lenskit.metrics.ExposureGini#

class lenskit.metrics.ExposureGini(*, items, k=None, weight=<lenskit.metrics.ranking._weighting.GeometricRankWeight object>)#

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:

k (int | None) – The maximum recommendation list length.
items (int | pd.Series | pd.DataFrame | Dataset) – The total number of items, a data frame or series of item data, or a dataset. If a frame or series is provided, its length will be used as the number of items. If a dataset is provided, its item count will be used.
weight (RankWeight) – The rank weighting model to use. Defaults to GeometricRankWeight with the specified patience parameter.

Stability:

Caller (see Stability Levels).

__init__(*, items, k=None, weight=<lenskit.metrics.ranking._weighting.GeometricRankWeight object>)#

Parameters:

Methods

`__init__`(*, items[, k, weight])
`compute_list_data`(output, test)	Compute measurements for a single list.
`extract_list_metric`(metric, /)	Extract a single-list metric from the per-list measurement result (if applicable).
`global_aggregate`(values)	Aggregate list metrics to compute a global value.
`truncate`(items)	Truncate an item list if it is longer than `k`.

Attributes

`k`	The maximum length of rankings to consider.
`label`	Default name — class name, optionally @K.
`weight`
`item_count`

compute_list_data(output, test)#

Compute measurements for a single list.

global_aggregate(values)#

Aggregate list metrics to compute a global value.