lenskit.hpf.HPFScorer
=====================

.. py:class:: lenskit.hpf.HPFScorer(config = None, **kwargs)
   

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


   Hierarchical Poisson factorization, provided by `hpfrec`_

   .. _hpfrec: https://hpfrec.readthedocs.io/en/latest/

   .. todo::
       Right now, this uses the 'rating' as a count. Actually use counts (:issue:`656`).

   :Stability: Experimental

   :param features: the number of features
   :param kwargs: additional arguments to pass to :class:`hpfrec.HPF`.


   .. py:attribute:: config
      :type:  HPFConfig

      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:: user_features
      :type:  numpy.ndarray[tuple[int, int], numpy.dtype[numpy.float64]]


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


   .. py:attribute:: item_features
      :type:  numpy.ndarray[tuple[int, int], numpy.dtype[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.