prune baseline points in qLogNEI by default (#2762)

Summary:
X-link: facebookresearch/aepsych#666

Pull Request resolved: #2762

See title

Reviewed By: SebastianAment

Differential Revision: D70665739

fbshipit-source-id: 46bf660ea3448887a8210723cd68bdee04065cdd

Author: sdaulton

botorch/acquisition/logei.py

@@ -273,7 +273,7 @@ def __init__(
         constraints: list[Callable[[Tensor], Tensor]] | None = None,
         eta: Tensor | float = 1e-3,
         fat: bool = True,
-        prune_baseline: bool = False,
+        prune_baseline: bool = True,
         cache_root: bool = True,
         tau_max: float = TAU_MAX,
         tau_relu: float = TAU_RELU,

test/optim/test_optimize_mixed.py

@@ -479,7 +479,9 @@ def test_optimize_acqf_mixed_binary_only(self) -> None:
         # get multiple candidates
         root = torch.zeros(dim, device=self.device)
         model = QuadraticDeterministicModel(root)
-        acqf = qLogNoisyExpectedImprovement(model=model, X_baseline=train_X)
+        acqf = qLogNoisyExpectedImprovement(
+            model=model, X_baseline=train_X, prune_baseline=False
+        )
         options["initialization_strategy"] = "equally_spaced"
         candidates, _ = optimize_acqf_mixed_alternating(
             acq_function=acqf,