Merge: Fix CI (emdgroup#704)

- fixes an issue that came up after latest mypy release
- reverses the sorting order in our FPS utility to make the results
consistent with the new `fpsample>=1.0.0` release

Author: Scienfitz

baybe/surrogates/gaussian_process/core.py

@@ -145,6 +145,7 @@ def _fit(self, train_x: Tensor, train_y: Tensor) -> None:
         import botorch
         import gpytorch
         import torch
+        from botorch.models.transforms import Normalize, Standardize
 
         # FIXME[typing]: It seems there is currently no better way to inform the type
         #   checker that the attribute is available at the time of the function call
@@ -155,12 +156,12 @@ def _fit(self, train_x: Tensor, train_y: Tensor) -> None:
         numerical_idxs = context.get_numerical_indices(train_x.shape[-1])
 
         # For GPs, we let botorch handle the scaling. See [Scaling Workaround] above.
-        input_transform = botorch.models.transforms.Normalize(
+        input_transform = Normalize(
             train_x.shape[-1],
             bounds=context.parameter_bounds,
             indices=list(numerical_idxs),
         )
-        outcome_transform = botorch.models.transforms.Standardize(train_y.shape[-1])
+        outcome_transform = Standardize(train_y.shape[-1])
 
         # extract the batch shape of the training data
         batch_shape = train_x.shape[:-2]

baybe/utils/sampling_algorithms.py

@@ -114,7 +114,9 @@ def farthest_point_sampling(
         return list(range(n_samples))
 
     # Sort the points to produce the same result regardless of the input order
-    sort_idx = np.lexsort(tuple(points.T))
+    # The choice here is done to produce the same results as fpsample==1.0.0
+    # See https://github.com/leonardodalinky/fpsample/issues/10
+    sort_idx = np.lexsort(tuple(points.T))[::-1]
     points = points[sort_idx]
 
     # Pre-compute the pairwise distances between all points