pyepo.metric.metrics
====================

.. py:module:: pyepo.metric.metrics

.. autoapi-nested-parse::

   Metrics for SKlearn model



Functions
---------

.. autoapisummary::

   pyepo.metric.metrics.SPOError
   pyepo.metric.metrics.makeSkScorer
   pyepo.metric.metrics.makeAutoSkScorer
   pyepo.metric.metrics.testMSE
   pyepo.metric.metrics.makeTestMSEScorer


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

.. py:function:: SPOError(pred_cost, true_cost, model_type, args)

   A function to calculate normalized true regret

   :param pred_cost: predicted costs
   :type pred_cost: numpy.array
   :param true_cost: true costs
   :type true_cost: numpy.array
   :param model_type: optModel class type
   :type model_type: ABCMeta
   :param args: optModel args
   :type args: dict

   :returns: regret loss
   :rtype: float


.. py:function:: makeSkScorer(optmodel)

   A function to create sklearn scorer

   :param optmodel: optimization model
   :type optmodel: optModel

   :returns: callable object that returns a scalar score; less is better.
   :rtype: scorer


.. py:function:: makeAutoSkScorer(optmodel)

   A function to create Auto-SKlearn scorer

   :param optmodel: optimization model
   :type optmodel: optModel

   :returns: callable object that returns a scalar score; less is better.
   :rtype: scorer


.. py:function:: testMSE(pred_cost, true_cost, model_type, args)

   A function to calculate MSE for testing

   :param pred_cost: predicted costs
   :type pred_cost: array
   :param true_cost: true costs
   :type true_cost: array
   :param model_type: optModel class type
   :type model_type: ABCMeta
   :param args: optModel args
   :type args: dict

   :returns: mse
   :rtype: float


.. py:function:: makeTestMSEScorer(optmodel)

   A function to create MSE scorer for testing

   :param optmodel: optimization model
   :type optmodel: optModel

   :returns: callable object that returns a scalar score; less is better.
   :rtype: scorer