lenskit.metrics.ranking.RecipRank
=================================

.. py:class:: lenskit.metrics.ranking.RecipRank(n = None, *, k = None)
   :canonical: lenskit.metrics.ranking._recip.RecipRank

   Bases: :py:obj:`lenskit.metrics.ranking._base.ListMetric`, :py:obj:`lenskit.metrics.ranking._base.RankingMetricBase`


   Compute the reciprocal rank :cite:p:`trec5-confusion` 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 :math:`\kappa` denote the 1-based rank of the first relevant item in
   :math:`L`, with :math:`\kappa=\infty` if none of the first :math:`k` items
   in :math:`L` are relevant; then the reciprocal rank is :math:`1 / \kappa`.
   If no elements are relevant, the reciprocal rank is therefore 0.
   :cite:t:`deshpande:iknn` call this the “reciprocal hit rate”.

   :Stability: Caller


   .. py:property:: label

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


   .. py:method:: measure_list(recs, test)

      Compute measurements for a single list.

      :returns:

                - A float for simple metrics
                - Intermediate data for decomposed metrics
                - A dict mapping metric names to values for multi-metric classes