I'm trying to understand how tf.keras.metrics.AUC(multi_label=True) works. From the docs, I'm led to understand that when working with multi-label vectors, each class is computed individually, then averaged.
However, I can't seem to get the following trivial case to compute correctly. That is, if the prediction is the same as the expected vector, why is the output not 1.0?
y_true = [
[1, 0, 0, 0, 1],
]
acc = tf.keras.metrics.AUC(multi_label=True, num_labels=5)
acc.reset_state()
acc.update_state(tf.constant(y_true), tf.constant(y_true))
acc.result().numpy()
>>> 0.0