lenskit.metrics.ranking.LogRankWeight
=====================================

.. py:class:: lenskit.metrics.ranking.LogRankWeight(*, base = 2, offset = 0)
   :canonical: lenskit.metrics.ranking._weighting.LogRankWeight

   Bases: :py:obj:`RankWeight`


   Logarithmic weighting for result ranks, as used in NDCG.

   This is the ranking model typically used for DCG and NDCG.

   Since :math:`\operatorname{lg} 1 = 0`, simply taking the log will result in
   division by 0 when weights are applied.  The correction for this in the
   original NDCG paper :cite:p:`ndcg` is to clip the ranks, so that both of the
   first two positions have discount :math:`\operatorname{lg} 2`.  A different
   correction somtimes seen is to compute :math:`\operatorname{lg} (k+1)`. This
   discount supports both; the default is to clip, but if the ``offset`` option
   is set to a positive number, it is added to the ranks instead.

   :param base: The log base to use.
   :param offset: An offset to add to ranks before computing logs.


   .. py:attribute:: base
      :type:  float


   .. py:attribute:: offset
      :type:  int


   .. py:method:: weight(ranks)

      Compute the discount for the specified ranks.

      Ranks must start with 1.