summary_plot#
- scio.eval.summary_plot(confs_ind, confs_oods, *, scores_and_layers=None, keep=None, oods_title=None, legend=True, convex_hull=False, show=True, block=None, **hist_kw)[source]#
Plot and show histograms for each score, ROCs for each OoD set.
- Parameters:
confs_ind (
NDArray) – First output ofcompute_confidence().confs_oods (
tuple[NDArray, ...]) – Second output ofcompute_confidence().scores_and_layers (
tuple[ScoreClassifAndLayers | str, ...], optional) – Seeroc_scores().keep (
ArrayLike, optional) – If provided, the plots are restricted to the corresponding scores. In this case, it must be a \(1\)D array of integer indexes, or a boolean mask. Using integer indexes allows arbitrary reordering of the scores.oods_title (
tuple[str, ...], optional) – Seehistogram_oods().legend (
tuple[bool, bool] | bool) – Whether to show legends for histograms and ROCs respectively. If a uniqueboolis provided, it is used for both. Defaults toTrue.convex_hull (
bool) – Seeroc_scores().show (
bool) – Whether to end with aplt.show()call. Defaults toTrue.block (
bool, optional) – Ifshow, passed toplt.show().**hist_kw – Passed to
sns.histplot(), except theaxkwarg. Unless overidden, the following values are also passed:bins=30,stat="density"andcommon_norm=False.
Note
In its current state,
summary_plot()may render cropped or incomplete legends when working with a lot of scores or OoD sets. In this case, you may wish to hide one or both legends with thelegendoption.