AUGRC#

class torch_uncertainty.metrics.classification.AUGRC(**kwargs)[source]#

Calculate The Area Under the Generalized Risk-Coverage curve (AUGRC).

The Area Under the Generalized Risk-Coverage curve (AUGRC) for Selective Classification (SC) performance assessment. It avoids putting too much weight on the most confident samples.

As input to forward and update the metric accepts the following input:

  • preds (Tensor): A float tensor of shape (N, ...) containing probabilities for each observation.

  • target (Tensor): An int tensor of shape (N, ...) containing ground-truth labels.

As output to forward and compute the metric returns the following output:

  • Augrc (Tensor): A scalar tensor containing the area under the risk-coverage curve

Parameters:

kwargs – Additional keyword arguments.

References

[1] Traub et al. Overcoming Common Flaws in the Evaluation of Selective Classification Systems.

See also

  • AURC : Parent class, the AURC metric

compute()[source]#

Normalize the AUGRC as if its support was between 0 and 1. This has an impact on the AUGRC when the number of samples is small.

Returns:

The AUGRC.

Return type:

Tensor

plot(ax=None, plot_value=True, name=None)[source]#

Plot the generalized risk-cov. curve corresponding to the inputs passed to update.

Parameters:

  • ax (Axes | None, optional) – An matplotlib axis object. If provided will add plot to this axis. Defaults to None.

  • plot_value (bool, optional) – Whether to print the AURC value on the plot. Defaults to True.

  • name (str | None, optional) – Name of the model. Defaults to None.

Returns:

Figure object and Axes object

Return type:

tuple[[Figure | None], Axes]