OWAdaBoost: Fix tests for learners that do not support sample weights

Author: pavlin-policar

Orange/widgets/classify/tests/test_owadaboostclassification.py

@@ -1,6 +1,8 @@
 # Test methods with long descriptive names can omit docstrings
 # pylint: disable=missing-docstring
-from Orange.classification import SklTreeLearner, KNNLearner
+from Orange.classification import KNNLearner
+from Orange.classification import RandomForestLearner
+from Orange.classification import SklTreeLearner
 from Orange.widgets.classify.owadaboost import OWAdaBoostClassification
 from Orange.widgets.tests.base import (WidgetTest, WidgetLearnerTestMixin,
                                        ParameterMapping)
@@ -30,10 +32,17 @@ def test_input_learner(self):
         output_base_est = self.get_output("Learner").params.get("base_estimator")
         self.assertEqual(output_base_est.max_depth, max_depth)
 
+    def test_input_learner_that_does_not_support_sample_weights(self):
+        self.send_signal("Learner", KNNLearner())
+        self.assertNotIsInstance(self.widget.base_estimator, KNNLearner)
+        self.assertEqual(
+            self.widget.base_estimator, self.widget.DEFAULT_BASE_ESTIMATOR)
+        self.assertTrue(self.widget.Error.no_weight_support.is_shown())
+
     def test_input_learner_disconnect(self):
         """Check base learner after disconnecting learner on the input"""
-        self.send_signal("Learner", KNNLearner())
-        self.assertIsInstance(self.widget.base_estimator, KNNLearner)
+        self.send_signal("Learner", RandomForestLearner())
+        self.assertIsInstance(self.widget.base_estimator, RandomForestLearner)
         self.send_signal("Learner", None)
         self.assertEqual(self.widget.base_estimator,
                          self.widget.DEFAULT_BASE_ESTIMATOR)

Orange/widgets/regression/tests/test_owadaboostregression.py

@@ -1,5 +1,6 @@
 # Test methods with long descriptive names can omit docstrings
 # pylint: disable=missing-docstring
+from Orange.regression import RandomForestRegressionLearner
 from Orange.regression import SklTreeRegressionLearner, KNNRegressionLearner
 from Orange.widgets.regression.owadaboostregression import OWAdaBoostRegression
 from Orange.widgets.tests.base import (WidgetTest, WidgetLearnerTestMixin,
@@ -31,10 +32,19 @@ def test_input_learner(self):
         output_base_est = self.get_output("Learner").params.get("base_estimator")
         self.assertEqual(output_base_est.max_depth, max_depth)
 
+    def test_input_learner_that_does_not_support_sample_weights(self):
+        self.send_signal("Learner", KNNRegressionLearner())
+        self.assertNotIsInstance(
+            self.widget.base_estimator, KNNRegressionLearner)
+        self.assertEqual(
+            self.widget.base_estimator, self.widget.DEFAULT_BASE_ESTIMATOR)
+        self.assertTrue(self.widget.Error.no_weight_support.is_shown())
+
     def test_input_learner_disconnect(self):
         """Check base learner after disconnecting learner on the input"""
-        self.send_signal("Learner", KNNRegressionLearner())
-        self.assertIsInstance(self.widget.base_estimator, KNNRegressionLearner)
+        self.send_signal("Learner", RandomForestRegressionLearner())
+        self.assertIsInstance(
+            self.widget.base_estimator, RandomForestRegressionLearner)
         self.send_signal("Learner", None)
         self.assertEqual(self.widget.base_estimator,
                          self.widget.DEFAULT_BASE_ESTIMATOR)