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Co-authored-by: nabenabe0928 <[email protected]>

autoPyTorch/pipeline/components/setup/network_embedding/__init__.py

@@ -155,8 +155,10 @@ def get_hyperparameter_search_space(
                     default = default_
                     break
 
-        categorical_columns = dataset_properties['categorical_columns'] \
-            if isinstance(dataset_properties['categorical_columns'], List) else []
+        if isinstance(dataset_properties['categorical_columns'], list):
+            categorical_columns = dataset_properties['categorical_columns']
+        else:
+            categorical_columns = []
 
         updates = self._get_search_space_updates()
         if '__choice__' in updates.keys():

autoPyTorch/pipeline/components/training/trainer/base_trainer.py

@@ -338,8 +338,8 @@ def _swa_update(self) -> None:
         """
         perform swa model update
         """
-        assert self.swa_model is not None, "SWA model can't be none when" \
-                                           " stochastic weight averaging is enabled"
+        if self.swa_model is None:
+            raise ValueError("SWA model cannot be none when stochastic weight averaging is enabled")
         self.swa_model.update_parameters(self.model)
         self.swa_updated = True
 
@@ -350,8 +350,8 @@ def _se_update(self, epoch: int) -> None:
             epoch (int):
                 current epoch
         """
-        assert self.model_snapshots is not None, "model snapshots container can't be " \
-                                                 "none when snapshot ensembling is enabled"
+        if self.model_snapshots is None:
+            raise ValueError("model snapshots cannot be None when snapshot ensembling is enabled")
         is_last_epoch = (epoch == self.budget_tracker.max_epochs)
         if is_last_epoch and self.use_stochastic_weight_averaging:
             model_copy = deepcopy(self.swa_model)