lenskit.knn.user.UserKNNScorer
==============================

.. py:class:: lenskit.knn.user.UserKNNScorer(config = None, **kwargs)
   

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


   User-user nearest-neighbor collaborative filtering with ratings. This
   user-user implementation is not terribly configurable; it hard-codes design
   decisions found to work well in the previous Java-based LensKit code.

   .. note::

       This component must be used with queries containing the user's history,
       either directly in the input or by wiring its query input to the output of a
       user history component (e.g., :class:`~lenskit.basic.UserTrainingHistoryLookup`).

   :Stability: Caller


   .. py:attribute:: config
      :type:  UserKNNConfig

      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:: users
      :type:  lenskit.data.Vocabulary

      The index of user IDs.


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

      The index of item IDs.


   .. py:attribute:: user_means
      :type:  numpy.ndarray[tuple[int], numpy.dtype[numpy.float32]] | None

      Mean rating for each known user.


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

      Normalized rating matrix (CSR) to find neighbors at prediction time.


   .. py:attribute:: user_ratings
      :type:  lenskit.data.matrix.SparseRowArray

      Centered but un-normalized rating matrix (COO) to find neighbor ratings.


   .. py:method:: is_trained()

      Query if this component has already been trained.



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

      "Train" a user-user CF model.  This memorizes the rating data in a format that is usable
      for future computations.

      :param ratings: (user, item, rating) data for collaborative filtering.
      :type ratings: pandas.DataFrame



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

      Compute predictions for a user and items.

      :param user: the user ID
      :param items: the items to predict
      :type items: array-like
      :param ratings: the user's ratings (indexed by item id); if provided, will be used to
                      recompute the user's bias at prediction time.
      :type ratings: pandas.Series

      :returns: scores for the items, indexed by item id.
      :rtype: pandas.Series