MAINT update function name

Author: mfeurer

autosklearn/automl.py

@@ -494,7 +494,7 @@ def send_warnings_to_log(message, category, filename, lineno,
 
         random_state = np.random.RandomState(self._seed)
         for identifier in self.models_:
-            if identifier in self.ensemble_.get_model_identifiers():
+            if identifier in self.ensemble_.get_selected_model_identifiers():
                 model = self.models_[identifier]
                 # this updates the model inplace, it can then later be used in
                 # predict method
@@ -554,7 +554,7 @@ def predict(self, X, batch_size=None, n_jobs=1):
         # Each process computes predictions in chunks of batch_size rows.
         all_predictions = joblib.Parallel(n_jobs=n_jobs)(
             joblib.delayed(_model_predict)(self, X, batch_size, identifier)
-            for identifier in self.ensemble_.get_model_selected_identifiers())
+            for identifier in self.ensemble_.get_selected_model_identifiers())
 
         if len(all_predictions) == 0:
             raise ValueError('Something went wrong generating the predictions. '

autosklearn/ensembles/abstract_ensemble.py

@@ -61,7 +61,7 @@ def get_models_with_weights(self, models):
 
 
     @abstractmethod
-    def get_model_identifiers(self):
+    def get_selected_model_identifiers(self):
         """Return identifiers of models in the ensemble.
 
         This includes models which have a weight of zero!

autosklearn/ensembles/ensemble_selection.py

@@ -222,10 +222,7 @@ def get_models_with_weights(self, models):
 
         return output
 
-    def get_model_identifiers(self):
-        return self.identifiers_
-
-    def get_model_selected_identifiers(self):
+    def get_selected_model_identifiers(self):
         output = []
 
         for i, weight in enumerate(self.weights_):