Adjust acqf objective logic

Author: Scienfitz

baybe/acquisition/base.py

@@ -115,22 +115,29 @@ def to_botorch(
         # Add acquisition objective / best observed value
         match objective:
             case SingleTargetObjective(NumericalTarget(mode=TargetMode.MIN)):
+                # Adjust best_f
                 if "best_f" in signature_params:
                     additional_params["best_f"] = (
                         bo_surrogate.posterior(train_x).mean.min().item()
                     )
                     if issubclass(acqf_cls, bo_acqf.MCAcquisitionFunction):
                         additional_params["best_f"] *= -1.0
 
+                # Adjust objective
                 if issubclass(
-                    acqf_cls, bo_acqf.AnalyticAcquisitionFunction
-                ) and not issubclass(acqf_cls, bo_acqf.PosteriorStandardDeviation):
-                    # Minimize acqfs in case the target should be minimized. PSTD  is
-                    # exempt as the direction does not depend on the target type.
-                    additional_params["maximize"] = False
-                elif issubclass(acqf_cls, bo_acqf.qNegIntegratedPosteriorVariance):
-                    # qNIPV is valid but does not require any adjusted params
+                    acqf_cls,
+                    (
+                        bo_acqf.qNegIntegratedPosteriorVariance,
+                        bo_acqf.PosteriorStandardDeviation,
+                        bo_acqf.qPosteriorStandardDeviation,
+                    ),
+                ):
+                    # The active learning acqfs are valid but no changes based on the
+                    # target direction are required.
                     pass
+                elif issubclass(acqf_cls, bo_acqf.AnalyticAcquisitionFunction):
+                    # Minimize acqfs in case the target should be minimized.
+                    additional_params["maximize"] = False
                 elif issubclass(acqf_cls, bo_acqf.MCAcquisitionFunction):
                     additional_params["objective"] = LinearMCObjective(
                         torch.tensor([-1.0])