Add tests for format_prediction

openml · mfeurer · Sep 2, 2020 · Jul 7, 2020 · Jul 7, 2020 · Jul 10, 2020
commit 41aa789363100d0d911da07312dc81de64bfb5ce
diff --git a/tests/test_runs/test_run_functions.py b/tests/test_runs/test_run_functions.py
@@ -22,10 +22,7 @@
 
 import openml.extensions.sklearn
 from openml.testing import TestBase, SimpleImputer
-from openml.runs.functions import (
-    _run_task_get_arffcontent,
-    run_exists,
-)
+from openml.runs.functions import _run_task_get_arffcontent, run_exists, format_prediction
 from openml.runs.trace import OpenMLRunTrace
 from openml.tasks import TaskTypeEnum
 
@@ -1336,3 +1333,44 @@ def test_run_flow_on_task_downloaded_flow(self):
         run.publish()
         TestBase._mark_entity_for_removal("run", run.run_id)
         TestBase.logger.info("collected from {}: {}".format(__file__.split("/")[-1], run.run_id))
+
+    def test_format_prediction_non_supervised(self):
+        # non-supervised tasks don't exist on the test server
+        openml.config.server = self.production_server
+        clustering = openml.tasks.get_task(126033, download_data=False)
+        ignored_input = [0] * 5
+        with self.assertRaises(TypeError):
+            format_prediction(clustering, *ignored_input)
+
+    def test_format_prediction_classification_no_probabilities(self):
+        classification = openml.tasks.get_task(self.TEST_SERVER_TASK_SIMPLE[0], download_data=False)
+        ignored_input = [0] * 5
+        with self.assertRaises(ValueError):
+            format_prediction(classification, *ignored_input, proba=None)
+
+    def test_format_prediction_classification_incomplete_probabilities(self):
+        classification = openml.tasks.get_task(self.TEST_SERVER_TASK_SIMPLE[0], download_data=False)
+        ignored_input = [0] * 5
+        incomplete_probabilities = {c: 0.2 for c in classification.class_labels[1:]}
+        with self.assertRaises(ValueError):
+            format_prediction(classification, *ignored_input, proba=incomplete_probabilities)
+
+    def test_format_prediction_task_without_classlabels_set(self):
+        classification = openml.tasks.get_task(self.TEST_SERVER_TASK_SIMPLE[0], download_data=False)
+        classification.class_labels = None
+        ignored_input = [0] * 5
+        with self.assertRaises(ValueError):
+            format_prediction(classification, *ignored_input, proba={})
+
+    def test_format_prediction_task_learning_curve_sample_not_set(self):
+        learning_curve = openml.tasks.get_task(801, download_data=False)
+        probabilities = {c: 0.2 for c in learning_curve.class_labels}
+        ignored_input = [0] * 5
+        with self.assertRaises(ValueError):
+            format_prediction(learning_curve, *ignored_input, sample=None, proba=probabilities)
+
+    def test_format_prediction_task_regression(self):
+        regression = openml.tasks.get_task(self.TEST_SERVER_TASK_REGRESSION[0], download_data=False)
+        ignored_input = [0] * 5
+        res = format_prediction(regression, *ignored_input)
+        self.assertListEqual(res, [0] * 5)