Merge pull request #1653 from VesnaT/confusion_matrix_output

[FIX] OWConfusionMatrix: Output None when no data is selected

Orange/widgets/evaluate/owconfusionmatrix.py

@@ -324,44 +324,46 @@ def commit(self):
             predicted = self.results.predicted[self.selected_learner[0]]
             selected = [i for i, t in enumerate(zip(actual, predicted))
                         if t in indices]
-            row_indices = self.results.row_indices[selected]
-
-            extra = []
-            class_var = self.data.domain.class_var
-            metas = self.data.domain.metas
-
-            if self.append_predictions:
-                predicted = numpy.array(predicted[selected], dtype=object)
-                extra.append(predicted.reshape(-1, 1))
-                var = Orange.data.DiscreteVariable(
-                    "{}({})".format(class_var.name, learner_name),
-                    class_var.values
+            if selected:
+                row_indices = self.results.row_indices[selected]
+                extra = []
+                class_var = self.data.domain.class_var
+                metas = self.data.domain.metas
+
+                if self.append_predictions:
+                    predicted = numpy.array(predicted[selected], dtype=object)
+                    extra.append(predicted.reshape(-1, 1))
+                    var = Orange.data.DiscreteVariable(
+                        "{}({})".format(class_var.name, learner_name),
+                        class_var.values
+                    )
+                    metas = metas + (var,)
+
+                if self.append_probabilities and \
+                        self.results.probabilities is not None:
+                    probs = self.results.probabilities[self.selected_learner[0],
+                                                       selected]
+                    extra.append(numpy.array(probs, dtype=object))
+                    pvars = [Orange.data.ContinuousVariable("p({})".format(value))
+                             for value in class_var.values]
+                    metas = metas + tuple(pvars)
+
+                X = self.data.X[row_indices]
+                Y = self.data.Y[row_indices]
+                M = self.data.metas[row_indices]
+                row_ids = self.data.ids[row_indices]
+
+                M = numpy.hstack((M,) + tuple(extra))
+                domain = Orange.data.Domain(
+                    self.data.domain.attributes,
+                    self.data.domain.class_vars,
+                    metas
                 )
-                metas = metas + (var,)
-
-            if self.append_probabilities and \
-                    self.results.probabilities is not None:
-                probs = self.results.probabilities[self.selected_learner[0],
-                                                   selected]
-                extra.append(numpy.array(probs, dtype=object))
-                pvars = [Orange.data.ContinuousVariable("p({})".format(value))
-                         for value in class_var.values]
-                metas = metas + tuple(pvars)
-
-            X = self.data.X[row_indices]
-            Y = self.data.Y[row_indices]
-            M = self.data.metas[row_indices]
-            row_ids = self.data.ids[row_indices]
-
-            M = numpy.hstack((M,) + tuple(extra))
-            domain = Orange.data.Domain(
-                self.data.domain.attributes,
-                self.data.domain.class_vars,
-                metas
-            )
-            data = Orange.data.Table.from_numpy(domain, X, Y, M)
-            data.ids = row_ids
-            data.name = learner_name
+                data = Orange.data.Table.from_numpy(domain, X, Y, M)
+                data.ids = row_ids
+                data.name = learner_name
+            else:
+                data = None
 
         else:
             data = None

Orange/widgets/evaluate/tests/test_owconfusionmatrix.py

@@ -6,7 +6,8 @@
 from Orange.widgets.evaluate.owconfusionmatrix import OWConfusionMatrix
 from Orange.widgets.tests.base import WidgetTest
 
-class TestOWClassificationTree(WidgetTest):
+
+class TestOWConfusionMatrix(WidgetTest):
     @classmethod
     def setUpClass(cls):
         super().setUpClass()
@@ -28,14 +29,30 @@ def test_selected_learner(self):
         """Check learner and model for various values of all parameters
         when pruning parameters are not checked
         """
-        self.widget.set_results(self.results_2_iris)
+        self.send_signal("Evaluation Results", self.results_2_iris)
         self.assertEqual(self.widget.selected_learner, [0])
         self.widget.selected_learner[:] = [1]
-        self.widget.set_results(self.results_2_titanic)
+        self.send_signal("Evaluation Results", self.results_2_titanic)
         self.widget.selected_learner[:] = [1]
-        self.widget.set_results(self.results_1_iris)
+        self.send_signal("Evaluation Results", self.results_1_iris)
         self.widget.selected_learner[:] = [0]
-        self.widget.set_results(None)
-        self.widget.set_results(self.results_1_iris)
+        self.send_signal("Evaluation Results", None)
+        self.send_signal("Evaluation Results", self.results_1_iris)
         self.widget.selected_learner[:] = [0]
 
+    def test_outputs(self):
+        self.send_signal("Evaluation Results", self.results_1_iris)
+
+        # check selected data output
+        self.assertIsNone(self.get_output("Selected Data"))
+
+        # select data instances
+        self.widget.select_correct()
+
+        # check selected data output
+        selected = self.get_output("Selected Data")
+        self.assertGreater(len(selected), 0)
+
+        # check output when data is removed
+        self.send_signal("Evaluation Results", None)
+        self.assertIsNone(self.get_output("Selected Data"))