lenskit.metrics.ranking.MeanPopRank
===================================

.. py:class:: lenskit.metrics.ranking.MeanPopRank(data, *, n = None, k = None, count = 'users')
   :canonical: lenskit.metrics.ranking._pop.MeanPopRank

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


   Compute the _obscurity_ (mean popularity rank) of the recommendations.

   Unlike other metrics, this metric requires access to the training dataset in
   order to compute item popularity metrics.  Supply this as a constructor
   parameter.

   This metric represents the popularity rank as a quantile, based on the
   either the number of distinct users who have interacted with the item, or
   the total interactions (depending on the options — distinct users is the
   default).

   Let $q_i$ be the _popularity rank_, represented as a quantile, of item $i$.
   $q_i = 1$ for the most-popular item; $q_i=0$ for an item with no users or
   interactions (the quantiles are min-max scaled). This metric computes the
   mean of the quantile popularity ranks for the recommended items:

   .. math::
       \mathcal{M}(L) = \frac{1}{|L|} \sum_{i \in L} q_i

   This metric is based on the ``obscurity'' metric of
   :cite:t:`ekstrandSturgeonCoolKids2017` and the popularity-based item novelty
   metric of :cite:t:`vargasRankRelevanceNovelty2011`.

   :Stability: Caller


   .. py:attribute:: item_ranks
      :type:  pandas.Series[float]


   .. 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