def test_get_X_baseline(self): tkwargs = {"device": self.device} for dtype in (torch.float, torch.double): tkwargs["dtype"] = dtype X_train = torch.rand(20, 2, **tkwargs) model = MockModel( MockPosterior(mean=(2 * X_train + 1).sum(dim=-1, keepdim=True))) # test NEI with X_baseline acqf = qNoisyExpectedImprovement(model, X_baseline=X_train[:2]) X = get_X_baseline(acq_function=acqf) self.assertTrue(torch.equal(X, acqf.X_baseline)) # test EI without X_baseline acqf = qExpectedImprovement(model, best_f=0.0) with warnings.catch_warnings( record=True) as w, settings.debug(True): X_rnd = get_X_baseline(acq_function=acqf, ) self.assertEqual(len(w), 1) self.assertTrue(issubclass(w[-1].category, BotorchWarning)) self.assertIsNone(X_rnd) # set train inputs model.train_inputs = (X_train, ) X = get_X_baseline(acq_function=acqf, ) self.assertTrue(torch.equal(X, X_train)) # test that we fail back to train_inputs if X_baseline is an empty tensor acqf.register_buffer("X_baseline", X_train[:0]) X = get_X_baseline(acq_function=acqf, ) self.assertTrue(torch.equal(X, X_train)) # test acquisitipon function without X_baseline or model acqf = FixedFeatureAcquisitionFunction(acqf, d=2, columns=[0], values=[0]) with warnings.catch_warnings( record=True) as w, settings.debug(True): X_rnd = get_X_baseline(acq_function=acqf, ) self.assertEqual(len(w), 1) self.assertTrue(issubclass(w[-1].category, BotorchWarning)) self.assertIsNone(X_rnd) Y_train = 2 * X_train[:2] + 1 moo_model = MockModel(MockPosterior(mean=Y_train, samples=Y_train)) ref_point = torch.zeros(2, **tkwargs) # test NEHVI with X_baseline acqf = qNoisyExpectedHypervolumeImprovement( moo_model, ref_point=ref_point, X_baseline=X_train[:2], cache_root=False, ) X = get_X_baseline(acq_function=acqf, ) self.assertTrue(torch.equal(X, acqf.X_baseline)) # test qEHVI without train_inputs acqf = qExpectedHypervolumeImprovement( moo_model, ref_point=ref_point, partitioning=FastNondominatedPartitioning( ref_point=ref_point, Y=Y_train, ), ) # test extracting train_inputs from model list GP model_list = ModelListGP( SingleTaskGP(X_train, Y_train[:, :1]), SingleTaskGP(X_train, Y_train[:, 1:]), ) acqf = qExpectedHypervolumeImprovement( model_list, ref_point=ref_point, partitioning=FastNondominatedPartitioning( ref_point=ref_point, Y=Y_train, ), ) X = get_X_baseline(acq_function=acqf, ) self.assertTrue(torch.equal(X, X_train)) # test MESMO for which we need to use # `acqf.mo_model` batched_mo_model = SingleTaskGP(X_train, Y_train) acqf = qMultiObjectiveMaxValueEntropy( batched_mo_model, sample_pareto_frontiers=lambda model: torch.rand( 10, 2, **tkwargs), ) X = get_X_baseline(acq_function=acqf, ) self.assertTrue(torch.equal(X, X_train)) # test that if there is an input transform that is applied # to the train_inputs when the model is in eval mode, we # extract the untransformed train_inputs model = SingleTaskGP(X_train, Y_train[:, :1], input_transform=Warp(indices=[0, 1])) model.eval() self.assertFalse(torch.equal(model.train_inputs[0], X_train)) acqf = qExpectedImprovement(model, best_f=0.0) X = get_X_baseline(acq_function=acqf, ) self.assertTrue(torch.equal(X, X_train))