Matthews Correlation fix and test

keras/metrics.py

@@ -83,9 +83,9 @@ def matthews_correlation(y_true, y_pred):
     tp = K.sum(y_pos * y_pred_pos)
     tn = K.sum(y_neg * y_pred_neg)
 
-    fp = K.sum(1 - y_neg * y_pred_pos)
-    fn = K.sum(1 - y_pos * y_pred_neg)
-    
+    fp = K.sum(y_neg * y_pred_pos)
+    fn = K.sum(y_pos * y_pred_neg)
+
     numerator = (tp * tn - fp * fn)
     denominator = K.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
 

tests/keras/test_metrics.py

@@ -34,6 +34,18 @@ def test_metrics():
         assert K.eval(output).shape == ()
 
 
+def test_matthews_correlation():
+    y_true = K.variable(np.array([0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0]))
+    y_pred = K.variable(np.array([1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0]))
+
+    # Calculated using sklearn.metrics.matthews_corrcoef
+    actual = -0.14907119849998601
+
+    calc = K.eval(metrics.matthews_correlation(y_true, y_pred))
+    epsilon = 1e-05
+    assert actual - epsilon <= calc <= actual + epsilon
+
+
 def test_sparse_metrics():
     for metric in all_sparse_metrics:
         y_a = K.variable(np.random.randint(0, 7, (6,)), dtype=K.floatx())