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Describe the feature and the current behavior/state. Please add multi-class precision and recall metrics, much like that in sklearn.metrics.

Currently, tf.metrics.Precision and tf.metrics.Recall only support binary labels.

sklearn.metrics supports averages of types binary, micro (global average), macro (average of metric per label), weighted (macro, but weighted), and samples.

Relevant information

• Are you willing to contribute it (yes/no): If someone can guide me, I am willing to give it a try.

• Are you willing to maintain it going forward? (yes/no): If I implement, then yes.

• Is there a relevant academic paper? (if so, where): The definitions are standard, or see sklearn implementation.

• Is there already an implementation in another framework? (if so, where): Please see sklearn/metrics/_classification.py.

• Was it part of tf.contrib? (if so, where): I couldn't find one.

Which API type would this fall under (layer, metric, optimizer, etc.) metric.

Who will benefit with this feature? People who are performing multi-label classification and are looking for precision and recall metrics.

Any other info. There is already an implementation of f1-score. Perhaps these two metrics can piggy back on that.

Also, these metrics need to mesh with the binary metrics provided by tf.

@Squadrick Please check if this feature is already added in the tensorflow main code base. There is an issue similar to this in the tensorflow repo as well but it seems to be not resolved. If it is not there then I have added some changes to support this feature. I can create a pull request.

Thank you

We have something in TFX. See https://www.tensorflow.org/tfx/model_analysis/metrics#multi-classmulti-label_classification_metrics If we are going to get this in charge here in Addons we could notify these two upstream tickets: https://github.com/tensorflow/tensorflow/issues/37256 https://github.com/keras-team/keras/issues/6507

In case it's useful, I gave an example on how to adapt the existing binary label-oriented metrics for a multi-class setting in https://github.com/tensorflow/tensorflow/issues/37256#issuecomment-751130406. HTH!

Perhaps I am misunderstanding, but I have been running a multiclass classification model and using the following precision and recall metrics:

model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['acc',tf.keras.metrics.Precision(),tf.keras.metrics.Recall()])

One thing I am having trouble with is multiclass classification reports from sklearn - any pointers, other good issue threads people have seen?

If anyone searches for this, maybe this will help. I created new metric to get multi class confusion matrix, I know we already have one in addons, but it wasn't helping my cause.

So the metric looks like this:

class MultiClassConfusionMatrix(tf.keras.metrics.Metric):

    def __init__(self, num_classes, name="multi_class_confusion_matrix", **kwargs):
        super(MultiClassConfusionMatrix, self).__init__(name=name, **kwargs)

        self.num_classes = num_classes
        
        self.mctp_conf_matrix = self.add_weight(name="confusion_matrix", shape=(num_classes, num_classes), dtype=tf.dtypes.int32, initializer='zeros')
        
    def update_state(self, y_true, y_pred, sample_weight=None):
        y_true = tf.reshape(y_true, [-1])
        y_pred = tf.argmax(y_pred, axis=-1)
        
        tmp = tf.math.confusion_matrix(y_true, y_pred, self.num_classes)
        res = tf.math.add(tmp, self.mctp_conf_matrix)

        self.mctp_conf_matrix.assign(res)

    def result(self):
        return self.mctp_conf_matrix
    
    def reset_states(self):
        """
        In version 2.5.0 this method is renamed to "reset_state"
        """
        self.mctp_conf_matrix.assign(tf.zeros((self.num_classes, self.num_classes), dtype=tf.dtypes.int32))

This way you basically get matrix at the end for example evaluation, after you do model.evaluate() where you could easily calculate Precision, Recall, F1 score (Micro or Macro) just by using the results. You could also implement that in def result(self) that way you would get those scores for each epoch when training. Basically you can calculate all the metrics that you get with report in sklearn for multi class.

If this is something that could help the people I will gladly make a PR.

One thing to note is that this class accepts only classes for which input Y labels are for defined like 0, 1, 2, 3, 4, .. etc. till how many classes you have. This can be easily tweaked.

What are your thoughts?

TensorFlow Addons is transitioning to a minimal maintenance and release mode. New features will not be added to this repository. For more information, please see our public messaging on this decision: TensorFlow Addons Wind Down

Please consider sending feature requests / contributions to other repositories in the TF community with a similar charters to TFA: Keras Keras-CV Keras-NLP