lenskit.sklearn.nmf.NMFScorer
=============================

.. py:class:: lenskit.sklearn.nmf.NMFScorer(config = None, **kwargs)
   

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


   Non-negative matrix factorization for implicit feedback using SciKit-Learn's
   :func:`sklearn.decomposition.non_negative_factorization`. It computes the user
   and item embedding matrices using an indicator matrix as the input.

   :Stability: Caller


   .. py:attribute:: config
      :type:  NMFConfig

      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


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


   .. py:attribute:: user_components
      :type:  lenskit.data.types.NPMatrix


   .. py:attribute:: item_components
      :type:  lenskit.data.types.NPMatrix


   .. 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.