def test_abstract_raises(self):
with self.assertRaises(TypeError):
AnalyticAcquisitionFunction()
# raise if model is multi-output, but no posterior transform is given
mean = torch.zeros(1, 2)
variance = torch.ones(1, 2)
mm = MockModel(MockPosterior(mean=mean, variance=variance))
with self.assertRaises(UnsupportedError):
DummyAnalyticAcquisitionFunction(model=mm)
def __init__(self, model: Model, options: dict) -> None:
# MCAcquisitionFunction.__init__(self, model=model, sampler=sampler, objective=IdentityMCObjective())
AnalyticAcquisitionFunction.__init__(
self,
model=model,
objective=ScalarizedObjective(weights=torch.Tensor([1.0])))
AcquisitionBaseTools.__init__(self,
model=model,
iden="Xsearch",
Nrestarts_eta=options["Nrestarts_eta"])
self.u_vec = None
self.Nsamples_fmin = options["Nsamples_fmin"]
self.Nrestarts = options["Nrestarts_safe"]
self.debug = False
self.method = options["method_safe"]
self.disp_info_scipy_opti = options["disp_info_scipy_opti"]
self.dim = self.model.dim
def test_abstract_raises(self):
with self.assertRaises(TypeError):
AnalyticAcquisitionFunction()