do not propagate error messages to openml server

Author: mfeurer

openml/runs/functions.py

@@ -53,12 +53,7 @@ def run_task(task, model):
 
     # execute the run
     run = OpenMLRun(task_id=task.task_id, flow_id=None, dataset_id=dataset.dataset_id, model=model)
-
-    try:
-        run.data_content, run.trace_content = _run_task_get_arffcontent(model, task, class_labels)
-    except PyOpenMLError as message:
-        run.error_message = str(message)
-        warnings.warn("Run terminated with error: %s" %run.error_message)
+    run.data_content, run.trace_content = _run_task_get_arffcontent(model, task, class_labels)
 
     # now generate the flow
     flow = sklearn_to_flow(model)
@@ -131,17 +126,13 @@ def _run_task_get_arffcontent(model, task, class_labels):
             testX = X[test_indices]
             testY = Y[test_indices]
 
-            try:
-                model.fit(trainX, trainY)
-
-                if isinstance(model, BaseSearchCV):
-                    _add_results_to_arfftrace(arff_tracecontent, fold_no, model, rep_no)
-                    model_classes = model.best_estimator_.classes_
-                else:
-                    model_classes = model.classes_
-            except AttributeError as e:
-                # typically happens when training a regressor on classification task
-                raise PyOpenMLError(str(e))
+            model.fit(trainX, trainY)
+
+            if isinstance(model, BaseSearchCV):
+                _add_results_to_arfftrace(arff_tracecontent, fold_no, model, rep_no)
+                model_classes = model.best_estimator_.classes_
+            else:
+                model_classes = model.classes_
 
             ProbaY = model.predict_proba(testX)
             PredY = model.predict(testX)

tests/test_runs/test_run_functions.py

@@ -136,7 +136,6 @@ def test__run_task_get_arffcontent(self):
         # 10 times 10 fold CV of 150 samples
         self.assertEqual(len(arff_datacontent), num_instances * num_repeats)
         for arff_line in arff_datacontent:
-            print(arff_line)
             # check number columns
             self.assertEqual(len(arff_line), 7)
             # check repeat
@@ -298,7 +297,6 @@ def test_run_on_dataset_with_missing_labels(self):
         # 2 folds, 5 repeats; keep in mind that this task comes from the test
         # server, the task on the live server is different
         self.assertEqual(len(data_content), 4490)
-        print(data_content[0])
         for row in data_content:
             # repeat, fold, row_id, 6 confidences, prediction and correct label
             self.assertEqual(len(row), 11)