lenskit.metrics.ranking.RankBiasedEntropy
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.. py:class:: lenskit.metrics.ranking.RankBiasedEntropy(dataset, attribute, n = None, *, weight = None)
   :canonical: lenskit.metrics.ranking._entropy.RankBiasedEntropy

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


   Evaluate diversity using rank-biased Shannon entropy over item categories.

   This metric measures the diversity of categories in recommendation list
   with rank-based weighting, giving more importance to items at the top
   of the recommendation list.

   :param dataset: The LensKit dataset containing item entities and their attributes.
   :param attribute: Name of the attribute to use for categories (e.g., 'genre', 'tag')
   :param n: Recommendation list length to evaluate
   :param weight: Rank weighting model. Defaults to GeometricRankWeight(0.85)

   :Stability: Caller


   .. py:attribute:: attribute
      :type:  str


   .. py:attribute:: weight
      :type:  lenskit.metrics.ranking._weighting.RankWeight


   .. 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