raise exception if X_pending is set on underlying AF in prior-guided AF

botorch/acquisition/prior_guided.py

@@ -21,6 +21,7 @@
 
 from botorch.acquisition.acquisition import AcquisitionFunction
 from botorch.acquisition.monte_carlo import SampleReducingMCAcquisitionFunction
+from botorch.exceptions.errors import BotorchError
 from botorch.utils.transforms import concatenate_pending_points, t_batch_mode_transform
 from torch import Tensor
 
@@ -60,8 +61,16 @@ def __init__(
                 [Hvarfner2022]_.
             X_pending: `n x d` Tensor with `n` `d`-dim design points that have
                 been submitted for evaluation but have not yet been evaluated.
+                Note: X_pending should be provided as an argument to or set on
+                `PriorGuidedAcquisitionFunction`, but not set on the underlying
+                acquisition function.
         """
         super().__init__(model=acq_function.model)
+        if getattr(acq_function, "X_pending", None) is not None:
+            raise BotorchError(
+                "X_pending is set on acq_function, but should be set on "
+                "`PriorGuidedAcquisitionFunction`."
+            )
         self.acq_func = acq_function
         self.prior_module = prior_module
         self._log = log

test/acquisition/test_prior_guided.py

@@ -10,6 +10,7 @@
 from botorch.acquisition.analytic import ExpectedImprovement
 from botorch.acquisition.monte_carlo import qExpectedImprovement
 from botorch.acquisition.prior_guided import PriorGuidedAcquisitionFunction
+from botorch.exceptions.errors import BotorchError
 from botorch.models import SingleTaskGP
 from botorch.utils.testing import BotorchTestCase
 from botorch.utils.transforms import match_batch_shape
@@ -122,3 +123,18 @@ def test_prior_guided_mc_acquisition_function(self):
                 expected_val = ei._sample_reduction(ei._q_reduction(weighted_val))
 
                 self.assertTrue(torch.equal(val, expected_val))
+
+    def test_X_pending_error(self) -> None:
+        X_pending = torch.rand(2, 3, dtype=torch.double, device=self.device)
+        model = SingleTaskGP(train_X=self.train_X, train_Y=self.train_Y)
+        ei = qExpectedImprovement(model=model, best_f=0.0)
+        ei.set_X_pending(X_pending)
+        msg = (
+            "X_pending is set on acq_function, but should be set on "
+            "`PriorGuidedAcquisitionFunction`."
+        )
+        with self.assertRaisesRegex(BotorchError, msg):
+            PriorGuidedAcquisitionFunction(
+                acq_function=ei,
+                prior_module=self.prior,
+            )