Add input constructor for qMultiFidelityHypervolumeKnowledgeGradient (p…

…ytorch#2524)

Summary:
Pull Request resolved: pytorch#2524

Adds new input constructors for qMultiFidelityHypervolumeKnowledgeGradient.

Reviewed By: Balandat

Differential Revision: D62459735

botorch/acquisition/input_constructors.py

@@ -65,6 +65,7 @@
 from botorch.acquisition.multi_objective.hypervolume_knowledge_gradient import (
     _get_hv_value_function,
     qHypervolumeKnowledgeGradient,
+    qMultiFidelityHypervolumeKnowledgeGradient,
 )
 from botorch.acquisition.multi_objective.logei import (
     qLogExpectedHypervolumeImprovement,
@@ -1274,21 +1275,6 @@ def construct_inputs_qKG(
     return inputs_qkg
 
 
-def _get_ref_point(
-    objective_thresholds: Tensor,
-    objective: Optional[MCMultiOutputObjective] = None,
-) -> Tensor:
-
-    if objective is None:
-        ref_point = objective_thresholds
-    elif isinstance(objective, RiskMeasureMCObjective):
-        ref_point = objective.preprocessing_function(objective_thresholds)
-    else:
-        ref_point = objective(objective_thresholds)
-
-    return ref_point
-
-
 @acqf_input_constructor(qHypervolumeKnowledgeGradient)
 def construct_inputs_qHVKG(
     model: Model,
@@ -1381,6 +1367,74 @@ def construct_inputs_qMFKG(
     }
 
 
+@acqf_input_constructor(qMultiFidelityHypervolumeKnowledgeGradient)
+def construct_inputs_qMFHVKG(
+    model: Model,
+    training_data: MaybeDict[SupervisedDataset],
+    bounds: list[tuple[float, float]],
+    target_fidelities: dict[int, Union[int, float]],
+    objective_thresholds: Tensor,
+    objective: Optional[MCMultiOutputObjective] = None,
+    posterior_transform: Optional[PosteriorTransform] = None,
+    fidelity_weights: Optional[dict[int, float]] = None,
+    cost_intercept: float = 1.0,
+    num_trace_observations: int = 0,
+    num_fantasies: int = 8,
+    num_pareto: int = 10,
+    **optimize_objective_kwargs: TOptimizeObjectiveKwargs,
+) -> dict[str, Any]:
+    r"""Construct kwargs for `qMultiFidelityHypervolumeKnowledgeGradient` constructor."""
+
+    inputs_mf = construct_inputs_mf_base(
+        target_fidelities=target_fidelities,
+        fidelity_weights=fidelity_weights,
+        cost_intercept=cost_intercept,
+        num_trace_observations=num_trace_observations,
+    )
+
+    if num_trace_observations > 0:
+        raise NotImplementedError(
+            "Trace observations are not currently supported "
+            "by `qMultiFidelityHypervolumeKnowledgeGradient`."
+        )
+
+    del inputs_mf["expand"]
+
+    X = _get_dataset_field(training_data, "X", first_only=True)
+    _bounds = torch.as_tensor(bounds, dtype=X.dtype, device=X.device)
+
+    ref_point = _get_ref_point(
+        objective_thresholds=objective_thresholds, objective=objective
+    )
+
+    acq_function = _get_hv_value_function(
+        model=model,
+        ref_point=ref_point,
+        use_posterior_mean=True,
+        objective=objective,
+    )
+
+    _, current_value = optimize_objective(
+        model=model,
+        bounds=_bounds.t(),
+        q=num_pareto,
+        acq_function=acq_function,
+        fixed_features=target_fidelities,
+        **optimize_objective_kwargs,
+    )
+
+    return {
+        "model": model,
+        "objective": objective,
+        "ref_point": ref_point,
+        "num_fantasies": num_fantasies,
+        "num_pareto": num_pareto,
+        "current_value": current_value.detach().cpu().max(),
+        "target_fidelities": target_fidelities,
+        **inputs_mf,
+    }
+
+
 @acqf_input_constructor(qMultiFidelityMaxValueEntropy)
 def construct_inputs_qMFMES(
     model: Model,
@@ -1806,3 +1860,18 @@ def construct_inputs_NIPV(
         "posterior_transform": posterior_transform,
     }
     return inputs
+
+
+def _get_ref_point(
+    objective_thresholds: Tensor,
+    objective: Optional[MCMultiOutputObjective] = None,
+) -> Tensor:
+
+    if objective is None:
+        ref_point = objective_thresholds
+    elif isinstance(objective, RiskMeasureMCObjective):
+        ref_point = objective.preprocessing_function(objective_thresholds)
+    else:
+        ref_point = objective(objective_thresholds)
+
+    return ref_point

botorch/acquisition/multi_objective/hypervolume_knowledge_gradient.py

@@ -389,7 +389,10 @@ def __init__(
         )
         self.project = project
         if kwargs.get("expand") is not None:
-            raise NotImplementedError("Trace observations are not currently supported.")
+            raise NotImplementedError(
+                "Trace observations are not currently supported "
+                "by `qMultiFidelityHypervolumeKnowledgeGradient`."
+            )
         self.expand = lambda X: X
         self.valfunc_cls = valfunc_cls
         self.valfunc_argfac = valfunc_argfac