Add input constructor for qMultiFidelityHypervolumeKnowledgeGradient (#…

…2524)

Summary:
Pull Request resolved: #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 +1367,76 @@ 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 +1862,18 @@ 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