Merge fc601b8 into cca54db

botorch/optim/homotopy.py

@@ -20,29 +20,24 @@ class HomotopySchedule(ABC):
     @abstractmethod
     def num_steps(self) -> int:
         """Number of steps in the schedule."""
-        pass
 
     @property
     @abstractmethod
     def value(self) -> Any:
         """Current value in the schedule."""
-        pass
 
     @property
     @abstractmethod
     def should_stop(self) -> bool:
         """Return true if we have incremented past the end of the schedule."""
-        pass
 
     @abstractmethod
     def restart(self) -> None:
         """Restart the schedule to start from the beginning."""
-        pass
 
     @abstractmethod
     def step(self) -> None:
         """Move to solving the next problem."""
-        pass
 
 
 class FixedHomotopySchedule(HomotopySchedule):

test/optim/test_homotopy.py

@@ -7,6 +7,7 @@
 
 import torch
 from botorch.acquisition import PosteriorMean
+from botorch.acquisition.monte_carlo import qExpectedImprovement
 from botorch.models import GenericDeterministicModel
 from botorch.optim.homotopy import (
     FixedHomotopySchedule,
@@ -142,6 +143,20 @@ def test_optimize_acqf_homotopy(self):
         )
         self.assertEqual(candidate[0, 0], torch.tensor(1, **tkwargs))
 
+        # With q > 1.
+        acqf = qExpectedImprovement(model=model, best_f=0.0)
+        candidate, acqf_val = optimize_acqf_homotopy(
+            q=3,
+            acq_function=acqf,
+            bounds=torch.tensor([[-10, -10], [5, 5]]).to(**tkwargs),
+            homotopy=Homotopy(homotopy_parameters=[hp]),
+            num_restarts=2,
+            raw_samples=16,
+            fixed_features=fixed_features,
+        )
+        self.assertEqual(candidate.shape, torch.Size([3, 2]))
+        self.assertEqual(acqf_val.shape, torch.Size([3]))
+
     def test_prune_candidates(self):
         tkwargs = {"device": self.device, "dtype": torch.double}
         # no pruning