scoring sheets, adapt test for randomness

Orange/classification/utils/fasterrisk/fasterrisk.py

@@ -80,6 +80,7 @@ def __init__(self, X, y, k, select_top_m=50, lb=-5, ub=5, \
         else:
             assert type(group_sparsity) == int, "group_sparsity needs to be an integer"
             assert group_sparsity > 0, "group_sparsity needs to be > 0!"
+            assert group_sparsity > 0, "group_sparsity needs to be > 0!"
 
             assert self.featureIndex_to_groupIndex is not None, "featureIndex_to_groupIndex must be provided if group_sparsity is not None"
             assert type(self.featureIndex_to_groupIndex[0]) == np.int_, "featureIndex_to_groupIndex needs to be a NumPy integer array"

Orange/widgets/model/tests/test_owscoringsheet.py

@@ -63,7 +63,10 @@ def test_settings_in_model(self):
         non_zero_coefficients = [coef for coef in coefficients if coef != 0]
 
         self.assertEqual(len(coefficients), self.widget.num_attr_after_selection)
-        self.assertEqual(len(non_zero_coefficients), self.widget.num_decision_params)
+
+        # most often equal, but in some cases the optimizer finds fewer parameters
+        self.assertLessEqual(len(non_zero_coefficients), self.widget.num_decision_params)
+
         self.assertLessEqual(
             max(non_zero_coefficients, key=lambda x: abs(x)),
             self.widget.max_points_per_param,