ribs.archives.ArchiveStats

class ribs.archives.ArchiveStats(num_elites, coverage, qd_score, norm_qd_score, obj_max, obj_mean)[source]

Holds statistics about an archive.

Attributes of type floating will match the dtype of their archive.

Methods

Attributes

num_elites

Number of elites in the archive.

coverage

Proportion of cells in the archive that have an elite - always in the range \([0,1]\).

qd_score

QD score, i.e. sum of objective values of all elites in the archive.

norm_qd_score

Normalized QD score, i.e. the QD score divided by the number of cells in the archive.

obj_max

Maximum objective value of the elites in the archive.

obj_mean

Mean objective value of the elites in the archive.

coverage: numpy.floating

Proportion of cells in the archive that have an elite - always in the range \([0,1]\).

norm_qd_score: numpy.floating

Normalized QD score, i.e. the QD score divided by the number of cells in the archive.

num_elites: int

Number of elites in the archive.

obj_max: numpy.floating

Maximum objective value of the elites in the archive. None if there are no elites in the archive.

Note

If the archive is non-elitist (this occurs when using the archive with a learning rate which is not 1.0, as in CMA-MAE), then an elite with this objective may no longer exist in the archive because it was replaced with an elite with a lower objective value. This can happen because in non-elitist archives, new solutions only need to exceed the threshold of the cell they are being inserted into, not the objective of the elite currently in the cell. See #314 for more info.

obj_mean: numpy.floating

Mean objective value of the elites in the archive. None if there are no elites in the archive.

qd_score: numpy.floating

QD score, i.e. sum of objective values of all elites in the archive. If qd_score_offset was passed in to the archive, this QD score normalizes the objectives by subtracting the offset from all objective values before computing the QD score.