lenskit.metrics.RecipRank#

class lenskit.metrics.RecipRank(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 $\kappa =\mathrm{\infty }$ 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:

k (int | None)

__init__(k=None)#

Parameters:

k (int | None)

Methods

__init__([k])
measure_list(recs, test)	Compute the metric value for a single result list.
truncate(items)	Truncate an item list if it is longer than k.

Attributes

default	The default value to infer when computing statistics over missing values.
k	The maximum length of rankings to consider.
label	The metric's default label in output.

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:

• recs (ItemList)

• test (ItemList)

Return type:

float