lenskit.sklearn.svd.BiasedSVDScorer
===================================

.. py:class:: lenskit.sklearn.svd.BiasedSVDScorer(config = None, **kwargs)
   

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


   Biased matrix factorization for explicit feedback using SciKit-Learn's
   :class:`~sklearn.decomposition.TruncatedSVD`.  It operates by first
   computing the bias, then computing the SVD of the bias residuals.

   You'll generally want one of the iterative SVD implementations such as
   :class:`lenskit.als.BiasedMFScorer`; this is here primarily as
   an example and for cases where you want to evaluate a pure SVD
   implementation.

   :Stability: Caller


   .. py:attribute:: config
      :type:  BiasedSVDConfig

      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:: bias
      :type:  lenskit.basic.BiasModel


   .. py:attribute:: factorization
      :type:  sklearn.decomposition.TruncatedSVD


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


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


   .. py:attribute:: user_components
      :type:  numpy.typing.NDArray[numpy.float64]


   .. py:method:: is_trained()

      Query if this component has already been trained.



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

      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.