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Sep 22, 2020
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#2066-doc-update-metrics-latest #2171

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nataliyah123 Sep 21, 2020
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nataliyah123 Sep 21, 2020
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nataliyah123 Sep 22, 2020
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51 changes: 32 additions & 19 deletions tensorflow_addons/metrics/cohens_kappa.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
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6 changes: 3 additions & 3 deletions tensorflow_addons/metrics/geometric_mean.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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

"""
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63 changes: 30 additions & 33 deletions tensorflow_addons/metrics/hamming.py
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Expand Up @@ -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))
Expand Down Expand Up @@ -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]")
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24 changes: 12 additions & 12 deletions tensorflow_addons/metrics/matthews_correlation_coefficient.py
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Expand Up @@ -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
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60 changes: 36 additions & 24 deletions tensorflow_addons/metrics/multilabel_confusion_matrix.py
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Expand Up @@ -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
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16 changes: 9 additions & 7 deletions tensorflow_addons/metrics/r_square.py
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Expand Up @@ -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
Expand Down