Predictions: Add models and folds to output Results

Orange/widgets/evaluate/owpredictions.py

@@ -687,7 +687,8 @@ def _commit_evaluation_results(self):
         nanmask = numpy.isnan(self.data.get_column_view(self.class_var)[0])
         data = self.data[~nanmask]
         results = Results(data, store_data=True)
-        results.folds = None
+        results.folds = [...]
+        results.models = numpy.array([[p.predictor for p in self.predictors]])
         results.row_indices = numpy.arange(len(data))
         results.actual = data.Y.ravel()
         results.predicted = numpy.vstack(

Orange/widgets/evaluate/tests/test_owcalibrationplot.py

@@ -642,7 +642,7 @@ def test_warn_nan_probabilities(self, *_):
     @patch("Orange.widgets.evaluate.owcalibrationplot.ThresholdClassifier")
     @patch("Orange.widgets.evaluate.owcalibrationplot.CalibratedLearner")
     def test_no_folds(self, *_):
-        """Warn about omitted points with nan probabiities"""
+        """Don't crash on malformed Results with folds=None"""
         widget = self.widget
 
         self.results.folds = None

Orange/widgets/evaluate/tests/test_owpredictions.py

@@ -10,6 +10,7 @@
 
 from Orange.base import Model
 from Orange.classification import LogisticRegressionLearner, NaiveBayesLearner
+from Orange.classification.majority import ConstantModel
 from Orange.data.io import TabReader
 from Orange.evaluation.scoring import TargetScore
 from Orange.preprocess import Remove
@@ -507,6 +508,9 @@ def test_multi_inputs(self):
         def check_evres(expected):
             out = self.get_output(w.Outputs.evaluation_results)
             self.assertSequenceEqual(out.learner_names, expected)
+            self.assertEqual(out.folds, [...])
+            self.assertEqual(out.models.shape, (1, len(out.learner_names)))
+            self.assertIsInstance(out.models[0, 0], ConstantModel)
 
         check_evres(["P1", "P2", "P3"])