#2066-doc-update-metrics-latest (#2171)

nataliyah123 · web-flow · commit 710903f896de · 2020-09-22T02:19:22.000-07:00
* all_done

* cohen_kapa_space

* requested changes
diff --git a/tensorflow_addons/metrics/cohens_kappa.py b/tensorflow_addons/metrics/cohens_kappa.py
@@ -38,28 +38,41 @@ 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, sparse_labels=True)
-    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, sparse_labels=True)
-    m.update_state(actuals, preds, sample_weight=weights)
-    print('Final result: ', m.result().numpy()) # Result: 0.37209308
-    ```
+    >>> y_true = np.array([4, 4, 3, 4, 2, 4, 1, 1], dtype=np.int32)
+    >>> y_pred = 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)
+    >>> metric = tfa.metrics.CohenKappa(num_classes=5, sparse_labels=True)
+    >>> metric.update_state(y_true , y_pred)
+    <tf.Tensor: shape=(5, 5), dtype=float32, numpy=
+     array([[0., 0., 0., 0., 0.],
+            [0., 2., 0., 0., 0.],
+            [0., 0., 0., 0., 1.],
+            [0., 0., 0., 1., 0.],
+            [0., 0., 1., 0., 3.]], dtype=float32)>
+    >>> result = metric.result()
+    >>> result.numpy()
+    0.61904764
+    >>> # To use this with weights, sample_weight argument can be used.
+    >>> metric = tfa.metrics.CohenKappa(num_classes=5, sparse_labels=True)
+    >>> metric.update_state(y_true , y_pred , sample_weight=weights)
+    <tf.Tensor: shape=(5, 5), dtype=float32, numpy=
+     array([[ 0.,  0.,  0.,  0.,  0.],
+            [ 0.,  6.,  0.,  0.,  0.],
+           [ 0.,  0.,  0.,  0., 10.],
+            [ 0.,  0.,  0.,  2.,  0.],
+            [ 0.,  0.,  2.,  0.,  7.]], dtype=float32)>
+    >>> result = metric.result()
+    >>> result.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))
-    model.compile('sgd', loss='mse')
-    ```
+    >>> inputs = tf.keras.Input(shape=(10,))
+    >>> x = tf.keras.layers.Dense(10)(inputs)
+    >>> outputs = tf.keras.layers.Dense(1)(x)
+    >>> model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
+    >>> model.compile('sgd', loss='mse', metrics=[tfa.metrics.CohenKappa(num_classes=3, sparse_labels=True)])
+
     """
 
     @typechecked
diff --git a/tensorflow_addons/metrics/geometric_mean.py b/tensorflow_addons/metrics/geometric_mean.py
@@ -35,9 +35,9 @@ class GeometricMean(Metric):
 
     Usage:
 
-    >>> m = tfa.metrics.GeometricMean()
-    >>> m.update_state([1, 3, 5, 7, 9])
-    >>> m.result().numpy()
+    >>> metric = tfa.metrics.GeometricMean()
+    >>> metric.update_state([1, 3, 5, 7, 9])
+    >>> metric.result().numpy()
     3.9362833
 
     """
diff --git a/tensorflow_addons/metrics/hamming.py b/tensorflow_addons/metrics/hamming.py
@@ -38,14 +38,14 @@ def hamming_distance(actuals: TensorLike, predictions: TensorLike) -> tf.Tensor:
 
     Usage:
 
-    ```python
-    actuals = tf.constant([1, 1, 0, 0, 1, 0, 1, 0, 0, 1],
-                          dtype=tf.int32)
-    predictions = tf.constant([1, 0, 0, 0, 1, 0, 0, 1, 0, 1],
-                              dtype=tf.int32)
-    result = hamming_distance(actuals, predictions)
-    print('Hamming distance: ', result.numpy())
-    ```
+    >>> actuals = tf.constant([1, 1, 0, 0, 1, 0, 1, 0, 0, 1],
+    ... dtype=tf.int32)
+    >>> predictions = tf.constant([1, 0, 0, 0, 1, 0, 0, 1, 0, 1],
+    ... dtype=tf.int32)
+    >>> metric = hamming_distance(actuals, predictions)
+    >>> metric.numpy()
+    0.3
+
     """
     result = tf.not_equal(actuals, predictions)
     not_eq = tf.reduce_sum(tf.cast(result, tf.float32))
@@ -84,31 +84,28 @@ def hamming_loss_fn(
 
     Usage:
 
-    ```python
-    # multi-class hamming loss
-    hl = HammingLoss(mode='multiclass', threshold=0.6)
-    actuals = tf.constant([[1, 0, 0, 0],[0, 0, 1, 0],
-                       [0, 0, 0, 1],[0, 1, 0, 0]],
-                      dtype=tf.float32)
-    predictions = tf.constant([[0.8, 0.1, 0.1, 0],
-                               [0.2, 0, 0.8, 0],
-                               [0.05, 0.05, 0.1, 0.8],
-                               [1, 0, 0, 0]],
-                          dtype=tf.float32)
-    hl.update_state(actuals, predictions)
-    print('Hamming loss: ', hl.result().numpy()) # 0.25
-
-    # multi-label hamming loss
-    hl = HammingLoss(mode='multilabel', threshold=0.8)
-    actuals = tf.constant([[1, 0, 1, 0],[0, 1, 0, 1],
-                       [0, 0, 0,1]], dtype=tf.int32)
-    predictions = tf.constant([[0.82, 0.5, 0.90, 0],
-                               [0, 1, 0.4, 0.98],
-                               [0.89, 0.79, 0, 0.3]],
-                               dtype=tf.float32)
-    hl.update_state(actuals, predictions)
-    print('Hamming loss: ', hl.result().numpy()) # 0.16666667
-    ```
+    >>> # multi-class hamming loss
+    >>> hl = HammingLoss(mode='multiclass', threshold=0.6)
+    >>> actuals = tf.constant([[1, 0, 0, 0],[0, 0, 1, 0],
+    ... [0, 0, 0, 1],[0, 1, 0, 0]], dtype=tf.float32)
+    >>> predictions = tf.constant([[0.8, 0.1, 0.1, 0],
+    ... [0.2, 0, 0.8, 0],[0.05, 0.05, 0.1, 0.8],[1, 0, 0, 0]],
+    ... dtype=tf.float32)
+    >>> hl.update_state(actuals,  predictions)
+    <tf.Variable 'UnreadVariable' shape=() dtype=float32, numpy=4.0>
+    >>> hl.result().numpy()
+    0.25
+    >>> # multi-label hamming loss
+    >>> hl = HammingLoss(mode='multilabel', threshold=0.8)
+    >>> actuals = tf.constant([[1, 0, 1, 0],[0, 1, 0, 1],
+    ... [0, 0, 0,1]], dtype=tf.int32)
+    >>> predictions = tf.constant([[0.82, 0.5, 0.90, 0],
+    ... [0, 1, 0.4, 0.98],[0.89, 0.79, 0, 0.3]],dtype=tf.float32)
+    >>> hl.update_state(actuals, predictions)
+    <tf.Variable 'UnreadVariable' shape=() dtype=float32, numpy=3.0>
+    >>> hl.result().numpy()
+    0.16666667
+
     """
     if mode not in ["multiclass", "multilabel"]:
         raise TypeError("mode must be either multiclass or multilabel]")
diff --git a/tensorflow_addons/metrics/matthews_correlation_coefficient.py b/tensorflow_addons/metrics/matthews_correlation_coefficient.py
@@ -42,18 +42,18 @@ class MatthewsCorrelationCoefficient(tf.keras.metrics.Metric):
           ((TP + FP) * (TP + FN) * (TN + FP ) * (TN + FN))^(1/2)
 
     Usage:
-    ```python
-    actuals = 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)
-    # Matthews correlation coefficient
-    mcc = MatthewsCorrelationCoefficient(num_classes=1)
-    mcc.update_state(actuals, preds)
-    print('Matthews correlation coefficient is:',
-    mcc.result().numpy())
-    # Matthews correlation coefficient is : -0.33333334
-    ```
+
+    >>> actuals = 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)
+    >>> # Matthews correlation coefficient
+    >>> metric = tfa.metrics.MatthewsCorrelationCoefficient(num_classes=1)
+    >>> metric.update_state(y_true = actuals, y_pred = preds)
+    >>> result = metric(y_true = actuals, y_pred = preds)
+    >>> result.numpy()
+    array([-0.33333334], dtype=float32)
+
     """
 
     @typechecked
diff --git a/tensorflow_addons/metrics/multilabel_confusion_matrix.py b/tensorflow_addons/metrics/multilabel_confusion_matrix.py
@@ -45,31 +45,43 @@ class MultiLabelConfusionMatrix(Metric):
     - false positives for class i in M(0,1)
     - false negatives for class i in M(1,0)
     - true positives for class i in M(1,1)
+    Usage:
+
+    >>> # multilabel confusion matrix
+    >>> y_true = tf.constant([[1, 0, 1], [0, 1, 0]],
+    ... dtype=tf.int32)
+    >>> y_pred = tf.constant([[1, 0, 0],[0, 1, 1]],
+    ... dtype=tf.int32)
+    >>> metric = tfa.metrics.MultiLabelConfusionMatrix(num_classes=3)
+    >>> metric.update_state(y_true, y_pred)
+    >>> result = metric.result()
+    >>> result.numpy()  #doctest: -DONT_ACCEPT_BLANKLINE
+    array([[[1., 0.],
+            [0., 1.]],
+    <BLANKLINE>
+           [[1., 0.],
+            [0., 1.]],
+    <BLANKLINE>
+           [[0., 1.],
+            [1., 0.]]], dtype=float32)
+    >>> # if multiclass input is provided
+    >>> y_true = tf.constant([[1, 0, 0], [0, 1, 0]],
+    ... dtype=tf.int32)
+    >>> y_pred = tf.constant([[1, 0, 0],[0, 0, 1]],
+    ... dtype=tf.int32)
+    >>> metric = tfa.metrics.MultiLabelConfusionMatrix(num_classes=3)
+    >>> metric.update_state(y_true, y_pred)
+    >>> result = metric.result()
+    >>> result.numpy() #doctest: -DONT_ACCEPT_BLANKLINE
+    array([[[1., 0.],
+               [0., 1.]],
+    <BLANKLINE>
+            [[1., 0.],
+             [1., 0.]],
+    <BLANKLINE>
+            [[1., 1.],
+             [0., 0.]]], dtype=float32)
 
-    ```python
-    # multilabel confusion matrix
-    y_true = tf.constant([[1, 0, 1], [0, 1, 0]],
-             dtype=tf.int32)
-    y_pred = tf.constant([[1, 0, 0],[0, 1, 1]],
-             dtype=tf.int32)
-    output = MultiLabelConfusionMatrix(num_classes=3)
-    output.update_state(y_true, y_pred)
-    print('Confusion matrix:', output.result().numpy())
-
-    # Confusion matrix: [[[1 0] [0 1]] [[1 0] [0 1]]
-                      [[0 1] [1 0]]]
-
-    # if multiclass input is provided
-    y_true = tf.constant([[1, 0, 0], [0, 1, 0]],
-             dtype=tf.int32)
-    y_pred = tf.constant([[1, 0, 0],[0, 0, 1]],
-             dtype=tf.int32)
-    output = MultiLabelConfusionMatrix(num_classes=3)
-    output.update_state(y_true, y_pred)
-    print('Confusion matrix:', output.result().numpy())
-
-    # Confusion matrix: [[[1 0] [0 1]] [[1 0] [1 0]] [[1 1] [0 0]]]
-    ```
     """
 
     @typechecked
diff --git a/tensorflow_addons/metrics/r_square.py b/tensorflow_addons/metrics/r_square.py
@@ -60,13 +60,15 @@ class RSquare(Metric):
      of the same metric.
 
      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
-    ```
+
+    >>> y_true = tf.constant([1, 4, 3], dtype=tf.float32)
+    >>> y_pred = tf.constant([2, 4, 4], dtype=tf.float32)
+    >>> metric = tfa.metrics.r_square.RSquare()
+    >>> metric.update_state(y_true, y_pred)
+    >>> result = metric.result()
+    >>> result.numpy()
+    0.57142854
+
     """
 
     @typechecked
