doctest example

Author: autoih

tensorflow_addons/metrics/cohens_kappa.py

@@ -38,23 +38,21 @@ class CohenKappa(Metric):
 
     Usage:
 
-    ```python
-    actuals = np.array([4, 4, 3, 4, 2, 4, 1, 1], dtype=np.int32)
-    preds = np.array([4, 4, 3, 4, 4, 2, 1, 1], dtype=np.int32)
-    weights = np.array([1, 1, 2, 5, 10, 2, 3, 3], dtype=np.int32)
-
-    m = tfa.metrics.CohenKappa(num_classes=5)
-    m.update_state(actuals, preds)
-    print('Final result: ', m.result().numpy()) # Result: 0.61904764
-
-    # To use this with weights, sample_weight argument can be used.
-    m = tfa.metrics.CohenKappa(num_classes=5)
-    m.update_state(actuals, preds, sample_weight=weights)
-    print('Final result: ', m.result().numpy()) # Result: 0.37209308
-    ```
+    >>> actuals = np.array([4, 4, 3, 4, 2, 4, 1, 1], dtype=np.int32)
+    >>> preds = np.array([4, 4, 3, 4, 4, 2, 1, 1], dtype=np.int32)
+    >>> weights = np.array([1, 1, 2, 5, 10, 2, 3, 3], dtype=np.int32)
+    >>> m = tfa.metrics.CohenKappa(num_classes=5)
+    >>> m_update = m.update_state(actuals, preds)
+    >>> m.result()
+    <tf.Tensor: id=203, shape=(), dtype=float32, numpy=0.61904764>
+
+    >>> m = tfa.metrics.CohenKappa(num_classes=5)
+    >>> m_update = m.update_state(actuals, preds, sample_weight=weights)
+    >>> m.result()
+    <tf.Tensor: id=406, shape=(), dtype=float32, numpy=0.37209308>
 
     Usage with tf.keras API:
-
+    
     ```python
     model = tf.keras.models.Model(inputs, outputs)
     model.add_metric(tfa.metrics.CohenKappa(num_classes=5)(outputs))