test fix in progress

autoPyTorch/data/tabular_feature_validator.py

@@ -77,10 +77,9 @@ class TabularFeatureValidator(BaseFeatureValidator):
     transformer.
 
     Attributes:
-        categories (List[List[str]]):
-            List for which an element at each index is a
-            list containing the categories for the respective
-            categorical column.
+        num_categories_per_col (List[int]):
+            List for which an element at each index is the number
+            of categories for the respective categorical column.
         transformed_columns (List[str])
             List of columns that were transformed.
         column_transformer (Optional[BaseEstimator])

autoPyTorch/datasets/time_series_dataset.py

@@ -559,7 +559,7 @@ def __init__(self,
         self.num_features: int = self.validator.feature_validator.num_features  # type: ignore[assignment]
         self.num_targets: int = self.validator.target_validator.out_dimensionality  # type: ignore[assignment]
 
-        self.categories = self.validator.feature_validator.categories
+        self.num_categories_per_col = self.validator.feature_validator.num_categories_per_col
 
         self.feature_shapes = self.validator.feature_shapes
         self.feature_names = tuple(self.validator.feature_names)
@@ -1072,7 +1072,7 @@ def get_required_dataset_info(self) -> Dict[str, Any]:
             'categorical_features': self.categorical_features,
             'numerical_columns': self.numerical_columns,
             'categorical_columns': self.categorical_columns,
-            'categories': self.categories,
+            'num_categories_per_col': self.num_categories_per_col,
         })
         return info
 

autoPyTorch/pipeline/components/preprocessing/tabular_preprocessing/column_splitting/ColumnSplitter.py

@@ -16,7 +16,7 @@
 
 class ColumnSplitter(autoPyTorchTabularPreprocessingComponent):
     """
-    Removes features that have the same value in the training data.
+    Splits categorical columns into embed or encode columns based on a hyperparameter.
     """
     def __init__(
         self,

autoPyTorch/pipeline/components/preprocessing/time_series_preprocessing/encoding/OneHotEncoder.py

@@ -19,14 +19,14 @@ def __init__(self,
     def fit(self, X: Dict[str, Any], y: Any = None) -> TimeSeriesBaseEncoder:
         OneHotEncoder.fit(self, X, y)
         categorical_columns = X['dataset_properties']['categorical_columns']
-        n_features_cat = X['dataset_properties']['categories']
+        num_categories_per_col = X['dataset_properties']['num_categories_per_col']
         feature_names = X['dataset_properties']['feature_names']
         feature_shapes = X['dataset_properties']['feature_shapes']
 
-        if len(n_features_cat) == 0:
-            n_features_cat = self.preprocessor['categorical'].categories  # type: ignore
+        if len(num_categories_per_col) == 0:
+            num_categories_per_col = [len(cat) for cat in self.preprocessor['categorical'].categories]  # type: ignore
         for i, cat_column in enumerate(categorical_columns):
-            feature_shapes[feature_names[cat_column]] = len(n_features_cat[i])
+            feature_shapes[feature_names[cat_column]] = num_categories_per_col[i]
         self.feature_shapes = feature_shapes
         return self
 

autoPyTorch/pipeline/components/preprocessing/time_series_preprocessing/encoding/time_series_base_encoder.py

@@ -15,11 +15,11 @@ def __init__(self) -> None:
         super(TimeSeriesBaseEncoder, self).__init__()
         self.add_fit_requirements([
             FitRequirement('categorical_columns', (List,), user_defined=True, dataset_property=True),
-            FitRequirement('categories', (List,), user_defined=True, dataset_property=True),
+            FitRequirement('num_categories_per_col', (List,), user_defined=True, dataset_property=True),
             FitRequirement('feature_names', (tuple,), user_defined=True, dataset_property=True),
             FitRequirement('feature_shapes', (Dict, ), user_defined=True, dataset_property=True),
         ])
-        self.feature_shapes: Union[Dict[str, int]] = {}
+        self.feature_shapes: Dict[str, int] = {}
 
     def transform(self, X: Dict[str, Any]) -> Dict[str, Any]:
         """

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

@@ -447,7 +447,13 @@ def _fit(self, X: Dict[str, Any], y: Any = None, **kwargs: Any) -> 'TrainerChoic
             raise RuntimeError("Budget exhausted without finishing an epoch.")
 
         if self.choice.use_stochastic_weight_averaging and self.choice.swa_updated:
-            use_double = 'float64' in X['preprocessed_dtype']
+            # By default, we assume the data is double. Only if the data was preprocessed,
+            # we check the dtype and use it accordingly
+            preprocessed_dtype = X.get('preprocessed_dtype', None)
+            if preprocessed_dtype is None:
+                use_double = True
+            else:
+                use_double = 'float64' in preprocessed_dtype
 
             # update batch norm statistics
             swa_model = self.choice.swa_model.double() if use_double else self.choice.swa_model
@@ -458,7 +464,6 @@ def _fit(self, X: Dict[str, Any], y: Any = None, **kwargs: Any) -> 'TrainerChoic
                 # we update only the last network which pertains to the stochastic weight averaging model
                 snapshot_model = self.choice.model_snapshots[-1].double() if use_double else self.choice.model_snapshots[-1]
                 swa_utils.update_bn(X['train_data_loader'], snapshot_model)
-                update_model_state_dict_from_swa(X['network_snapshots'][-1], self.choice.swa_model.state_dict())
 
         # wrap up -- add score if not evaluating every epoch
         if not self.eval_valid_each_epoch(X):