[10.0]]))
Y = torch.rand(4, 3)
mm = MockModel(MockPosterior(mean=Y))
objective_thresholds = torch.arange(3, dtype=torch.float)
obj_and_obj_t = get_weighted_mc_objective_and_objective_thresholds(
objective_weights=weights,
objective_thresholds=objective_thresholds,
)
(weighted_obj, new_obj_thresholds) = obj_and_obj_t
cons_tfs = get_outcome_constraint_transforms(constraints)
with manual_seed(0):
seed = torch.randint(1, 10000, (1, )).item()
with ExitStack() as es:
mock_get_acqf = es.enter_context(mock.patch(GET_ACQF_PATH))
es.enter_context(
mock.patch(GET_CONSTRAINT_PATH, return_value=cons_tfs))
es.enter_context(
mock.patch(GET_OBJ_PATH, return_value=obj_and_obj_t))
es.enter_context(manual_seed(0))
get_EHVI(
model=mm,
objective_weights=weights,
outcome_constraints=constraints,
objective_thresholds=objective_thresholds,
X_observed=X_observed,
X_pending=X_pending,
)
mock_get_acqf.assert_called_once_with(
acquisition_function_name="qEHVI",
model=mm,
objective=weighted_obj,
X_observed=X_observed,
X_pending=X_pending,
constraints=cons_tfs,
mc_samples=128,
qmc=True,
alpha=0.0,
seed=seed,
ref_point=new_obj_thresholds.tolist(),
Y=Y,
)
def test_get_EHVI_input_validation_errors(self):
model = MultiObjectiveBotorchModel()
x = torch.zeros(2, 2)
weights = torch.ones(2)
ref_point = torch.zeros(2)
with self.assertRaisesRegex(ValueError,
"There are no feasible observed points."):
get_EHVI(model=model,
objective_weights=weights,
ref_point=ref_point)
with self.assertRaisesRegex(
ValueError,
"Expected Hypervolume Improvement requires Ys argument"):
get_EHVI(
model=model,
X_observed=x,
objective_weights=weights,
ref_point=ref_point,
)