optimizer

Module Contents

default
class PipeOptimization

Provides a live interface of the current optimization

get_device(self)
Returns:InterfaceDevice (not process safe, deprecated)
get_historic(self)
Returns:OptiHistoric
set_device(self, theDevice)
set_historic(self, theHistoric)
class OptiHistoric(**kwargs)

Bases: object

Contains all the points that have been evaluated

class _pointData(currTime, objectives, constraints)
time :float
objectives :List[float]
constraints :List[float]
_DEVICE = autosaved
_LOGOPTI = logopti
_RESULTS = results
_CONVERGENCE = optiConvergence
add_point(self, device, currTime, objectives, constraints)
set_results(self, devicesList)
set_convergence(self, theConvergence)
set_info(self, theInfo)
save(self)
get_results(self)
get_convergence(self)
Returns:convergence InterfaceConvergence
get_devices(self)
Returns:List of devices (ordered by evaluation number)
get_logopti(self)
Returns:Log optimization (to check the convergence)
class Optimizer

Bases: optimeed.core.options.Option_class

Main optimizing class

DISPLAY_INFO = 1
KWARGS_OPTIHISTO = 2
set_optimizer(self, theDevice, theObjectiveList, theConstraintList, theOptimizationVariables, theOptimizationAlgorithm=default['Algo'], theCharacterization=default['Charac'], theMathsToPhysics=default['M2P'])

Prepare the optimizer for the optimization.

Parameters:
Returns:

PipeOptimization
run_optimization(self)

Perform the optimization.

Returns:Collection of the best optimized machines
set_max_opti_time(self, max_time_sec)
evaluateObjectiveAndConstraints(self, x)

Evaluates the performances of machine 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 actually 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. It is not (yet?) possible to access this motor on the main process before the end of the fork. This behaviour could be changed by using pipes or Managers.

Parameters:x – Input mathematical vector from optimization algorithm
Returns:dictionary, containing objective values (list of scalar), constraint values (list of scalar), and other info (motor, time)
callback_on_evaluation(self, returnedValues)

Save the output of evaluateObjectiveAndConstraints to optiHistoric. This function should be called by the optimizer IN a process safe context.

formatInfo(self)