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[FIX] ROC Analysis: color support for more than 9 evaluation learners #2394

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Jun 23, 2017
Merged

[FIX] ROC Analysis: color support for more than 9 evaluation learners #2394

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cb6e179
[FIX] ROC Analysis: color support for more than 9 evaluation learners
jerneju Jun 9, 2017
06de5b5
ROC Analysis: Reenable the original palette when possible
janezd Jun 16, 2017
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6 changes: 4 additions & 2 deletions Orange/widgets/evaluate/owrocanalysis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)))
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23 changes: 22 additions & 1 deletion Orange/widgets/evaluate/tests/test_owrocanalysis.py
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Expand Up @@ -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)