[Fix] long running regression (#272)

* Fix long running regression

* Fix metric append

* Remove print

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

Co-authored-by: Ravin Kohli <13005107+ravinkohli@users.noreply.github.com>

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

@@ -249,15 +249,15 @@ def _fit(self, X: Dict[str, Any], y: Any = None, **kwargs: Any) -> 'TrainerChoic
         )
 
         # Support additional user metrics
-        additional_metrics = X['additional_metrics'] if 'additional_metrics' in X else None
-        if 'optimize_metric' in X:
-            additional_metrics = additional_metrics.append(X['optimize_metric']) if additional_metrics is not None \
-                else [X['optimize_metric']]
+        metrics = get_metrics(dataset_properties=X['dataset_properties'])
+        if 'additional_metrics' in X:
+            metrics.extend(get_metrics(dataset_properties=X['dataset_properties'], names=X['additional_metrics']))
+        if 'optimize_metric' in X and X['optimize_metric'] not in [m.name for m in metrics]:
+            metrics.extend(get_metrics(dataset_properties=X['dataset_properties'], names=[X['optimize_metric']]))
         additional_losses = X['additional_losses'] if 'additional_losses' in X else None
         self.choice.prepare(
             model=X['network'],
-            metrics=get_metrics(dataset_properties=X['dataset_properties'],
-                                names=additional_metrics),
+            metrics=metrics,
             criterion=get_loss(X['dataset_properties'],
                                name=additional_losses),
             budget_tracker=budget_tracker,

cicd/test_preselected_configs.py

@@ -153,7 +153,7 @@ def test_can_properly_fit_a_config(openml_task_id, configuration, scorer, lower_
     train_data, target_data = fit_dictionary['backend'].load_datamanager().train_tensors
     predictions = pipeline.predict(train_data[val_indices])
     score = scorer(fit_dictionary['y_train'][val_indices], predictions)
-    assert pytest.approx(score) >= lower_bound_score
+    assert score >= lower_bound_score
 
     # Check that we reverted to the best score
     run_summary = pipeline.named_steps['trainer'].run_summary