``optimizer``
=====================================

.. py:module:: optimeed.optimize.optimizer


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

Classes
~~~~~~~

.. autoapisummary::

   optimeed.optimize.optimizer.OptimizerSettings
   optimeed.optimize.optimizer._Evaluator
   optimeed.optimize.optimizer._ArrayEvaluator



Functions
~~~~~~~~~

.. autoapisummary::

   optimeed.optimize.optimizer.run_optimization



Attributes
~~~~~~~~~~

.. autoapisummary::

   optimeed.optimize.optimizer.default


.. py:data:: default

   

.. py:class:: OptimizerSettings(theDevice, theObjectives, theConstraints, theOptimizationVariables, theOptimizationAlgorithm=None, theMathsToPhysics=None, theCharacterization=None)


   Bases: :py:obj:`optimeed.core.SaveableObject`

   Abstract class for dynamically type-hinted objects.
   This class is to solve the special case where the exact type of an attribute is not known before runtime, yet has to be saved.

   .. py:method:: get_additional_attributes_to_save()

      Return list of attributes corresponding to object, whose type cannot be determined statically (e.g. topology change)


   .. py:method:: get_additional_attributes_to_save_list()

      Same behavior as get_additional_attributes_to_save, but where the attributes contains list of unknown items


   .. py:method:: get_device()


   .. py:method:: get_M2P()


   .. py:method:: get_charac()


   .. py:method:: get_optivariables()


   .. py:method:: get_objectives()


   .. py:method:: get_constraints()


   .. py:method:: get_optialgorithm()



.. py:class:: _Evaluator(optimization_parameters: OptimizerSettings)


   This is the main class that serves as evaluator. This class is NOT process safe (i.e., copy of it might be generated upon process call)

   .. py:method:: start()


   .. py:method:: evaluate(x)

      Evaluates the performances of device associated to entrance vector x. Outputs the objective function and the constraints,
      and other data used in optiHistoric.

      This function is NOT process safe: "self." is a FORK in multiprocessing algorithms.
      It means that the motor originally contained in self. is modified only in the fork, and only gathered by reaching the end of the fork.

      :param x: Input mathematical vector from optimization algorithm
      :return: dictionary, containing objective values (list of scalar), constraint values (list of scalar), and other info (motor, time)


   .. py:method:: reevaluate_solutions(x_solutions)



.. py:class:: _ArrayEvaluator(optimization_parameters: OptimizerSettings)


   Bases: :py:obj:`_Evaluator`

   Same as _evaluator, using array as inputs. Allows to evaluate all generation at once, in case of numpy array computations.
   Drawbacks: devices can not be visualized and saved.
   Use this with care.

   .. py:method:: evaluate(list_of_x)

      Evaluates the performances of device associated to entrance vector x. Outputs the objective function and the constraints,
      and other data used in optiHistoric.

      This function is NOT process safe: "self." is a FORK in multiprocessing algorithms.
      It means that the motor originally contained in self. is modified only in the fork, and only gathered by reaching the end of the fork.

      :param x: Input mathematical vector from optimization algorithm
      :return: dictionary, containing objective values (list of scalar), constraint values (list of scalar), and other info (motor, time)


   .. py:method:: reevaluate_solutions(x_solutions)



.. py:function:: run_optimization(optimizer_settings: OptimizerSettings, opti_historic, max_opti_time_sec=10, return_x_solutions=False, array_evaluator=False, initialVectorGuess=None)

   Perform the optimization.

   :param optimizer_settings: :class:`OptimizerSettings` containing all information in models, objectives and optimization variable
   :param opti_historic: OptiHistoric to log evaluations and results
   :param max_opti_time_sec: Maximum optimization time (default stopping criterion, unless modified in algorithm)
   :param return_x_solutions: If True, returns raw parameters in reults
   :param array_evaluator: If True, evaluate each generation at once using numpy array. Use it only with care, as it dismisses some features (expert mode)
   :return: list of the best optimized devices, convergence information and [if return_x_solutions=True] best solutions