Add support for sparse labels in MatthewsCorrelationCoefficient

Author: lgeiger

tensorflow_addons/metrics/matthews_correlation_coefficient.py

@@ -69,7 +69,7 @@ def __init__(
     ):
         """Creates a Matthews Correlation Coefficient instance."""
         super().__init__(name=name, dtype=dtype)
-        self.num_classes = num_classes
+        self.num_classes = max(2, num_classes)
         self.conf_mtx = self.add_weight(
             "conf_mtx",
             shape=(self.num_classes, self.num_classes),
@@ -82,9 +82,19 @@ def update_state(self, y_true, y_pred, sample_weight=None):
         y_true = tf.cast(y_true, dtype=self.dtype)
         y_pred = tf.cast(y_pred, dtype=self.dtype)
 
+        if y_true.shape[-1] == 1:
+            labels = tf.squeeze(tf.cast(tf.round(y_true), tf.int64), axis=-1)
+        else:
+            labels = tf.argmax(y_true, 1)
+
+        if y_pred.shape[-1] == 1:
+            predictions = tf.squeeze(tf.cast(tf.round(y_pred), tf.int64), axis=-1)
+        else:
+            predictions = tf.argmax(y_pred, 1)
+
         new_conf_mtx = tf.math.confusion_matrix(
-            labels=tf.argmax(y_true, 1),
-            predictions=tf.argmax(y_pred, 1),
+            labels=labels,
+            predictions=predictions,
             num_classes=self.num_classes,
             weights=sample_weight,
             dtype=self.dtype,
@@ -126,7 +136,4 @@ def reset_states(self):
         """Resets all of the metric state variables."""
 
         for v in self.variables:
-            K.set_value(
-                v,
-                np.zeros((self.num_classes, self.num_classes), v.dtype.as_numpy_dtype),
-            )
+            K.set_value(v, np.zeros(v.shape, v.dtype.as_numpy_dtype))

tensorflow_addons/metrics/tests/matthews_correlation_coefficient_test.py

@@ -23,19 +23,30 @@
 
 def test_config():
     # mcc object
-    mcc1 = MatthewsCorrelationCoefficient(num_classes=1)
-    assert mcc1.num_classes == 1
+    mcc1 = MatthewsCorrelationCoefficient(num_classes=2)
+    assert mcc1.num_classes == 2
     assert mcc1.dtype == tf.float32
     # check configure
     mcc2 = MatthewsCorrelationCoefficient.from_config(mcc1.get_config())
-    assert mcc2.num_classes == 1
+    assert mcc2.num_classes == 2
     assert mcc2.dtype == tf.float32
 
 
 def check_results(obj, value):
     np.testing.assert_allclose(value, obj.result().numpy(), atol=1e-6)
 
 
+def test_binary_classes_sparse():
+    gt_label = tf.constant([[1.0], [1.0], [1.0], [0.0]], dtype=tf.float32)
+    preds = tf.constant([[1.0], [0.0], [1.0], [1.0]], dtype=tf.float32)
+    # Initialize
+    mcc = MatthewsCorrelationCoefficient(1)
+    # Update
+    mcc.update_state(gt_label, preds)
+    # Check results
+    check_results(mcc, [-0.33333334])
+
+
 def test_binary_classes():
     gt_label = tf.constant(
         [[0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0]], dtype=tf.float32
@@ -91,6 +102,16 @@ def test_multiple_classes():
     sklearn_result = sklearn_matthew(gt_label.argmax(axis=1), preds.argmax(axis=1))
     check_results(mcc, sklearn_result)
 
+    gt_label_sparse = tf.constant(
+        [[0.0], [2.0], [0.0], [2.0], [1.0], [1.0], [0.0], [0.0], [2.0], [1.0]]
+    )
+    preds_sparse = tf.constant(
+        [[2.0], [0.0], [2.0], [2.0], [2.0], [2.0], [2.0], [0.0], [2.0], [2.0]]
+    )
+    mcc = MatthewsCorrelationCoefficient(3)
+    mcc.update_state(gt_label_sparse, preds_sparse)
+    check_results(mcc, sklearn_result)
+
 
 # Keras model API check
 def test_keras_model():
@@ -110,13 +131,9 @@ def test_keras_model():
 
 
 def test_reset_states_graph():
-    gt_label = tf.constant(
-        [[0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0]], dtype=tf.float32
-    )
-    preds = tf.constant(
-        [[0.0, 1.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0]], dtype=tf.float32
-    )
-    mcc = MatthewsCorrelationCoefficient(2)
+    gt_label = tf.constant([[1.0], [1.0], [1.0], [0.0]], dtype=tf.float32)
+    preds = tf.constant([[1.0], [0.0], [1.0], [1.0]], dtype=tf.float32)
+    mcc = MatthewsCorrelationCoefficient(1)
     mcc.update_state(gt_label, preds)
 
     @tf.function