Venn Diagram: Silence warning about nans

Author: janezd

Orange/tests/test_classification.py

@@ -7,7 +7,11 @@
 import unittest
 
 import traceback
+import warnings
+
 import numpy as np
+from sklearn.exceptions import ConvergenceWarning
+
 from Orange.base import SklLearner
 
 import Orange.classification
@@ -269,6 +273,9 @@ class LearnerAccessibility(unittest.TestCase):
 
     def setUp(self):
         Variable._clear_all_caches()
+        # Convergence warnings are irrelevant for these tests
+        warnings.filterwarnings("ignore", ".*", ConvergenceWarning)
+
 
     def all_learners(self):
         classification_modules = pkgutil.walk_packages(

Orange/tests/test_neural_network.py

@@ -2,6 +2,9 @@
 # pylint: disable=missing-docstring
 
 import unittest
+import warnings
+
+from sklearn.exceptions import ConvergenceWarning
 
 from Orange.data import Table
 from Orange.classification import NNClassificationLearner
@@ -17,6 +20,11 @@ def setUpClass(cls):
         cls.housing = Table('housing')
         cls.learner = NNLearner()
 
+    def setUp(self):
+        # Convergence warnings are irrelevant for these tests
+        warnings.filterwarnings("ignore", ".*", ConvergenceWarning)
+        super().setUp()
+
     def test_NN_classification(self):
         results = CrossValidation(self.iris, [NNClassificationLearner()], k=3)
         ca = CA(results)

Orange/tests/test_sgd.py

@@ -2,8 +2,10 @@
 # pylint: disable=missing-docstring
 
 import unittest
+import warnings
 
 import numpy as np
+from sklearn.exceptions import ConvergenceWarning
 
 from Orange.data import Table
 from Orange.classification import SGDClassificationLearner
@@ -12,6 +14,11 @@
 
 
 class TestSGDRegressionLearner(unittest.TestCase):
+    def setUp(self):
+        # Convergence warnings are irrelevant for these tests
+        warnings.filterwarnings("ignore", ".*", ConvergenceWarning)
+        super().setUp()
+
     def test_SGDRegression(self):
         nrows, ncols = 500, 5
         X = np.random.rand(nrows, ncols)
@@ -32,6 +39,11 @@ class TestSGDClassificationLearner(unittest.TestCase):
     def setUpClass(cls):
         cls.iris = Table('iris')
 
+    def setUp(self):
+        # Convergence warnings are irrelevant for these tests
+        warnings.filterwarnings("ignore", ".*", ConvergenceWarning)
+        super().setUp()
+
     def test_SGDClassification(self):
         sgd = SGDClassificationLearner()
         res = CrossValidation(self.iris, [sgd], k=3)

Orange/tests/test_svm.py

@@ -2,8 +2,10 @@
 # pylint: disable=missing-docstring
 
 import unittest
+import warnings
 
 import numpy as np
+from sklearn.exceptions import ConvergenceWarning
 
 from Orange.classification import (SVMLearner, LinearSVMLearner,
                                    NuSVMLearner, OneClassSVMLearner)
@@ -24,6 +26,8 @@ def test_SVM(self):
         self.assertGreater(CA(res)[0], 0.9)
 
     def test_LinearSVM(self):
+        # This warning is irrelevant here
+        warnings.filterwarnings("ignore", ".*", ConvergenceWarning)
         learn = LinearSVMLearner()
         res = CrossValidation(self.data, [learn], k=2)
         self.assertGreater(CA(res)[0], 0.8)

Orange/widgets/visualize/owvenndiagram.py

@@ -907,7 +907,8 @@ def varying_between(table, idvar):
         subset = table[indices]
         for var in list(candidate_set):
             column, _ = subset.get_column_view(var)
-            values = util.unique(column)
+            with numpy.errstate(invalid="ignore"):  # nans are removed below
+                values = util.unique(column)
 
             if not var.is_string:
                 values = unique_non_nan(values)