add multi_target_input tests

Author: JakaKokosar

Orange/widgets/evaluate/tests/test_owpredictions.py

@@ -877,6 +877,27 @@ def test_change_target(self):
             self.assertEqual(table.model.columnCount(), 4)
             self.assertEqual(float(table.model.data(table.model.index(0, 3))),
                              idx)
+            
+    def test_multi_target_input(self):
+        widget = self.widget
+
+        domain = Domain([ContinuousVariable('var1')],
+                        class_vars=[
+                            ContinuousVariable('c1'),
+                            DiscreteVariable('c2', values=('no', 'yes'))
+                        ])
+        data = Table.from_list(domain, [[1, 5, 0], [2, 10, 1]])
+
+        mock_model = Mock(spec=Model, return_value=np.asarray([0.2, 0.1]))
+        mock_model.name = 'Mockery'
+        mock_model.domain = domain
+        mock_learner = Mock(return_value=mock_model)
+        model = mock_learner(data)
+
+        self.send_signal(widget.Inputs.data, data)
+        self.send_signal(widget.Inputs.predictors, model, 1)
+        pred = self.get_output(widget.Outputs.predictions)
+        self.assertIsInstance(pred, Table)
 
     def test_report(self):
         widget = self.widget

Orange/widgets/evaluate/tests/test_owtestandscore.py

@@ -16,7 +16,7 @@
 from Orange.evaluation import Results, TestOnTestData, scoring
 from Orange.evaluation.scoring import ClassificationScore, RegressionScore, \
     Score
-from Orange.base import Learner
+from Orange.base import Learner, Model
 from Orange.modelling import ConstantLearner
 from Orange.regression import MeanLearner
 from Orange.widgets.evaluate.owtestandscore import (
@@ -724,6 +724,42 @@ def test_copy_to_clipboard(self):
                                for i in (0, 3, 4, 5, 6, 7)]) + "\r\n"
         self.assertEqual(clipboard_text, view_text)
 
+    def test_multi_target_input(self):
+        class NewScorer(Score):
+            class_types = (
+                ContinuousVariable,
+                DiscreteVariable,
+            )
+
+            @staticmethod
+            def is_adequate(domain: Domain) -> bool:
+                return True
+
+            def compute_score(self, results):
+                return [0.75]
+
+        domain = Domain([ContinuousVariable('var1')],
+                        class_vars=[
+                            ContinuousVariable('c1'),
+                            DiscreteVariable('c2', values=('no', 'yes'))
+                        ])
+        data = Table.from_list(domain, [[1, 5, 0], [2, 10, 1], [2, 10, 1]])
+
+        mock_model = Mock(spec=Model, return_value=np.asarray([[0.2, 0.1, 0.2]]))
+        mock_model.name = 'Mockery'
+        mock_model.domain = domain
+        mock_learner = Mock(spec=Learner, return_value=mock_model)
+        mock_learner.name = 'Mockery'
+
+        self.widget.resampling = OWTestAndScore.TestOnTrain
+        self.send_signal(self.widget.Inputs.train_data, data)
+        self.send_signal(self.widget.Inputs.learner, MajorityLearner(), 0)
+        self.send_signal(self.widget.Inputs.learner, mock_learner, 1)
+        _ = self.get_output(self.widget.Outputs.evaluations_results, wait=5000)
+        self.assertTrue(len(self.widget.scorers) == 1)
+        self.assertTrue(NewScorer in self.widget.scorers)
+        self.assertTrue(len(self.widget._successful_slots()) == 1)
+
 
 class TestHelpers(unittest.TestCase):
     def test_results_one_vs_rest(self):