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


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

.. py:function:: SPOError(pred_cost: numpy.ndarray, true_cost: numpy.ndarray, optmodel: pyepo.model.opt.optModel) -> float

   Normalized true regret of predicted costs over a dataset.

   Solves each instance at the predicted and the true cost and returns
   :math:`\sum_i l_i / \sum_i |z^*(\mathbf{c}_i)|`; instances with
   near-zero true optima inflate the ratio.

   :param pred_cost: predicted costs of shape (num_data, num_cost)
   :param true_cost: true costs of shape (num_data, num_cost)
   :param optmodel: a PyEPO optimization model

   :returns: normalized regret
   :rtype: float


.. py:function:: makeSkScorer(optmodel: pyepo.model.opt.optModel) -> Callable

   A function to create sklearn scorer

   :param optmodel: optimization model

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


.. py:function:: makeAutoSkScorer(optmodel: pyepo.model.opt.optModel) -> Callable

   A function to create Auto-SKlearn scorer

   :param optmodel: optimization model

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