add input constructor for qEUBO

botorch/acquisition/input_constructors.py

@@ -83,7 +83,10 @@
     MCAcquisitionObjective,
     PosteriorTransform,
 )
-from botorch.acquisition.preference import AnalyticExpectedUtilityOfBestOption
+from botorch.acquisition.preference import (
+    AnalyticExpectedUtilityOfBestOption,
+    qExpectedUtilityOfBestOption,
+)
 from botorch.acquisition.risk_measures import RiskMeasureMCObjective
 from botorch.acquisition.utils import (
     compute_best_feasible_objective,
@@ -1328,6 +1331,57 @@
         }
 
 
+@acqf_input_constructor(qExpectedUtilityOfBestOption)
+def construct_inputs_qeubo(
+    model: Model,
+    pref_model: Optional[Model] = None,
+    sample_multiplier: Optional[float] = 1.0,
+    **kwargs,
+) -> Dict[str, Any]:
+    r"""Construct kwargs for the `qExpectedUtilityOfBestOption` (qEUBO) constructor.
+
+    `model` is the primary model defined over the parameter space. It can be the
+    outcomde model in BOPE or the preference model in PBO. `pref_model` is the model
+    defined over the outcome/metric space, which is typically the preference model
+    in BOPE.
+
+    If both model and pref_model exist, we are performing Bayesian Optimization with
+    Preference Exploration (BOPE). When only pref_model is None, we are performing
+    preferential BO (PBO).
+
+    Args:
+        model: The outcome model to be used in the acquisition function in BOPE
+            when pref_model exists; otherwise, model is the preference model and
+            we are doing Preferential BO
+        pref_model: The preference model to be used in preference exploration as in
+            BOPE; if None, we are doing PBO and model is the preference model.
+        sample_multiplier: The scale factor for the single-sample model.
+
+    Returns:
+        A dict mapping kwarg names of the constructor to values.
+    """
+    if pref_model is None:
+        return {
+            "pref_model": model,
+            "outcome_model": None,
+            **kwargs,
+        }
+    else:
+        # construct a deterministic fixed single sample model from `model`
+        # i.e., performing EUBO-zeta by default as described
+        # in https://arxiv.org/abs/2203.11382
+        # using pref_model.dim instead of model.num_outputs here as MTGP's
+        # num_outputs could be tied to the number of tasks
+        w = torch.randn(pref_model.dim) * sample_multiplier
+        one_sample_outcome_model = FixedSingleSampleModel(model=model, w=w)
+
+        return {
+            "pref_model": pref_model,
+            "outcome_model": one_sample_outcome_model,
+            **kwargs,
+        }
+
+
 def get_best_f_analytic(
     training_data: MaybeDict[SupervisedDataset],
     posterior_transform: Optional[PosteriorTransform] = None,

test/acquisition/test_input_constructors.py

@@ -78,7 +78,10 @@
     LinearMCObjective,
     ScalarizedPosteriorTransform,
 )
-from botorch.acquisition.preference import AnalyticExpectedUtilityOfBestOption
+from botorch.acquisition.preference import (
+    AnalyticExpectedUtilityOfBestOption,
+    qExpectedUtilityOfBestOption,
+)
 from botorch.acquisition.utils import (
     expand_trace_observations,
     project_to_target_fidelity,
@@ -393,7 +396,10 @@ def test_construct_inputs_noisy_ei(self) -> None:
                 with self.assertRaisesRegex(ValueError, "Field `X` must be shared"):
                     c(model=mock_model, training_data=self.multiX_multiY)
 
-    def test_construct_inputs_constrained_analytic_eubo(self) -> None:
+    def test_construct_inputs_eubo(self) -> None:
+        """test input constructor for analytical EUBO and MC qEUBO"""
+
+        # Set up
         # create dummy modellist gp
         n = 10
         X = torch.linspace(0, 0.95, n).unsqueeze(dim=-1)
@@ -409,21 +415,40 @@ def test_construct_inputs_constrained_analytic_eubo(self) -> None:
         )
         self.assertEqual(model.num_outputs, 6)
 
-        c = get_acqf_input_constructor(AnalyticExpectedUtilityOfBestOption)
         mock_pref_model = self.mock_model
         # assume we only have a preference model with 2 outcomes
         mock_pref_model.dim = 2
         mock_pref_model.datapoints = torch.tensor([])
 
-        # test basic construction
-        kwargs = c(model=model, pref_model=mock_pref_model)
-        self.assertIsInstance(kwargs["outcome_model"], FixedSingleSampleModel)
-        self.assertIs(kwargs["pref_model"], mock_pref_model)
-        self.assertIsNone(kwargs["previous_winner"])
-        # test instantiation
-        AnalyticExpectedUtilityOfBestOption(**kwargs)
+        for eubo_acqf in (
+            AnalyticExpectedUtilityOfBestOption,
+            qExpectedUtilityOfBestOption,
+        ):
+            c = get_acqf_input_constructor(eubo_acqf)
+
+            # test basic construction
+            kwargs = c(model=model, pref_model=mock_pref_model)
+            self.assertIsInstance(kwargs["outcome_model"], FixedSingleSampleModel)
+            self.assertIs(kwargs["pref_model"], mock_pref_model)
+            if eubo_acqf is AnalyticExpectedUtilityOfBestOption:
+                self.assertIsNone(kwargs["previous_winner"])
+            # test instantiation
+            eubo_acqf(**kwargs)
+
+            # test sample_multiplier
+            torch.manual_seed(123)
+            kwargs = c(
+                model=model,
+                pref_model=mock_pref_model,
+                sample_multiplier=1e6,
+            )
+            # w by default is drawn from std normal and very unlikely to be > 10.0
+            self.assertTrue((kwargs["outcome_model"].w.abs() > 10.0).all())
+            # Check w has the right dimension that agrees with the preference model
+            self.assertEqual(kwargs["outcome_model"].w.shape[-1], mock_pref_model.dim)
 
         # test previous_winner
+        c = get_acqf_input_constructor(AnalyticExpectedUtilityOfBestOption)
         previous_winner = torch.randn(mock_pref_model.dim)
         kwargs = c(
             model=model,
@@ -434,18 +459,6 @@ def test_construct_inputs_constrained_analytic_eubo(self) -> None:
         # test instantiation
         AnalyticExpectedUtilityOfBestOption(**kwargs)
 
-        # test sample_multiplier
-        torch.manual_seed(123)
-        kwargs = c(
-            model=model,
-            pref_model=mock_pref_model,
-            sample_multiplier=1e6,
-        )
-        # w by default is drawn from std normal and very unlikely to be > 10.0
-        self.assertTrue((kwargs["outcome_model"].w.abs() > 10.0).all())
-        # Check w has the right dimension that agrees with the preference model
-        self.assertEqual(kwargs["outcome_model"].w.shape[-1], mock_pref_model.dim)
-
 
 class TestMCAcquisitionFunctionInputConstructors(InputConstructorBaseTestCase):
     def test_construct_inputs_mc_base(self) -> None: