Adding tests for voting ensemble

sknn/tests/test_classifier.py

@@ -45,10 +45,13 @@ def test_PredictUninitializedNoLabels(self):
     def test_PredictBinaryProbability(self):
         a_in = numpy.random.uniform(-1.0, 1.0, size=(8,16))
         a_out = numpy.array((a_in.sum(axis=1) >= 0.0), dtype=numpy.int32)
+        a_out[0], a_out[-1] = 0, 1
         self.nn.fit(a_in, a_out)
 
         a_proba = self.nn.predict_proba(a_in)
+        a_test = self.nn.predict(a_in)
         c_out = numpy.unique(a_out)
+
         assert_equal(2, c_out.shape[0])
         assert_equal(2, a_proba.shape[1])
 

sknn/tests/test_sklearn.py

@@ -6,7 +6,8 @@
 
 from sklearn.grid_search import GridSearchCV, RandomizedSearchCV
 from sklearn.cross_validation import cross_val_score
-
+from sklearn.ensemble import VotingClassifier
+
 from sknn.mlp import Regressor as MLPR, Classifier as MLPC
 from sknn.mlp import Layer as L
 
@@ -77,3 +78,12 @@ def test_Classifier(self):
         a_out = numpy.random.randint(0, 4, (64,))
 
         cross_val_score(MLPC(layers=[L("Softmax")], n_iter=1), a_in, a_out, cv=5)
+
+
+class TestVotingEnsemble(unittest.TestCase):
+
+    def test_SingleVote(self):
+        a_in, a_out = numpy.random.uniform(0.0, 1.0, (64,16)), numpy.zeros((64,))
+        vc = VotingClassifier([('nn1', MLPC(layers=[L("Softmax")], n_iter=1))])
+        vc.fit(a_in, a_out)
+        vc.predict(a_in)