Rename deprecated method (#2533)

* Rename deprecated method

tensorflow_addons/metrics/README.md

@@ -15,7 +15,7 @@ must:
 Any PR which adds a new metric must ensure that:
 
 1. It inherits from the `tf.keras.metrics.Metric` class.
-2. Overrides the `update_state()`, `result()`, and `reset_states()` methods.
+2. Overrides the `update_state()`, `result()`, and `reset_state()` methods.
 3. Implements a `get_config()` method.
 
 The implementation must also ensure that the following cases are well tested and supported:

tensorflow_addons/metrics/cohens_kappa.py

@@ -254,11 +254,17 @@ def get_config(self):
         base_config = super().get_config()
         return {**base_config, **config}
 
-    def reset_states(self):
+    def reset_state(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),
             )
+
+    def reset_states(self):
+        # Backwards compatibility alias of `reset_state`. New classes should
+        # only implement `reset_state`.
+        # Required in Tensorflow < 2.5.0
+        return self.reset_state()

tensorflow_addons/metrics/f_scores.py

@@ -203,10 +203,16 @@ def get_config(self):
         base_config = super().get_config()
         return {**base_config, **config}
 
-    def reset_states(self):
+    def reset_state(self):
         reset_value = tf.zeros(self.init_shape, dtype=self.dtype)
         K.batch_set_value([(v, reset_value) for v in self.variables])
 
+    def reset_states(self):
+        # Backwards compatibility alias of `reset_state`. New classes should
+        # only implement `reset_state`.
+        # Required in Tensorflow < 2.5.0
+        return self.reset_state()
+
 
 @tf.keras.utils.register_keras_serializable(package="Addons")
 class F1Score(FBetaScore):

tensorflow_addons/metrics/geometric_mean.py

@@ -75,5 +75,11 @@ def result(self) -> tf.Tensor:
         ret = tf.math.exp(self.total / self.count)
         return tf.cast(ret, dtype=self.dtype)
 
-    def reset_states(self) -> None:
+    def reset_state(self) -> None:
         K.batch_set_value([(v, 0) for v in self.variables])
+
+    def reset_states(self):
+        # Backwards compatibility alias of `reset_state`. New classes should
+        # only implement `reset_state`.
+        # Required in Tensorflow < 2.5.0
+        return self.reset_state()

tensorflow_addons/metrics/kendalls_tau.py

@@ -76,7 +76,7 @@ def __init__(
         self.preds_max = preds_max
         self.actual_cutpoints = actual_cutpoints
         self.preds_cutpoints = preds_cutpoints
-        self.reset_states()
+        self.reset_state()
 
     def update_state(self, y_true, y_pred, sample_weight=None):
         """Accumulates ranks.
@@ -177,7 +177,7 @@ def get_config(self):
         base_config = super().get_config()
         return {**base_config, **config}
 
-    def reset_states(self):
+    def reset_state(self):
         """Resets all of the metric state variables."""
         self.actual_cuts = tf.linspace(
             tf.cast(self.actual_min, tf.float32),
@@ -201,3 +201,9 @@ def reset_states(self):
             tf.zeros((0, 1), dtype=tf.int64), [], [self.preds_cutpoints]
         )
         self.n = 0
+
+    def reset_states(self):
+        # Backwards compatibility alias of `reset_state`. New classes should
+        # only implement `reset_state`.
+        # Required in Tensorflow < 2.5.0
+        return self.reset_state()

tensorflow_addons/metrics/matthews_correlation_coefficient.py

@@ -122,11 +122,17 @@ def get_config(self):
         base_config = super().get_config()
         return {**base_config, **config}
 
-    def reset_states(self):
+    def reset_state(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),
             )
+
+    def reset_states(self):
+        # Backwards compatibility alias of `reset_state`. New classes should
+        # only implement `reset_state`.
+        # Required in Tensorflow < 2.5.0
+        return self.reset_state()

tensorflow_addons/metrics/multilabel_confusion_matrix.py

@@ -177,6 +177,12 @@ def get_config(self):
         base_config = super().get_config()
         return {**base_config, **config}
 
-    def reset_states(self):
+    def reset_state(self):
         reset_value = np.zeros(self.num_classes, dtype=np.int32)
         K.batch_set_value([(v, reset_value) for v in self.variables])
+
+    def reset_states(self):
+        # Backwards compatibility alias of `reset_state`. New classes should
+        # only implement `reset_state`.
+        # Required in Tensorflow < 2.5.0
+        return self.reset_state()

tensorflow_addons/metrics/r_square.py

@@ -179,10 +179,16 @@ def result(self) -> tf.Tensor:
 
         return r2_score
 
-    def reset_states(self) -> None:
+    def reset_state(self) -> None:
         # The state of the metric will be reset at the start of each epoch.
         K.batch_set_value([(v, np.zeros(v.shape)) for v in self.variables])
 
+    def reset_states(self):
+        # Backwards compatibility alias of `reset_state`. New classes should
+        # only implement `reset_state`.
+        # Required in Tensorflow < 2.5.0
+        return self.reset_state()
+
     def get_config(self):
         config = {
             "y_shape": self.y_shape,

tensorflow_addons/metrics/tests/cohens_kappa_test.py

@@ -48,9 +48,9 @@ def update_obj_states(obj1, obj2, obj3, actuals, preds, weights):
 
 
 def reset_obj_states(obj1, obj2, obj3):
-    obj1.reset_states()
-    obj2.reset_states()
-    obj3.reset_states()
+    obj1.reset_state()
+    obj2.reset_state()
+    obj3.reset_state()
 
 
 def check_results(objs, values):
@@ -128,7 +128,7 @@ def test_kappa_with_sample_weights():
     check_results([kp_obj1, kp_obj2, kp_obj3], [-0.25473321, -0.38992332, -0.60695344])
 
 
-def test_kappa_reset_states():
+def test_kappa_reset_state():
     # Initialize
     kp_obj1, kp_obj2, kp_obj3 = initialize_vars()
 

tensorflow_addons/metrics/tests/geometric_mean_test.py

@@ -92,10 +92,10 @@ def test_call_gmean(values, expected):
     np.testing.assert_equal(len(values), count)
 
 
-def test_reset_states():
+def test_reset_state():
     obj = GeometricMean()
     obj.update_state([1, 2, 3, 4, 5])
-    obj.reset_states()
+    obj.reset_state()
     assert obj.total.numpy() == 0.0
     assert obj.count.numpy() == 0.0
 

tensorflow_addons/metrics/tests/matthews_correlation_coefficient_test.py

@@ -109,7 +109,7 @@ def test_keras_model():
     model.fit(data, labels, epochs=1, batch_size=32, verbose=0)
 
 
-def test_reset_states_graph():
+def test_reset_state_graph():
     gt_label = tf.constant(
         [[0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0]], dtype=tf.float32
     )
@@ -120,9 +120,9 @@ def test_reset_states_graph():
     mcc.update_state(gt_label, preds)
 
     @tf.function
-    def reset_states():
-        mcc.reset_states()
+    def reset_state():
+        mcc.reset_state()
 
-    reset_states()
+    reset_state()
     # Check results
     check_results(mcc, [0])

tensorflow_addons/metrics/tests/r_square_test.py

@@ -54,8 +54,8 @@ def update_obj_states(obj, actuals, preds, sample_weight=None):
 
 
 @tf.function
-def reset_obj_states(obj):
-    obj.reset_states()
+def reset_obj_state(obj):
+    obj.reset_state()
 
 
 def check_results(obj, value):
@@ -120,7 +120,7 @@ def test_r2_random_score():
 
 
 @pytest.mark.usefixtures("maybe_run_functions_eagerly")
-def test_r2_reset_states():
+def test_r2_reset_state():
     actuals = tf.constant([100, 700, 40, 5.7], dtype=tf.float32)
     preds = tf.constant([100, 700, 40, 5.7], dtype=tf.float32)
     actuals = tf.cast(actuals, dtype=tf.float32)
@@ -130,7 +130,7 @@ def test_r2_reset_states():
     # Update
     update_obj_states(r2_obj, actuals, preds)
     # Reset
-    reset_obj_states(r2_obj)
+    reset_obj_state(r2_obj)
     # Check variables
     check_variables(r2_obj, 0.0)