Label Ranking Loss

label-ranking-loss

Metric label_ranking_loss from scikit-learn (sklearn.metrics.label_ranking_loss)

When to invoke this skill

The user has predictions + ground truth and asks to evaluate with label_ranking_loss, or mentions sklearn.metrics.label_ranking_loss directly, or wants the standard scikit-learn implementation.

Reference signature

from sklearn.metrics import label_ranking_loss

# label_ranking_loss(y_true, y_score, *, sample_weight=None)

Library docstring

Compute Ranking loss measure.

Compute the average number of label pairs that are incorrectly ordered
given y_score weighted by the size of the label set and the number of
labels not in the label set.

This is similar to the error set size, but weighted by the number of
relevant and irrelevant labels. The best performance is achieved with
a ranking loss of zero.

Read more in the :ref:`User Guide <label_ranking_loss>`.

.. versionadded:: 0.17
   A function *label_ranking_loss*

Parameters
----------
y_true : {array-like, sparse matrix} of shape (n_samples, n_labels)
    True binary labels in binary indicator format.

y_score : array-like of shape (n_samples, n_labels)
    Target scores, can either be probability estimates of the positive
    class, confidence values, or non-thresholded measure of decisions
    (as returned by "decision_function" on some classifiers).
    For :term:`decision_function` scores, values greater than or equal to
    zero should indicate the positive class.

sample_weight : array-like of shape (n_samples,), default=None
    Sample weights.

Returns
-------
loss : float
    Average number of label pairs that are incorrectly ordered given
    y_score weighted by the size of the label set and the number of labels not
    in the label set.

References
----------
.. [1] Tsoumakas, G., Katakis, I., & Vlahavas, I. (2010).
       Mining multi-label data. In Data mining and knowledge discovery
       handbook (pp. 667-685). Springer US.

Examples
--------
>>> from sklearn.metrics import label_ranking_loss
>>> y_true = [[1, 0, 0], [0, 0, 1]]
>>> y_score = [[0.75, 0.5, 1], [1, 0.2, 0.1]]
>>> label_ranking_loss(y_true, y_score)
0.75

Quick recipe

import sklearn.metrics as _m
score = _m.label_ranking_loss(y_true, y_pred)

Don'ts

• Don't reimplement when the library version handles edge cases (NaN, ties, empty inputs) better than a hand-rolled formula.
• Always check the library version's argument order — sklearn is (y_true, y_pred) while torchmetrics is (preds, target).