Add TODO notes

baybe/surrogates/multi_armed_bandit.py

@@ -29,6 +29,7 @@ class BernoulliMultiArmedBanditSurrogate(Surrogate):
     supports_transfer_learning: ClassVar[bool] = False
     # See base class.
 
+    # TODO: Introduce BetaPrior class
     prior: tuple[float, float] = field(
         default=(1, 1), converter=lambda x: cattrs.structure(x, tuple[float, float])
     )
@@ -74,6 +75,8 @@ def _posterior(self, candidates: Tensor) -> tuple[Tensor, Tensor]:
     def _fit(self, searchspace: SearchSpace, train_x: Tensor, train_y: Tensor) -> None:
         # See base class.
 
+        # TODO: Fix requirement of OHE encoding
+        # TODO: Generalize to arbitrary number of categorical parameters
         if not (
             (len(searchspace.parameters) == 1)
             and isinstance(p := searchspace.parameters[0], CategoricalParameter)
@@ -84,6 +87,8 @@ def _fit(self, searchspace: SearchSpace, train_x: Tensor, train_y: Tensor) -> No
                 f"spanned by exactly one categorical parameter using one-hot encoding."
             )
 
+        # TODO: Incorporate training target validation at the appropriate place in
+        #   the BayBE ecosystem.
         wins = (train_x * train_y).sum(axis=0)
         losses = (train_x * (1 - train_y)).sum(axis=0)
         self._win_lose_counts = np.vstack([wins, losses])