small fix when calling get_hyperparameter_search_space()

Author: urbanmatthias

autoPyTorch/core/api.py

@@ -70,28 +70,31 @@ def get_current_autonet_config(self):
         return self.pipeline.get_pipeline_config(**self.base_config)
 
     def get_hyperparameter_search_space(self, X_train=None, Y_train=None, X_valid=None, Y_valid=None, **autonet_config):
-        """Return hyperparameter search space of Auto-PyTorch. Does depend on the dataset!
+        """Return hyperparameter search space of Auto-PyTorch. Does depend on the dataset and the configuration.!
         
         Keyword Arguments:
             X_train {array} -- Training data.
             Y_train {array} -- Targets of training data.
             X_valid {array} -- Validation data. Will be ignored if cv_splits > 1. (default: {None})
             Y_valid {array} -- Validation data. Will be ignored if cv_splits > 1. (default: {None})
+            autonet_config{dict} -- if not given and fit already called, config of last fit will be used
         
         Returns:
             ConfigurationSpace -- The configuration space that should be optimized.
         """
 
         dataset_info = None
+        pipeline_config = dict(self.base_config, **autonet_config) if autonet_config else \
+            self.get_current_autonet_config()
         if X_train is not None and Y_train is not None:
             dataset_info_node = self.pipeline[CreateDatasetInfo.get_name()]
-            dataset_info = dataset_info_node.fit(pipeline_config=dict(self.base_config, **autonet_config),
+            dataset_info = dataset_info_node.fit(pipeline_config=pipeline_config,
                                                  X_train=X_train,
                                                  Y_train=Y_train,
                                                  X_valid=X_valid,
                                                  Y_valid=Y_valid)["dataset_info"]
 
-        return self.pipeline.get_hyperparameter_search_space(dataset_info=dataset_info, **self.get_current_autonet_config())
+        return self.pipeline.get_hyperparameter_search_space(dataset_info=dataset_info, **pipeline_config)
 
     @classmethod
     def get_default_pipeline(cls):