lenskit.metrics.Precision#

class lenskit.metrics.Precision(k=None)#

Bases: ListMetric, RankingMetricBase

Compute recommendation precision. This is computed as:

\[\frac{|L \cap I_u^{\mathrm{test}}|}{|L|}\]

In the uncommon case that k is specified and len(recs) < k, this metric uses len(recs) as the denominator.

__init__(k=None)#

Methods

`__init__`([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 `k`.

Attributes

`default`
`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:

Return type:

float