lenskit.metrics.ListGini#

class lenskit.metrics.ListGini(n=None, *, k=None, items)#

Bases: GiniBase

Measure item diversity of recommendations with the Gini coefficient.

This computes the Gini coefficient of the number of lists that each item appears in.

Parameters:

n (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.
k (int | None)

Stability:

Caller (see Stability Levels).

__init__(n=None, *, k=None, items)#

Parameters:

Methods

`__init__`([n, k])
`compute_list_data`(output, test)	Compute measurements for a single list.
`extract_list_metric`(data, /)	Extract a single-list metric from the per-list measurement result (if applicable).
`extract_list_metrics`(data, /)	Extract per-list metric(s) from intermediate measurement data.
`global_aggregate`(values)	Aggregate list metrics to compute a global value.
`measure_list`(output, test, /)	Compute measurements for a single list.
`summarize`(values, /)	Aggregate intermediate values into summary statistics.
`truncate`(items)	Truncate an item list if it is longer than `n`.

Attributes

`k`
`label`	Default name — class name, optionally @N.
`n`	The maximum length of rankings to consider.
`set_k`
`item_count`

compute_list_data(output, test)#

Compute measurements for a single list.

Use measure_list in Metric for new implementations.

global_aggregate(values)#

Aggregate list metrics to compute a global value.

Implement Metric.summarize() in new implementations.