R-square metric (#283)

Author: SSaishruthi

tensorflow_addons/metrics/BUILD

@@ -7,6 +7,7 @@ py_library(
     srcs = [
         "__init__.py",
         "cohens_kappa.py",
+        "r_square.py",
     ],
     srcs_version = "PY2AND3",
     deps = [
@@ -26,3 +27,16 @@ py_test(
         ":metrics",
     ],
 )
+
+py_test(
+    name = "r_square_test",
+    size = "small",
+    srcs = [
+        "r_square_test.py",
+    ],
+    main = "r_square_test.py",
+    srcs_version = "PY2AND3",
+    deps = [
+        ":metrics",
+    ],
+)

tensorflow_addons/metrics/README.md

@@ -4,11 +4,13 @@
 | Submodule  | Maintainers  | Contact Info   |
 |:---------- |:------------- |:--------------|
 |  cohens_kappa| Aakash Nain   |  aakashnain@outlook.com|
+|  r_square| Saishruthi Swaminathan| saishruthi.tn@gmail.com|
 
 ## Contents
 | Submodule | Metric  | Reference                               |
 |:----------------------- |:-------------------|:---------------|
 | cohens_kappa| CohenKappa|[Cohen's Kappa](https://en.wikipedia.org/wiki/Cohen%27s_kappa)|
+| r_square| RSquare|[R-Sqaure](https://en.wikipedia.org/wiki/Coefficient_of_determination)|
 
 
 ## Contribution Guidelines

tensorflow_addons/metrics/__init__.py

@@ -18,4 +18,5 @@
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow_addons.metrics.cohens_kappa import CohenKappa
+from tensorflow_addons.metrics.cohens_kappa import CohenKappa
+from tensorflow_addons.metrics.r_square import RSquare

tensorflow_addons/metrics/r_square.py

@@ -0,0 +1,73 @@
+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Implements R^2 scores."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+from tensorflow.keras.metrics import Metric
+
+
+class RSquare(Metric):
+    """Compute R^2 score.
+
+     This is also called as coefficient of determination.
+     It tells how close are data to the fitted regression line.
+
+     - Highest score can be 1.0 and it indicates that the predictors
+       perfectly accounts for variation in the target.
+     - Score 0.0 indicates that the predictors do not
+       account for variation in the target.
+     - It can also be negative if the model is worse.
+
+     Usage:
+     ```python
+     actuals = tf.constant([1, 4, 3], dtype=tf.float32)
+     preds = tf.constant([2, 4, 4], dtype=tf.float32)
+     result = tf.keras.metrics.RSquare()
+     result.update_state(actuals, preds)
+     print('R^2 score is: ', r1.result().numpy()) # 0.57142866
+    ```
+    """
+
+    def __init__(self, name='r_square', dtype=tf.float32):
+        super(RSquare, self).__init__(name=name, dtype=dtype)
+        self.squared_sum = self.add_weight("squared_sum", initializer="zeros")
+        self.sum = self.add_weight("sum", initializer="zeros")
+        self.res = self.add_weight("residual", initializer="zeros")
+        self.count = self.add_weight("count", initializer="zeros")
+
+    def update_state(self, y_true, y_pred):
+        y_true = tf.convert_to_tensor(y_true, tf.float32)
+        y_pred = tf.convert_to_tensor(y_pred, tf.float32)
+        self.squared_sum.assign_add(tf.reduce_sum(y_true**2))
+        self.sum.assign_add(tf.reduce_sum(y_true))
+        self.res.assign_add(
+            tf.reduce_sum(tf.square(tf.subtract(y_true, y_pred))))
+        self.count.assign_add(tf.cast(tf.shape(y_true)[0], tf.float32))
+
+    def result(self):
+        mean = self.sum / self.count
+        total = self.squared_sum - 2 * self.sum * mean + self.count * mean**2
+        return 1 - (self.res / total)
+
+    def reset_states(self):
+        # The state of the metric will be reset at the start of each epoch.
+        self.squared_sum.assign(0.0)
+        self.sum.assign(0.0)
+        self.res.assign(0.0)
+        self.count.assign(0.0)

tensorflow_addons/metrics/r_square_test.py

@@ -0,0 +1,85 @@
+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for R-Square Metric."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+from tensorflow_addons.metrics import RSquare
+
+
+class RSquareTest(tf.test.TestCase):
+    def test_config(self):
+        r2_obj = RSquare(name='r_square')
+        self.assertEqual(r2_obj.name, 'r_square')
+        self.assertEqual(r2_obj.dtype, tf.float32)
+        # Check save and restore config
+        r2_obj2 = RSquare.from_config(r2_obj.get_config())
+        self.assertEqual(r2_obj2.name, 'r_square')
+        self.assertEqual(r2_obj2.dtype, tf.float32)
+
+    def initialize_vars(self):
+        r2_obj = RSquare()
+        self.evaluate(tf.compat.v1.variables_initializer(r2_obj.variables))
+        return r2_obj
+
+    def update_obj_states(self, obj, actuals, preds):
+        update_op = obj.update_state(actuals, preds)
+        self.evaluate(update_op)
+
+    def check_results(self, obj, value):
+        self.assertAllClose(value, self.evaluate(obj.result()), atol=1e-5)
+
+    def test_r2_perfect_score(self):
+        actuals = tf.constant([100, 700, 40, 5.7], dtype=tf.float32)
+        preds = tf.constant([100, 700, 40, 5.7], dtype=tf.float32)
+        actuals = tf.constant(actuals, dtype=tf.float32)
+        preds = tf.constant(preds, dtype=tf.float32)
+        # Initialize
+        r2_obj = self.initialize_vars()
+        # Update
+        self.update_obj_states(r2_obj, actuals, preds)
+        # Check results
+        self.check_results(r2_obj, 1.0)
+
+    def test_r2_worst_score(self):
+        actuals = tf.constant([10, 600, 4, 9.77], dtype=tf.float32)
+        preds = tf.constant([1, 70, 40, 5.7], dtype=tf.float32)
+        actuals = tf.constant(actuals, dtype=tf.float32)
+        preds = tf.constant(preds, dtype=tf.float32)
+        # Initialize
+        r2_obj = self.initialize_vars()
+        # Update
+        self.update_obj_states(r2_obj, actuals, preds)
+        # Check results
+        self.check_results(r2_obj, -0.073607)
+
+    def test_r2_random_score(self):
+        actuals = tf.constant([10, 600, 3, 9.77], dtype=tf.float32)
+        preds = tf.constant([1, 340, 40, 5.7], dtype=tf.float32)
+        actuals = tf.constant(actuals, dtype=tf.float32)
+        preds = tf.constant(preds, dtype=tf.float32)
+        # Initialize
+        r2_obj = self.initialize_vars()
+        # Update
+        self.update_obj_states(r2_obj, actuals, preds)
+        # Check results
+        self.check_results(r2_obj, 0.7376327)
+
+
+if __name__ == '__main__':
+    tf.test.main()