def test_abstract_raises(self):
with self.assertRaises(TypeError):
MCAcquisitionFunction()
# raise if model is multi-output, but no objective is given
no = "botorch.utils.testing.MockModel.num_outputs"
with mock.patch(no, new_callable=mock.PropertyMock) as mock_num_outputs:
mock_num_outputs.return_value = 2
mm = MockModel(MockPosterior())
with self.assertRaises(UnsupportedError):
DummyMCAcquisitionFunction(model=mm)
def __init__(self, model_list: List[Model], options: dict) -> None:
# AnalyticAcquisitionFunction.__init__(self, model=ModelListGP(model_list[0],model_list[1]), objective=ScalarizedObjective(weights=torch.Tensor([1.0])))
MCAcquisitionFunction.__init__(self,
model=ModelListGP(
model_list[0], model_list[1]),
objective=constrained_obj)
AcquisitionBaseToolsConstrained.__init__(
self,
model_list=model_list,
iden="XsearchFailures",
Nrestarts_eta_c=options["Nrestarts_eta_c"],
budget_failures=options["budget_failures"])
self.dim = model_list[0].dim
self.u_vec = None
self.Nsamples_fmin = options["Nsamples_fmin"]
self.Nrestarts_safe = options["Nrestarts_safe"]
assert self.Nrestarts_safe > 1, "Choose at least 2 restart points."
self.Nrestarts_risky = options["Nrestarts_risky"]
assert self.Nrestarts_risky > 1, "Choose at least 2 restart points."
self.which_mode = "risky"
self.NBOiters = options["NBOiters"]
self.rho_conserv = options["rho_safe"]
self.method_safe = options["method_safe"]
self.method_risky = options["method_risky"]
self.constrained_opt = ConstrainedOptimizationNonLinearConstraints(
self.dim, self.forward, self.probabilistic_constraint)
self.use_nlopt = False
self.disp_info_scipy_opti = options["disp_info_scipy_opti"]
self.decision_boundary = options["decision_boundary"]
# Initialize rho latent process:
if float(self.budget_failures) / self.NBOiters == 1.0:
self.zk = norm.ppf(self.rho_conserv)
else:
self.zk = norm.ppf(float(self.budget_failures) / self.NBOiters)
self.zrisk = norm.ppf(1.0 - self.rho_conserv)
self.zsafe = norm.ppf(self.rho_conserv)
def test_abstract_raises(self):
with self.assertRaises(TypeError):
MCAcquisitionFunction()
# raise if model is multi-output, but no outcome transform or objective
# are given
no = "botorch.utils.testing.MockModel.num_outputs"
with mock.patch(no,
new_callable=mock.PropertyMock) as mock_num_outputs:
mock_num_outputs.return_value = 2
mm = MockModel(MockPosterior())
with self.assertRaises(UnsupportedError):
DummyMCAcquisitionFunction(model=mm)
# raise if model is multi-output, but outcome transform does not
# scalarize and no objetive is given
with mock.patch(no,
new_callable=mock.PropertyMock) as mock_num_outputs:
mock_num_outputs.return_value = 2
mm = MockModel(MockPosterior())
ptf = DummyNonScalarizingPosteriorTransform()
with self.assertRaises(UnsupportedError):
DummyMCAcquisitionFunction(model=mm, posterior_transform=ptf)
def test_abstract_raises(self):
with self.assertRaises(TypeError):
MCAcquisitionFunction()