Addressed comments, better documentation and dict for runhistory

Author: ravinkohli

autoPyTorch/api/base_task.py

@@ -173,7 +173,7 @@ def __init__(
         self._dataset_requirements: Optional[List[FitRequirement]] = None
         self._metric: Optional[autoPyTorchMetric] = None
         self._logger: Optional[PicklableClientLogger] = None
-        self.run_history: Optional[RunHistory] = None
+        self.run_history: Dict = {}
         self.trajectory: Optional[List] = None
         self.dataset_name: Optional[str] = None
         self.cv_models_: Dict = {}
@@ -688,6 +688,10 @@ def _do_traditional_prediction(self, num_run: int, time_left: int, func_eval_tim
                                      "Please consider increasing the run time to further improve performance.")
                 break
 
+        self._logger.debug("Run history traditional: {}".format(run_history))
+        # add run history of traditional to api run history
+        self.run_history.update(run_history.data)
+        run_history.save_json(os.path.join(self._backend.internals_directory, 'traditional_run_history.json'))
         return num_run
 
     def _search(
@@ -958,8 +962,9 @@ def _search(
                 search_space_updates=self.search_space_updates
             )
             try:
-                self.run_history, self.trajectory, budget_type = \
+                run_history, self.trajectory, budget_type = \
                     _proc_smac.run_smbo()
+                self.run_history.update(run_history.data)
                 trajectory_filename = os.path.join(
                     self._backend.get_smac_output_directory_for_run(self.seed),
                     'trajectory.json')

autoPyTorch/evaluation/abstract_evaluator.py

@@ -86,6 +86,15 @@ def estimator_supports_iterative_fit(self) -> bool:  # pylint: disable=R0201
         return False
 
     def get_additional_run_info(self) -> Dict[str, Any]:  # pylint: disable=R0201
+        """
+        Can be used to return additional info for the run.
+        Returns:
+            Dict[str, Any]:
+            Currently contains
+                1. pipeline_configuration: the configuration of the pipeline, i.e, the traditional model used
+                2. trainer_configuration: the parameters for the traditional model used.
+                    Can be found in autoPyTorch/pipeline/components/setup/traditional_ml/classifier_configs
+        """
         return {'pipeline_configuration': self.configuration,
                 'trainer_configuration': self.pipeline.named_steps['model_trainer'].choice.model.get_config()}