lenskit.metrics.ranking#

LensKit ranking (and list) metrics.

Classes#

`RankingMetricBase`	Base class for most ranking metrics, implementing an `n` parameter for
`DCG`	Compute the _unnormalized_ discounted cumulative gain [JarvelinKekalainen02].
`NDCG`	Compute the normalized discounted cumulative gain [JarvelinKekalainen02].
`Entropy`	Evaluate diversity using Shannon entropy over item categories.
`RankBiasedEntropy`	Evaluate diversity using rank-biased Shannon entropy over item categories.
`ExposureGini`	Measure exposure distribution of recommendations with the Gini coefficient.
`ListGini`	Measure item diversity of recommendations with the Gini coefficient.
`Hit`	Compute whether or not a list is a hit; any list with at least one
`ILS`	Evaluate recommendation diversity using intra-list similarity (ILS).
`AveragePrecision`	Compute Average Precision (AP) for a single user's recommendations. This is
`MeanPopRank`	Compute the _obscurity_ (mean popularity rank) of the recommendations.
`Precision`	Compute recommendation precision. This is computed as:
`Recall`	Compute recommendation recall. This is computed as:
`RBP`	Evaluate recommendations with rank-biased precision [MZ08].
`RecipRank`	Compute the reciprocal rank [KV97] of the first relevant
`GeometricRankWeight`	Geometric cascade weighting for result ranks.
`LogRankWeight`	Logarithmic weighting for result ranks, as used in NDCG.
`RankWeight`	Base class for rank weighting models.

rank_biased_precision(good, weights[, normalization])

Compute rank-biased precision given explicit weights.

lenskit.metrics.ranking.rank_biased_precision(good, weights, normalization=1.0)#

Compute rank-biased precision given explicit weights.

Parameters:

good (numpy.ndarray) – Boolean array indicating relevant items at each position.
weights (numpy.ndarray) – Weight for each item position (same length as good).
normalization (float) – Optional normalization factor, defaults to 1.0.

Returns:

RBP score

Return type:

float