Simplify multi-space optimization logic

baybe/recommenders/pure/bayesian/botorch.py

@@ -7,6 +7,7 @@
 from collections.abc import Collection, Iterable
 from typing import TYPE_CHECKING, Any, ClassVar
 
+import numpy as np
 import pandas as pd
 from attrs import define, field
 from attrs.converters import optional as optional_c
@@ -447,10 +448,17 @@ def __str__(self) -> str:
 
     def _optimize_subspaces_without_cardinality_constraints(
         self, subspaces: Iterable[SubspaceContinuous], batch_size: int
-    ) -> tuple[Tensor, Tensor]:
-        import torch
+    ) -> tuple[Tensor, float]:
+        """Find the optimum candidates from multiple subspaces.
+
+        Args:
+            subspaces: The subspaces to consider for the optimization.
+            batch_size: The number of points to be recommended.
 
-        acqf_values_all: list[Tensor] = []
+        Returns:
+            The batch of candidates and the corresponding acquisition value.
+        """
+        acqf_values_all: list[float] = []
         points_all: list[Tensor] = []
 
         for subspace in subspaces:
@@ -459,19 +467,18 @@ def _optimize_subspaces_without_cardinality_constraints(
                 f = self._recommend_continuous_without_cardinality_constraints
                 points_i, acqf_values_i = f(subspace, batch_size)
 
-                # Append recommendation list and acquisition function values
+                # Append optimization results
                 points_all.append(points_i.unsqueeze(0))
-                acqf_values_all.append(acqf_values_i.unsqueeze(0))
+                acqf_values_all.append(acqf_values_i.item())
 
-            # # The optimization problem may be infeasible for certain inactive
-            # # parameters. The optimize_acqf raises a ValueError when the optimization
-            # # problem is infeasible.
+            # The optimization problem may be infeasible in certain subspaces
             except ValueError:
                 pass
 
-        # Find the best option
-        points = torch.cat(points_all)[torch.argmax(torch.cat(acqf_values_all)), :]
-        acqf_values = torch.max(torch.cat(acqf_values_all))
+        # Find the best option f
+        best_idx = np.argmax(acqf_values_all)
+        points = points_all[best_idx]
+        acqf_values = acqf_values_all[best_idx]
 
         return points, acqf_values