lenskit.reranking.fair.FAIRReranker
===================================

.. py:class:: lenskit.reranking.fair.FAIRReranker(config = None, **kwargs)
   

   Bases: :py:obj:`lenskit.pipeline.Component`\ [\ :py:obj:`lenskit.data.ItemList`\ ], :py:obj:`lenskit.training.Trainable`


   FA*IR top-N re-ranking algorithm.

   Re-ranks a sorted candidate list to satisfy binomial lower-bound constraints
   for a binary protected/unprotected attribute at every prefix (1..N). It expects
   a sorted list of items, the target proportion of protected items ``p`` and
   significance level ``alpha``.

   For details, see `Zehlike et al. (2017)`_.

   .. _Zehlike et al. (2017): https://doi.org/10.1145/3132847.3132938

   :Stability: Caller


   .. py:attribute:: config
      :type:  FAIRRerankerConfig

      FA*IR reranker configuration.


   .. py:attribute:: alpha_c
      :type:  float


   .. py:attribute:: m_list
      :type:  list[int]


   .. py:method:: is_trained()

      Query if this component has already been trained.



   .. py:method:: train(data, options = TrainingOptions())

      Precompute adjusted alpha and m-table for the configured (n,p,alpha)

      Training computes the (multiple-tests–corrected) prefix requirements m[i]
      so that every prefix of size i has at least m[i] protected items with
      significance α (after correction).

      :param protected attributes: The item entity table must have a scalar attribute ``protected`` that is ``True``
                                   if the item is in the protected group and ``False`` otherwise.



   .. py:method:: __call__(items, n = None)

      Run the pipeline's operation and produce a result.  This is the key
      method for components to implement.