lenskit.metrics.ranking.ExposureGini

class lenskit.metrics.ranking.ExposureGini(n=None, *, k=None, items, weight=GeometricRankWeight())

Bases: GiniBase

Measure exposure distribution of recommendations with the Gini coefficient.

This uses a weighting model to compute the exposure of each item in each list, and computes the Gini coefficient of the total exposure.

Parameters:

• n (int | None) – The maximum recommendation list length.

• items (lenskit.data.Vocabulary | lenskit.data.Dataset) – The item vocabulary or a dataset from which to extract the items.

• weight (lenskit.metrics.ranking._weighting.RankWeight) – The rank weighting model to use. Defaults to GeometricRankWeight with the specified patience parameter.

• k (int | None)

Stability:

Caller (see Stability Levels).

weight: lenskit.metrics.ranking._weighting.RankWeight

measure_list(output, test)

Compute measurements for a single list.

Returns:

• A float for simple metrics

• Intermediate data for decomposed metrics

• A dict mapping metric names to values for multi-metric classes

Parameters:

output (lenskit.data.ItemList)

Return type:

tuple[numpy.typing.NDArray[numpy.int32], numpy.typing.NDArray[numpy.float64]]