pyepo.func.jax.contrastive
==========================

.. py:module:: pyepo.func.jax.contrastive

.. autoapi-nested-parse::

   Noise contrastive estimation loss function



Attributes
----------

.. autoapisummary::

   pyepo.func.jax.contrastive.NCE
   pyepo.func.jax.contrastive.CMAP


Classes
-------

.. autoapisummary::

   pyepo.func.jax.contrastive.noiseContrastiveEstimation
   pyepo.func.jax.contrastive.contrastiveMAP


Module Contents
---------------

.. py:class:: noiseContrastiveEstimation(optmodel, processes=1, solve_ratio=1.0, reduction: pyepo.func.runtime.Reduction = 'mean', dataset=None)

   Bases: :py:obj:`pyepo.func.jax.abcmodule.optModule`


   Noise Contrastive Estimation (NCE) -- contrastive loss against a cached pool.

   Averages the predicted-cost margin between the true optimum and every pool
   member.

   Reference: Mulamba et al. (2021) `<https://www.ijcai.org/proceedings/2021/390>`_


   .. py:method:: forward(pred_cost, true_sol)

      Forward pass



.. py:class:: contrastiveMAP(optmodel, processes=1, solve_ratio=1.0, reduction: pyepo.func.runtime.Reduction = 'mean', dataset=None)

   Bases: :py:obj:`pyepo.func.jax.abcmodule.optModule`


   Contrastive Maximum-a-Posteriori (CMAP) -- max-margin special case of NCE.

   Keeps only the most-violating pool member (smallest predicted-cost
   objective) as the negative.

   Reference: Mulamba et al. (2021) `<https://www.ijcai.org/proceedings/2021/390>`_


   .. py:method:: forward(pred_cost, true_sol)

      Forward pass



.. py:data:: NCE

.. py:data:: CMAP