lenskit.metrics.RecipRank#

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

Bases: ListMetric, RankingMetricBase

Compute the reciprocal rank [KV97] of the first relevant item in a list of recommendations. Taking the mean of this metric over the recommendation lists in a run yields the MRR (mean reciprocal rank).

Let \(\kappa\) denote the 1-based rank of the first relevant item in \(L\), with \(\kappa=\infty\) if none of the first \(k\) items in \(L\) are relevant; then the reciprocal rank is \(1 / \kappa\). If no elements are relevant, the reciprocal rank is therefore 0. Deshpande and Karypis [DK04] call this the “reciprocal hit rate”.

Stability:
Caller (see Stability Levels).
Parameters:

  • n (int | None)

  • k (int | None)

__init__(n=None, *, k=None)#
Parameters:

  • n (int | None)

  • k (int | None)

Methods

__init__([n, k])

extract_list_metrics(data, /)

Return the given per-list metric result.

measure_list(recs, test)

Compute the metric value for a single result list.

summarize(values, /)

Summarize per-list metric values

truncate(items)

Truncate an item list if it is longer than n.

Attributes

default

k

label

The metric's default label in output.

n

The maximum length of rankings to consider.

set_k

property label#

The metric’s default label in output. The base implementation returns the class name by default.

measure_list(recs, test)#

Compute the metric value for a single result list.

Individual metric classes need to implement this method.

Parameters:
Return type:

float