Make scheduler public

package/samplers/cmamae/sampler.py

@@ -113,7 +113,8 @@ def __init__(
         # Number of solutions generated in each batch from pyribs.
         self._batch_size = n_emitters * emitter_batch_size
 
-        self._scheduler = Scheduler(
+        # Public to allow access for, e.g., visualization.
+        self.scheduler = Scheduler(
             archive,
             emitters,
             result_archive=result_archive,
@@ -161,7 +162,7 @@ def sample_relative(
         self._validate_param_names(search_space.keys())
 
         # Note: Batch optimization means we need to enqueue trials.
-        solutions = self._scheduler.ask()
+        solutions = self.scheduler.ask()
         next_params = self._convert_to_optuna_params(solutions[0])
         for solution in solutions[1:]:
             params = self._convert_to_optuna_params(solution)
@@ -196,7 +197,7 @@ def after_trial(
 
         # Tell the batch results to external sampler once the batch is ready.
         values_to_tell = np.asarray(self._values_to_tell)[np.argsort(self._stored_trial_numbers)]
-        self._scheduler.tell(objective=values_to_tell[:, 0], measures=values_to_tell[:, 1:])
+        self.scheduler.tell(objective=values_to_tell[:, 0], measures=values_to_tell[:, 1:])
 
         # Empty the results.
         self._values_to_tell = []