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()