Suppress non-log EI warnings in HVKG helpers

Summary: My first idea was to change this to use Log-EHVI, but this helper is used in the tutorial to compute the hypervolume, so we can't change the return value. Since this is not directly constructed by the user, raising a warning is not productive. This diff suppresses the warning.

Differential Revision: D61722790

botorch/acquisition/multi_objective/hypervolume_knowledge_gradient.py

@@ -16,6 +16,7 @@
     Learning, 2023.
 """
 
+import warnings
 from copy import deepcopy
 from typing import Any, Callable, Optional
 
@@ -35,6 +36,7 @@
 )
 from botorch.acquisition.multi_objective.objective import MCMultiOutputObjective
 from botorch.exceptions.errors import UnsupportedError
+from botorch.exceptions.warnings import NumericsWarning
 from botorch.models.deterministic import PosteriorMeanModel
 from botorch.models.model import Model
 from botorch.sampling.base import MCSampler
@@ -500,20 +502,22 @@ def _get_hv_value_function(
     if use_posterior_mean:
         model = PosteriorMeanModel(model=model)
         sampler = StochasticSampler(sample_shape=torch.Size([1]))  # dummy sampler
-    base_value_function = qExpectedHypervolumeImprovement(
-        model=model,
-        ref_point=ref_point,
-        partitioning=FastNondominatedPartitioning(
+    with warnings.catch_warnings():
+        warnings.simplefilter(action="ignore", category=NumericsWarning)
+        base_value_function = qExpectedHypervolumeImprovement(
+            model=model,
             ref_point=ref_point,
-            Y=torch.empty(
-                (0, ref_point.shape[0]),
-                dtype=ref_point.dtype,
-                device=ref_point.device,
-            ),
-        ),  # create empty partitioning
-        sampler=sampler,
-        objective=objective,
-    )
+            partitioning=FastNondominatedPartitioning(
+                ref_point=ref_point,
+                Y=torch.empty(
+                    (0, ref_point.shape[0]),
+                    dtype=ref_point.dtype,
+                    device=ref_point.device,
+                ),
+            ),  # create empty partitioning
+            sampler=sampler,
+            objective=objective,
+        )
     # ProjectedAcquisitionFunction requires this
     base_value_function.posterior_transform = None
 

test/acquisition/multi_objective/test_hypervolume_knowledge_gradient.py

@@ -4,6 +4,7 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
+import warnings
 from itertools import product
 from unittest import mock
 
@@ -22,6 +23,7 @@
     IdentityMCMultiOutputObjective,
 )
 from botorch.exceptions.errors import UnsupportedError
+from botorch.exceptions.warnings import NumericsWarning
 from botorch.models.deterministic import GenericDeterministicModel
 from botorch.models.gp_regression import SingleTaskGP
 from botorch.models.model_list_gp_regression import ModelListGP
@@ -366,24 +368,26 @@ def test_evaluate_q_hvkg(self):
                 for use_posterior_mean in (True, False):
                     with mock.patch.object(
                         ModelListGP, "fantasize", return_value=mfm
-                    ) as patch_f:
-                        with mock.patch(
-                            NO, new_callable=mock.PropertyMock
-                        ) as mock_num_outputs:
-                            mock_num_outputs.return_value = 3
-                            qHVKG = acqf_class(
-                                model=model,
-                                num_fantasies=n_f,
-                                objective=objective,
-                                ref_point=ref_point,
-                                num_pareto=num_pareto,
-                                use_posterior_mean=use_posterior_mean,
-                                **mf_kwargs,
-                            )
-                            val = qHVKG(X)
-                            patch_f.assert_called_once()
-                            cargs, ckwargs = patch_f.call_args
-                            self.assertEqual(ckwargs["X"].shape, torch.Size([1, 1, 1]))
+                    ) as patch_f, mock.patch(
+                        NO, new_callable=mock.PropertyMock
+                    ) as mock_num_outputs, warnings.catch_warnings(
+                        record=True
+                    ) as ws:
+                        mock_num_outputs.return_value = 3
+                        qHVKG = acqf_class(
+                            model=model,
+                            num_fantasies=n_f,
+                            objective=objective,
+                            ref_point=ref_point,
+                            num_pareto=num_pareto,
+                            use_posterior_mean=use_posterior_mean,
+                            **mf_kwargs,
+                        )
+                        val = qHVKG(X)
+                    patch_f.assert_called_once()
+                    cargs, ckwargs = patch_f.call_args
+                    self.assertEqual(ckwargs["X"].shape, torch.Size([1, 1, 1]))
+                    self.assertFalse(any(w.category is NumericsWarning for w in ws))
                     Ys = mean if use_posterior_mean else samples
                     objs = objective(Ys.squeeze(1)).view(-1, num_pareto, num_objectives)
                     if num_objectives == 2: