Merge pull request #2394 from jerneju/index-rocanalysis

[FIX] ROC Analysis: color support for more than 9 evaluation learners
biolab · Jun 23, 2017 · 6a5f2f6 · 6a5f2f6
2 parents 7636e49 + 06de5b5
commit 6a5f2f6
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Showing 2 changed files with 26 additions and 3 deletions.
diff --git a/Orange/widgets/evaluate/owrocanalysis.py b/Orange/widgets/evaluate/owrocanalysis.py
@@ -448,8 +448,10 @@ def _initialize(self, results):
             names = ["#{}".format(i + 1)
                      for i in range(len(results.predicted))]
 
-        self.colors = colorpalette.ColorPaletteGenerator(
-            len(names), colorbrewer.colorSchemes["qualitative"]["Dark2"])
+        scheme = colorbrewer.colorSchemes["qualitative"]["Dark2"]
+        if len(names) > len(scheme):
+            scheme = colorpalette.DefaultRGBColors
+        self.colors = colorpalette.ColorPaletteGenerator(len(names), scheme)
 
         self.classifier_names = names
         self.selected_classifiers = list(range(len(names)))

diff --git a/Orange/widgets/evaluate/tests/test_owrocanalysis.py b/Orange/widgets/evaluate/tests/test_owrocanalysis.py
@@ -152,4 +152,25 @@ def test_nan_input(self):
         self.send_signal("Evaluation Results", res)
         self.assertTrue(self.widget.Error.invalid_results.is_shown())
         self.send_signal("Evaluation Results", None)
-        self.assertFalse(self.widget.Error.invalid_results.is_shown())
+        self.assertFalse(self.widget.Error.invalid_results.is_shown())
+
+    def test_many_evaluation_results(self):
+        """
+        Now works with more than 9 evaluation results.
+        GH-2394
+        """
+        data = Orange.data.Table("iris")
+        learners = [
+            Orange.classification.MajorityLearner(),
+            Orange.classification.LogisticRegressionLearner(),
+            Orange.classification.TreeLearner(),
+            Orange.classification.SVMLearner(),
+            Orange.classification.KNNLearner(),
+            Orange.classification.CN2Learner(),
+            Orange.classification.SGDClassificationLearner(),
+            Orange.classification.RandomForestLearner(),
+            Orange.classification.NaiveBayesLearner(),
+            Orange.classification.SGDClassificationLearner()
+        ]
+        res = Orange.evaluation.CrossValidation(data, learners, k=2, store_data=True)
+        self.send_signal("Evaluation Results", res)