lenskit.knn.slim.SLIMScorer
===========================

.. py:class:: lenskit.knn.slim.SLIMScorer(config = None, **kwargs)
   

   Bases: :py:obj:`lenskit.pipeline.components.Component`, :py:obj:`lenskit.training.Trainable`


   Item scorer using Sparse LInear Methods (SLIM).  SLIM was described for
   recommendation by :cite:t`ningSLIMSparseLinear2011`.  This scorer supports
   both SLIM and fsSLIM variants (by setting :attr:`SLIMConfig.max_nbrs`).

   SLIM operates by learning an elastic-net regression for each item,
   predicting whether the user will interact with that item based on the other
   items they have interacted with.

   This implementation closely follows the paper with some reference to
   `libslim`_ for computational details.  It uses coodrinate descent with soft
   thresholding :cite:p:`friedmanRegularizationPathsGeneralized2010` to
   estimate the SLIM weight matrix.

   .. note::

       The coordinate descent method of
       :cite:t:`friedmanRegularizationPathsGeneralized2010` operates on
       standardized predictor variables, with a hint that standardization can
       be inlined to the optimization logic for sparse problems.  However, they
       do not provide details for how to do that inlining, and the original
       SLIM implementation from the Karypis lab does not appear to center the
       predictors.  Therefore, this implementation also does not center them.

   .. _libslim: https://github.com/KarypisLab/SLIM/tree/master/src/libslim


   .. py:attribute:: config
      :type:  SLIMConfig

      The component configuration object.  Component classes that support
      configuration **must** redefine this attribute with their specific
      configuration class type, which can be a Python dataclass or a Pydantic
      model class.


   .. py:attribute:: weights
      :type:  scipy.sparse.csr_array


   .. py:attribute:: items
      :type:  lenskit.data.Vocabulary


   .. py:method:: is_trained()

      Query if this component has already been trained.



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

      Train the model to learn its parameters from a training dataset.

      :param data: The training dataset.
      :param options: The training options.



   .. py:method:: __call__(query, items)

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