Allow monotonic rejection GP to initialize its inducing point allocat…

…or from config. (#485)

Summary:
Pull Request resolved: #485

Monotonic rejection GP used to completely ignore any inducing point config, now it follows other models.

Differential Revision: D67225873

aepsych/models/monotonic_rejection_gp.py

@@ -19,6 +19,7 @@
 from aepsych.means.constant_partial_grad import ConstantMeanPartialObsGrad
 from aepsych.models.base import AEPsychMixin
 from aepsych.models.inducing_points import AutoAllocator, SobolAllocator
+from aepsych.models.utils import select_inducing_points
 from aepsych.utils import _process_bounds, get_optimizer_options, promote_0d
 from botorch.fit import fit_gpytorch_mll
 from botorch.models.utils.inducing_point_allocators import InducingPointAllocator
@@ -99,9 +100,10 @@ def __init__(
         # TODO: This allocator *must* be SobolAllocator and not the set one. This
         # suggests that this model doesn't actually properly use data for inducing
         # points properly.
-        inducing_points = SobolAllocator(
-            bounds=torch.stack([lb, ub]), dim=self.dim
-        ).allocate_inducing_points(num_inducing=self.inducing_size)
+        inducing_points = select_inducing_points(
+            allocator=SobolAllocator(bounds=torch.stack([lb, ub]), dim=self.dim),
+            inducing_size=self.inducing_size,
+        )
 
         inducing_points_aug = self._augment_with_deriv_index(inducing_points, 0)
         variational_distribution = CholeskyVariationalDistribution(
@@ -166,10 +168,11 @@ def fit(self, train_x: torch.Tensor, train_y: torch.Tensor, **kwargs) -> None:
         """
         self.set_train_data(train_x, train_y)
 
-        self.inducing_points = self.inducing_point_method.allocate_inducing_points(
-            num_inducing=self.inducing_size,
+        self.inducing_points = select_inducing_points(
+            allocator=self.inducing_point_method,
+            inducing_size=self.inducing_size,
             covar_module=self.covar_module,
-            inputs=self._augment_with_deriv_index(self.train_inputs[0], 0),
+            X=self._augment_with_deriv_index(self.train_inputs[0], 0),
         )
         self._set_model(train_x, train_y)