lenskit.flexmf.FlexMFExplicitConfig
===================================

.. py:class:: lenskit.flexmf.FlexMFExplicitConfig
   :canonical: lenskit.flexmf._explicit.FlexMFExplicitConfig

   Bases: :py:obj:`lenskit.flexmf._base.FlexMFConfigBase`


   Configuration for :class:`FlexMFExplicitScorer`.  This class overrides
   certain base class defaults for better explicit-feedback performance.

   :Stability: Experimental


   .. py:attribute:: regularization
      :type:  float
      :value: 0.1


      The regularization strength.

      .. note::
          The explicit-feedback model uses a different default strength.


   .. py:attribute:: reg_method
      :type:  Literal['AdamW', 'L2'] | None
      :value: 'L2'


      The regularization method to use.

      With the default AdamW regularization, training will use the
      :class:`~torch.optim.AdamW` optimizer with weight decay. With L2
      regularization, training will use sparse gradients and the
      :class:`torch.optim.SparseAdam` optimizer.

      .. note::
          The explicit-feedback model defaults this setting to ``"L2"``.

      ``None``
          Use no regularization.

      ``"L2"``
          Use L2 regularization on the parameters used in each training batch. The
          strength is applied to the _mean_ norms in a batch, so that the
          regularization term scale is not dependent on the batch size.

      ``"AdamW"``
          Use :class:`torch.optim.AdamW` with the specified regularization
          strength.  This configuration does *not* use sparse gradients, but
          training time is often comparable.

      .. note::
          Regularization values do not necessarily have the same range or meaning
          for the different regularization methods.