[FIX] score.FCBF: don't segfault on continuous variables w/ <0 values

Orange/preprocess/score.py

@@ -230,13 +230,11 @@ def _gini(D):
                   np.sum(D, axis=0) / np.sum(D))
 
 
-def _symmetrical_uncertainty(X, Y):
+def _symmetrical_uncertainty(data, attr1, attr2):
     """Symmetrical uncertainty, Press et al., 1988."""
-    from Orange.preprocess._relieff import contingency_table
-    X, Y = np.around(X), np.around(Y)
-    cont = contingency_table(X, Y)
+    cont = np.asarray(contingency.Discrete(data, attr1, attr2), dtype=float)
     ig = InfoGain().from_contingency(cont, 1)
-    return 2 * ig / (_entropy(cont.sum(0)) + _entropy(cont.sum(1)))
+    return 2 * ig / (_entropy(cont) + _entropy(cont.T))
 
 
 class FCBF(ClassificationScorer):
@@ -248,9 +246,10 @@ class FCBF(ClassificationScorer):
     2003. http://www.aaai.org/Papers/ICML/2003/ICML03-111.pdf
     """
     def score_data(self, data, feature=None):
+        attributes = data.domain.attributes
         S = []
-        for i, a in enumerate(data.X.T):
-            S.append((_symmetrical_uncertainty(a, data.Y), i))
+        for i, attr in enumerate(attributes):
+            S.append((_symmetrical_uncertainty(data, attr, data.domain.class_var), i))
         S.sort()
         worst = []
 
@@ -262,9 +261,7 @@ def score_data(self, data, feature=None):
             while True:
                 try: SUqc, Fq = S[-q]
                 except IndexError: break
-                # TODO: cache
-                if _symmetrical_uncertainty(data.X.T[Fp],
-                                            data.X.T[Fq]) >= SUqc:
+                if _symmetrical_uncertainty(data, attributes[Fp], attributes[Fq]) >= SUqc:
                     del S[-q]
                     worst.append((1e-4*SUqc, Fq))
                 else:

Orange/tests/test_score_feature.py

@@ -5,7 +5,7 @@
 
 import numpy as np
 
-from Orange.data import Table, Domain, DiscreteVariable
+from Orange.data import Table, Domain, DiscreteVariable, ContinuousVariable
 from Orange import preprocess
 from Orange.preprocess.score import InfoGain, GainRatio, Gini, Chi2, ANOVA,\
     UnivariateLinearRegression, ReliefF, FCBF, RReliefF
@@ -129,5 +129,13 @@ def test_fcbf(self):
         scorer = FCBF()
         weights = scorer(self.zoo, None)
         found = [self.zoo.domain[attr].name for attr in reversed(weights.argsort()[-5:])]
-        reference = ['legs', 'backbone', 'toothed', 'hair', 'aquatic']
+        reference = ['legs', 'milk', 'toothed', 'feathers', 'backbone']
         self.assertEqual(found, reference)
+
+        # GH-1916
+        data = Table(Domain([ContinuousVariable('1'), ContinuousVariable('2')],
+                            DiscreteVariable('target')),
+                     np.full((2, 2), np.nan),
+                     np.r_[0., 1])
+        weights = scorer(data, None)
+        np.testing.assert_equal(weights, np.nan)