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

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

.. autoapi-nested-parse::

   Noise contrastive estimation loss function



Classes
-------

.. autoapisummary::

   pyepo.func.contrastive.NCE
   pyepo.func.contrastive.contrastiveMAP


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

.. py:class:: NCE(optmodel, processes=1, solve_ratio=1, reduction='mean', dataset=None)

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


   An autograd module for noise contrastive estimation as surrogate loss
   functions, based on viewing suboptimal solutions as negative examples.

   For the NCE, the cost vector needs to be predicted from contextual data and
   maximizes the separation of the probability of the optimal solution.

   Thus allows us to design an algorithm based on stochastic gradient descent.

   Reference: <https://www.ijcai.org/proceedings/2021/390>


   .. py:attribute:: solpool


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

      Forward pass



.. py:class:: contrastiveMAP(optmodel, processes=1, solve_ratio=1, reduction='mean', dataset=None)

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


   An autograd module for Maximum A Posterior contrastive estimation as
   surrogate loss functions, which is an efficient self-contrastive algorithm.

   For the MAP, the cost vector needs to be predicted from contextual data and
   maximizes the separation of the probability of the optimal solution.

   Thus, it allows us to design an algorithm based on stochastic gradient descent.

   Reference: <https://www.ijcai.org/proceedings/2021/390>


   .. py:attribute:: solpool


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

      Forward pass