Add support for using run_model_on_task simply (#888)

* Add support for using run_model_on_task simply

* Add unit test

* fix mypy error

openml/runs/functions.py

@@ -25,7 +25,7 @@
     OpenMLRegressionTask, OpenMLSupervisedTask, OpenMLLearningCurveTask
 from .run import OpenMLRun
 from .trace import OpenMLRunTrace
-from ..tasks import TaskTypeEnum
+from ..tasks import TaskTypeEnum, get_task
 
 # Avoid import cycles: https://mypy.readthedocs.io/en/latest/common_issues.html#import-cycles
 if TYPE_CHECKING:
@@ -38,7 +38,7 @@
 
 def run_model_on_task(
     model: Any,
-    task: OpenMLTask,
+    task: Union[int, str, OpenMLTask],
     avoid_duplicate_runs: bool = True,
     flow_tags: List[str] = None,
     seed: int = None,
@@ -54,8 +54,9 @@ def run_model_on_task(
         A model which has a function fit(X,Y) and predict(X),
         all supervised estimators of scikit learn follow this definition of a model [1]
         [1](http://scikit-learn.org/stable/tutorial/statistical_inference/supervised_learning.html)
-    task : OpenMLTask
-        Task to perform. This may be a model instead if the first argument is an OpenMLTask.
+    task : OpenMLTask or int or str
+        Task to perform or Task id.
+        This may be a model instead if the first argument is an OpenMLTask.
     avoid_duplicate_runs : bool, optional (default=True)
         If True, the run will throw an error if the setup/task combination is already present on
         the server. This feature requires an internet connection.
@@ -84,7 +85,7 @@ def run_model_on_task(
     # Flexibility currently still allowed due to code-snippet in OpenML100 paper (3-2019).
     # When removing this please also remove the method `is_estimator` from the extension
     # interface as it is only used here (MF, 3-2019)
-    if isinstance(model, OpenMLTask):
+    if isinstance(model, (int, str, OpenMLTask)):
         warnings.warn("The old argument order (task, model) is deprecated and "
                       "will not be supported in the future. Please use the "
                       "order (model, task).", DeprecationWarning)
@@ -98,6 +99,14 @@ def run_model_on_task(
 
     flow = extension.model_to_flow(model)
 
+    def get_task_and_type_conversion(task: Union[int, str, OpenMLTask]) -> OpenMLTask:
+        if isinstance(task, (int, str)):
+            return get_task(int(task))
+        else:
+            return task
+
+    task = get_task_and_type_conversion(task)
+
     run = run_flow_on_task(
         task=task,
         flow=flow,

tests/test_runs/test_run_functions.py

@@ -110,9 +110,9 @@ def _compare_predictions(self, predictions, predictions_prime):
 
         return True
 
-    def _rerun_model_and_compare_predictions(self, run_id, model_prime, seed):
+    def _rerun_model_and_compare_predictions(self, run_id, model_prime, seed,
+                                             create_task_obj):
         run = openml.runs.get_run(run_id)
-        task = openml.tasks.get_task(run.task_id)
 
         # TODO: assert holdout task
 
@@ -121,12 +121,24 @@ def _rerun_model_and_compare_predictions(self, run_id, model_prime, seed):
         predictions_url = openml._api_calls._file_id_to_url(file_id)
         response = openml._api_calls._download_text_file(predictions_url)
         predictions = arff.loads(response)
-        run_prime = openml.runs.run_model_on_task(
-            model=model_prime,
-            task=task,
-            avoid_duplicate_runs=False,
-            seed=seed,
-        )
+
+        # if create_task_obj=False, task argument in run_model_on_task is specified task_id
+        if create_task_obj:
+            task = openml.tasks.get_task(run.task_id)
+            run_prime = openml.runs.run_model_on_task(
+                model=model_prime,
+                task=task,
+                avoid_duplicate_runs=False,
+                seed=seed,
+            )
+        else:
+            run_prime = openml.runs.run_model_on_task(
+                model=model_prime,
+                task=run.task_id,
+                avoid_duplicate_runs=False,
+                seed=seed,
+            )
+
         predictions_prime = run_prime._generate_arff_dict()
 
         self._compare_predictions(predictions, predictions_prime)
@@ -425,13 +437,17 @@ def determine_grid_size(param_grid):
                 raise e
 
             self._rerun_model_and_compare_predictions(run.run_id, model_prime,
-                                                      seed)
+                                                      seed, create_task_obj=True)
+            self._rerun_model_and_compare_predictions(run.run_id, model_prime,
+                                                      seed, create_task_obj=False)
         else:
             run_downloaded = openml.runs.get_run(run.run_id)
             sid = run_downloaded.setup_id
             model_prime = openml.setups.initialize_model(sid)
-            self._rerun_model_and_compare_predictions(run.run_id,
-                                                      model_prime, seed)
+            self._rerun_model_and_compare_predictions(run.run_id, model_prime,
+                                                      seed, create_task_obj=True)
+            self._rerun_model_and_compare_predictions(run.run_id, model_prime,
+                                                      seed, create_task_obj=False)
 
         # todo: check if runtime is present
         self._check_fold_timing_evaluations(run.fold_evaluations, 1, num_folds,