def configure(self):
driver = self.add('driver', FixedPointIterator())
adapt = self.add('adapt', AdaptiveSampleDriver())
ei_opt = self.add('ei_opt', Genetic())
branin = self.add('branin', BraninComponent())
kwargs = {'params': ("x", "y"), 'responses': ('f_xy', )}
meta = self.add('meta', MetaModel(**kwargs))
meta.default_surrogate = KrigingSurrogate()
pareto = self.add('pareto', ParetoFilter(**kwargs))
ei = self.add('ei', ExpectedImprovement())
#initial training DOE
adapt.DOEgenerator = OptLatinHypercube(num_samples=15)
#adapt.DOEgenerator = Uniform(100)
adapt.add_parameter('branin.x', low=-5, high=10)
adapt.add_parameter('branin.y', low=0, high=15)
adapt.add_response('branin.f_xy')
#pass training data from sampler to metamodel and pareto filter
self.connect('adapt.all_case_inputs.branin.x',
['meta.params.x', 'pareto.params.x'])
self.connect('adapt.all_case_inputs.branin.y',
['meta.params.y', 'pareto.params.y'])
self.connect('adapt.all_case_outputs.branin.f_xy',
['meta.responses.f_xy', 'pareto.responses.f_xy'])
#connect meta and pareto to ei
self.connect('meta.f_xy',
'ei.current') #this passes a normal distribution variable
self.connect(
'pareto.pareto_outputs[0, 0]', 'ei.target'
) #for single objective, frontier is just a scalar value that is the minimum of the set
#EI optimization to find next point
ei_opt.opt_type = "maximize"
ei_opt.population_size = 100
ei_opt.generations = 10
ei_opt.add_parameter('meta.x', low=-5, high=10)
ei_opt.add_parameter('meta.y', low=0, high=15)
ei_opt.add_objective('ei.PI') #could use ei.EI too
#Iterative sampling process
driver.add_parameter('adapt.adaptive_inputs.branin.x[0]')
driver.add_parameter('adapt.adaptive_inputs.branin.y[0]')
driver.add_constraint('adapt.adaptive_inputs.branin.x[0] = meta.x')
driver.add_constraint('adapt.adaptive_inputs.branin.y[0] = meta.y')
driver.max_iterations = 30
#Iteration Heirarchy
driver.workflow.add(['adapt', 'pareto', 'ei_opt'])
adapt.workflow.add(['branin'])
ei_opt.workflow.add(['meta', 'ei'])
#FPI now support stop conditions
driver.add_stop_condition('ei.EI <= .0001')
def configure(self):
driver = self.add('driver', FixedPointIterator())
adapt = self.add('adapt', AdaptiveSampleDriver())
MOEI_opt = self.add('MOEI_opt', Genetic())
self.add('spiral', SpiralComponent())
kwargs = {'params': ("x", "y"), 'responses': ('f1_xy', 'f2_xy')}
meta = self.add('meta', MetaModel(**kwargs))
meta.default_surrogate = KrigingSurrogate()
self.add('pareto', ParetoFilter(**kwargs))
self.add('MOEI', MultiObjExpectedImprovement())
# initial training DOE
adapt.DOEgenerator = OptLatinHypercube(num_samples=25)
adapt.add_parameter('spiral.x')
adapt.add_parameter('spiral.y')
adapt.add_response('spiral.f1_xy')
adapt.add_response('spiral.f2_xy')
# pass training data from sampler to metamodel and pareto filter
self.connect('adapt.all_case_inputs.spiral.x',
['meta.params.x', 'pareto.params.x'])
self.connect('adapt.all_case_inputs.spiral.y',
['meta.params.y', 'pareto.params.y'])
self.connect('adapt.all_case_outputs.spiral.f1_xy',
['meta.responses.f1_xy', 'pareto.responses.f1_xy'])
self.connect('adapt.all_case_outputs.spiral.f2_xy',
['meta.responses.f2_xy', 'pareto.responses.f2_xy'])
# connect meta and pareto to ei
self.connect('[meta.f1_xy, meta.f2_xy]', 'MOEI.current')
self.connect('pareto.pareto_outputs', 'MOEI.target')
# MOEI optimization to find next point
MOEI_opt.opt_type = "maximize"
MOEI_opt.population_size = 100
MOEI_opt.generations = 10
# MOEI_opt.selection_method = "tournament"
MOEI_opt.add_parameter("meta.x", low=0.75, high=5. * pi)
MOEI_opt.add_parameter("meta.y", low=0.75, high=5. * pi)
MOEI_opt.add_objective("MOEI.PI")
# Iterative sampling process
driver.add_parameter('adapt.adaptive_inputs.spiral.x[0]')
driver.add_parameter('adapt.adaptive_inputs.spiral.y[0]')
driver.add_constraint('adapt.adaptive_inputs.spiral.x[0] = meta.x')
driver.add_constraint('adapt.adaptive_inputs.spiral.y[0] = meta.y')
driver.max_iterations = 30
# Iteration Heirarchy
driver.workflow.add(['adapt', 'pareto', 'MOEI_opt'])
adapt.workflow.add(['spiral'])
MOEI_opt.workflow.add(['meta', 'MOEI'])
# FPI now support stop conditions
driver.add_stop_condition('MOEI.PI <= .0001')
def configure(self):
self.add('driver', IterateUntil())
self.add('adaptive', AdaptiveSampleDriver())
self.add('driven', DrivenComponent())
self.driver.workflow.add('adaptive')
self.adaptive.workflow.add('driven')
self.adaptive.DOEgenerator = FullFactorial()
self.adaptive.DOEgenerator.num_levels = 2
self.adaptive.add_parameter('driven.x', low=-10., high=10.)
self.adaptive.add_response('driven.y')
self.adaptive.record_doe = False