[FIX] Datamanager in memory

autoPyTorch/evaluation/abstract_evaluator.py

@@ -433,34 +433,16 @@ def __init__(self, backend: Backend,
         self.backend: Backend = backend
         self.queue = queue
 
-        self.datamanager: BaseDataset = self.backend.load_datamanager()
-
-        assert self.datamanager.task_type is not None, \
-            "Expected dataset {} to have task_type got None".format(self.datamanager.__class__.__name__)
-        self.task_type = STRING_TO_TASK_TYPES[self.datamanager.task_type]
-        self.output_type = STRING_TO_OUTPUT_TYPES[self.datamanager.output_type]
-        self.issparse = self.datamanager.issparse
-
         self.include = include
         self.exclude = exclude
         self.search_space_updates = search_space_updates
 
-        self.X_train, self.y_train = self.datamanager.train_tensors
-
-        if self.datamanager.val_tensors is not None:
-            self.X_valid, self.y_valid = self.datamanager.val_tensors
-        else:
-            self.X_valid, self.y_valid = None, None
-
-        if self.datamanager.test_tensors is not None:
-            self.X_test, self.y_test = self.datamanager.test_tensors
-        else:
-            self.X_test, self.y_test = None, None
-
         self.metric = metric
 
         self.seed = seed
 
+        self._init_datamanager_info()
+
         # Flag to save target for ensemble
         self.output_y_hat_optimization = output_y_hat_optimization
 
@@ -497,12 +479,6 @@ def __init__(self, backend: Backend,
                 else:
                     raise ValueError('task {} not available'.format(self.task_type))
             self.predict_function = self._predict_proba
-        self.dataset_properties = self.datamanager.get_dataset_properties(
-            get_dataset_requirements(info=self.datamanager.get_required_dataset_info(),
-                                     include=self.include,
-                                     exclude=self.exclude,
-                                     search_space_updates=self.search_space_updates
-                                     ))
 
         self.additional_metrics: Optional[List[autoPyTorchMetric]] = None
         metrics_dict: Optional[Dict[str, List[str]]] = None
@@ -542,6 +518,53 @@ def __init__(self, backend: Backend,
         self.logger.debug("Fit dictionary in Abstract evaluator: {}".format(dict_repr(self.fit_dictionary)))
         self.logger.debug("Search space updates :{}".format(self.search_space_updates))
 
+    def _init_datamanager_info(
+        self,
+    ) -> None:
+        """
+        Initialises instance attributes that come from the datamanager.
+        For example,
+            X_train, y_train, etc.
+        """
+
+        datamanager: BaseDataset = self.backend.load_datamanager()
+
+        assert datamanager.task_type is not None, \
+            "Expected dataset {} to have task_type got None".format(datamanager.__class__.__name__)
+        self.task_type = STRING_TO_TASK_TYPES[datamanager.task_type]
+        self.output_type = STRING_TO_OUTPUT_TYPES[datamanager.output_type]
+        self.issparse = datamanager.issparse
+
+        self.X_train, self.y_train = datamanager.train_tensors
+
+        if datamanager.val_tensors is not None:
+            self.X_valid, self.y_valid = datamanager.val_tensors
+        else:
+            self.X_valid, self.y_valid = None, None
+
+        if datamanager.test_tensors is not None:
+            self.X_test, self.y_test = datamanager.test_tensors
+        else:
+            self.X_test, self.y_test = None, None
+
+        self.resampling_strategy = datamanager.resampling_strategy
+
+        self.num_classes: Optional[int] = datamanager.num_classes if hasattr(datamanager, 'num_classes') \
+            else None
+
+        self.dataset_properties = datamanager.get_dataset_properties(
+            get_dataset_requirements(info=datamanager.get_required_dataset_info(),
+                                     include=self.include,
+                                     exclude=self.exclude,
+                                     search_space_updates=self.search_space_updates
+                                     ))
+        self.splits = datamanager.splits
+        if self.splits is None:
+            raise AttributeError("Must have called create_splits on {}".format(datamanager.__class__.__name__))
+
+        # delete datamanager from memory
+        del datamanager
+
     def _init_fit_dictionary(
         self,
         logger_port: int,
@@ -988,21 +1011,20 @@ def _ensure_prediction_array_sizes(self, prediction: np.ndarray,
             (np.ndarray):
                 The formatted prediction
         """
-        assert self.datamanager.num_classes is not None, "Called function on wrong task"
-        num_classes: int = self.datamanager.num_classes
+        assert self.num_classes is not None, "Called function on wrong task"
 
         if self.output_type == MULTICLASS and \
-                prediction.shape[1] < num_classes:
+                prediction.shape[1] < self.num_classes:
             if Y_train is None:
                 raise ValueError('Y_train must not be None!')
             classes = list(np.unique(Y_train))
 
             mapping = dict()
-            for class_number in range(num_classes):
+            for class_number in range(self.num_classes):
                 if class_number in classes:
                     index = classes.index(class_number)
                     mapping[index] = class_number
-            new_predictions = np.zeros((prediction.shape[0], num_classes),
+            new_predictions = np.zeros((prediction.shape[0], self.num_classes),
                                        dtype=np.float32)
 
             for index in mapping:

autoPyTorch/evaluation/test_evaluator.py

@@ -145,17 +145,12 @@ def __init__(
             search_space_updates=search_space_updates
         )
 
-        if not isinstance(self.datamanager.resampling_strategy, (NoResamplingStrategyTypes)):
-            resampling_strategy = self.datamanager.resampling_strategy
+        if not isinstance(self.resampling_strategy, (NoResamplingStrategyTypes)):
             raise ValueError(
                 f'resampling_strategy for TestEvaluator must be in '
-                f'NoResamplingStrategyTypes, but got {resampling_strategy}'
+                f'NoResamplingStrategyTypes, but got {self.resampling_strategy}'
             )
 
-        self.splits = self.datamanager.splits
-        if self.splits is None:
-            raise AttributeError("create_splits must be called  in {}".format(self.datamanager.__class__.__name__))
-
     def fit_predict_and_loss(self) -> None:
 
         split_id = 0

autoPyTorch/evaluation/train_evaluator.py

@@ -152,16 +152,12 @@ def __init__(self, backend: Backend, queue: Queue,
             search_space_updates=search_space_updates
         )
 
-        if not isinstance(self.datamanager.resampling_strategy, (CrossValTypes, HoldoutValTypes)):
-            resampling_strategy = self.datamanager.resampling_strategy
+        if not isinstance(self.resampling_strategy, (CrossValTypes, HoldoutValTypes)):
             raise ValueError(
                 f'resampling_strategy for TrainEvaluator must be in '
-                f'(CrossValTypes, HoldoutValTypes), but got {resampling_strategy}'
+                f'(CrossValTypes, HoldoutValTypes), but got {self.resampling_strategy}'
             )
 
-        self.splits = self.datamanager.splits
-        if self.splits is None:
-            raise AttributeError("Must have called create_splits on {}".format(self.datamanager.__class__.__name__))
         self.num_folds: int = len(self.splits)
         self.Y_targets: List[Optional[np.ndarray]] = [None] * self.num_folds
         self.Y_train_targets: np.ndarray = np.ones(self.y_train.shape) * np.NaN

autoPyTorch/optimizer/smbo.py

@@ -18,7 +18,6 @@
 from smac.utils.io.traj_logging import TrajEntry
 
 from autoPyTorch.automl_common.common.utils.backend import Backend
-from autoPyTorch.datasets.base_dataset import BaseDataset
 from autoPyTorch.datasets.resampling_strategy import (
     CrossValTypes,
     DEFAULT_RESAMPLING_PARAMETERS,
@@ -194,9 +193,8 @@ def __init__(self,
         super(AutoMLSMBO, self).__init__()
         # data related
         self.dataset_name = dataset_name
-        self.datamanager: Optional[BaseDataset] = None
         self.metric = metric
-        self.task: Optional[str] = None
+
         self.backend = backend
         self.all_supported_metrics = all_supported_metrics
 
@@ -252,21 +250,11 @@ def __init__(self,
             self.initial_configurations = initial_configurations \
                 if len(initial_configurations) > 0 else None
 
-    def reset_data_manager(self) -> None:
-        if self.datamanager is not None:
-            del self.datamanager
-        self.datamanager = self.backend.load_datamanager()
-
-        if self.datamanager is not None and self.datamanager.task_type is not None:
-            self.task = self.datamanager.task_type
-
     def run_smbo(self, func: Optional[Callable] = None
                  ) -> Tuple[RunHistory, List[TrajEntry], str]:
 
         self.watcher.start_task('SMBO')
         self.logger.info("Started run of SMBO")
-        # == first things first: load the datamanager
-        self.reset_data_manager()
 
         # == Initialize non-SMBO stuff
         # first create a scenario