Hypervolume Knowledge Gradient (#1950)

Summary:
Pull Request resolved: #1950

This includes support for decoupled evaluations and multi-fidelity optimization

Differential Revision: D47809667

fbshipit-source-id: 33021e43e3b65250f3c00e4652af74a07f1836a8

botorch/acquisition/knowledge_gradient.py

@@ -40,6 +40,7 @@
 from botorch.acquisition.cost_aware import CostAwareUtility
 from botorch.acquisition.monte_carlo import MCAcquisitionFunction, qSimpleRegret
 from botorch.acquisition.objective import MCAcquisitionObjective, PosteriorTransform
+from botorch.acquisition.projected import ProjectedAcquisitionFunction
 from botorch.exceptions.errors import UnsupportedError
 from botorch.models.model import Model
 from botorch.sampling.base import MCSampler
@@ -483,36 +484,6 @@ def forward(self, X: Tensor) -> Tensor:
         return values.mean(dim=0)
 
 
-class ProjectedAcquisitionFunction(AcquisitionFunction):
-    r"""
-    Defines a wrapper around  an `AcquisitionFunction` that incorporates the project
-    operator. Typically used to handle value functions in look-ahead methods.
-    """
-
-    def __init__(
-        self,
-        base_value_function: AcquisitionFunction,
-        project: Callable[[Tensor], Tensor],
-    ) -> None:
-        r"""
-        Args:
-            base_value_function: The wrapped `AcquisitionFunction`.
-            project: A callable mapping a `batch_shape x q x d` tensor of design
-                points to a tensor with shape `batch_shape x q_term x d` projected
-                to the desired target set (e.g. the target fidelities in case of
-                multi-fidelity optimization). For the basic case, `q_term = q`.
-        """
-        super().__init__(base_value_function.model)
-        self.base_value_function = base_value_function
-        self.project = project
-        self.objective = getattr(base_value_function, "objective", None)
-        self.posterior_transform = base_value_function.posterior_transform
-        self.sampler = getattr(base_value_function, "sampler", None)
-
-    def forward(self, X: Tensor) -> Tensor:
-        return self.base_value_function(self.project(X))
-
-
 def _get_value_function(
     model: Model,
     objective: Optional[MCAcquisitionObjective] = None,

botorch/acquisition/multi_objective/__init__.py

@@ -8,6 +8,10 @@
     ExpectedHypervolumeImprovement,
     MultiObjectiveAnalyticAcquisitionFunction,
 )
+from botorch.acquisition.multi_objective.hypervolume_knowledge_gradient import (
+    qHypervolumeKnowledgeGradient,
+    qMultiFidelityHypervolumeKnowledgeGradient,
+)
 from botorch.acquisition.multi_objective.max_value_entropy_search import (
     qMultiObjectiveMaxValueEntropy,
 )
@@ -35,6 +39,8 @@
     "get_default_partitioning_alpha",
     "prune_inferior_points_multi_objective",
     "qExpectedHypervolumeImprovement",
+    "qHypervolumeKnowledgeGradient",
+    "qMultiFidelityHypervolumeKnowledgeGradient",
     "qNoisyExpectedHypervolumeImprovement",
     "MOMF",
     "qMultiObjectiveMaxValueEntropy",