def configure(self):
# Components
self.add("sin_meta_model", MetaModel())
self.sin_meta_model.model = Sin()
self.sin_meta_model.default_surrogate = FloatKrigingSurrogate()
# Training the MetaModel
self.add("DOE_Trainer", DOEdriver())
self.DOE_Trainer.DOEgenerator = FullFactorial()
self.DOE_Trainer.DOEgenerator.num_levels = 25
self.DOE_Trainer.add_parameter("sin_meta_model.x", low=0, high=20)
self.DOE_Trainer.case_outputs = ["sin_meta_model.f_x"]
self.DOE_Trainer.add_event("sin_meta_model.train_next")
self.DOE_Trainer.recorders = [DBCaseRecorder()]
# MetaModel Validation
self.add("sin_calc", Sin())
self.add("DOE_Validate", DOEdriver())
self.DOE_Validate.DOEgenerator = Uniform()
self.DOE_Validate.DOEgenerator.num_samples = 200
self.DOE_Validate.add_parameter(("sin_meta_model.x", "sin_calc.x"), low=0, high=20)
self.DOE_Validate.case_outputs = ["sin_calc.f_x", "sin_meta_model.f_x"]
self.DOE_Validate.recorders = [DBCaseRecorder()]
# Iteration Hierarchy
self.driver.workflow = SequentialWorkflow()
self.driver.workflow.add(['DOE_Trainer', 'DOE_Validate'])
self.DOE_Trainer.workflow.add('sin_meta_model')
self.DOE_Validate.workflow.add('sin_meta_model')
self.DOE_Validate.workflow.add('sin_calc')
def __init__(self):
super(Simulation,self).__init__()
#Components
self.add("sin_meta_model",MetaModel())
self.sin_meta_model.surrogate = {"default":KrigingSurrogate()}
self.sin_meta_model.model = Sin()
self.sin_meta_model.recorder = DBCaseRecorder()
#Training the MetaModel
self.add("DOE_Trainer",DOEdriver())
self.DOE_Trainer.DOEgenerator = FullFactorial()
self.DOE_Trainer.DOEgenerator.num_levels = 25
self.DOE_Trainer.add_parameter("sin_meta_model.x",low=0,high=20)
self.DOE_Trainer.case_outputs = ["sin_meta_model.f_x"]
self.DOE_Trainer.add_event("sin_meta_model.train_next")
self.DOE_Trainer.recorders = [DBCaseRecorder()]
self.DOE_Trainer.force_execute = True
#MetaModel Validation
self.add("sin_calc",Sin())
self.add("DOE_Validate",DOEdriver())
self.DOE_Validate.DOEgenerator = Uniform()
self.DOE_Validate.DOEgenerator.num_samples = 100
self.DOE_Validate.add_parameter(("sin_meta_model.x","sin_calc.x"),low=0,high=20)
self.DOE_Validate.case_outputs = ["sin_calc.f_x","sin_meta_model.f_x"]
self.DOE_Validate.recorders = [DBCaseRecorder()]
self.DOE_Validate.force_execute = True
#Iteration Hierarchy
self.driver.workflow = SequentialWorkflow()
self.driver.workflow.add(['DOE_Trainer','DOE_Validate'])
self.DOE_Trainer.workflow.add('sin_meta_model')
self.DOE_Validate.workflow.add('sin_meta_model')
self.DOE_Validate.workflow.add('sin_calc')
def configure(self):
super(hyperTop, self).configure()
#--------------------------Component Setup-----------------------------------
self.add('NPSS', DesignCase()) #imported from npssCases.py
#---------------------------DOE Setup----------------------------------------
self.add('driver', DOEdriver())
self.driver.add_parameter('NPSS.tube_Pt', low=99.0 / 0.5282,
high=300.) #low=1760., high=1960.)
self.driver.add_parameter('NPSS.capsule_MN', low=0.6,
high=1.) #low=1760., high=1960.)
self.driver.add_parameter('NPSS.bearing_Pt', low=11000.,
high=12000.) #low=1760., high=1960.)
self.driver.DOEgenerator = FullFactorial(num_levels=1)
#------------------------Case Recorders Setup--------------------------------
#outfile = open('test_jeff.txt','w')
#self.driver.recorders = [CSVCaseRecorder(filename='DOEoutdata.csv'), DumpCaseRecorder(outfile)]
##other options DBCase,ListCaseRecorder
#self.driver.printvars = ['designInputs.*']
#self.driver.recorders[0].num_backups = 0 #save backup copies of previous runs (dated)
#-------------------------- Assembly Setup ----------------------------------
# Assembly Workflow
self.driver.workflow.add(['NPSS']) #
def configure(self):
self.add('paraboloid', Paraboloid())
self.add('driver', DOEdriver())
self.driver.log_level = logging.INFO
# Configure the generator.
self.driver.DOEgenerator = MonteCarlo()
self.driver.DOEgenerator.num_samples = 5000
self.driver.DOEgenerator.dist_types = {
'Default':random.uniform, 'y':random.standard_normal}
self.driver.DOEgenerator.dist_args = {'Default':[0, 1], 'y':[]}
self.driver.DOEgenerator.parameters = ['x', 'y']
# Configure driver parameters.
self.driver.add_parameter('paraboloid.x', low=0, high=1)
self.driver.add_parameter('paraboloid.y', low=0, high=1)
#tell the DOEdriver to also record f_xy.
self.driver.add_response('paraboloid.f_xy')
#Simple recorder which stores the cases in memory.
self.recorders = [ListCaseRecorder(),]
self.driver.workflow.add('paraboloid')
def configure(self):
self.add('paraboloid', Paraboloid())
self.add('driver', DOEdriver())
#There are a number of different kinds of DOE available in openmdao.lib.doegenerators
self.driver.DOEgenerator = MonteCarlo()
self.driver.DOEgenerator.num_samples = 5000
self.driver.DOEgenerator.dist_types = {
'Default': random.uniform,
'y': random.standard_normal
}
self.driver.DOEgenerator.dist_args = {'Default': [0, 1], 'y': []}
self.driver.DOEgenerator.parameters = ['x', 'y']
#DOEdriver will automatically record the values of any parameters for each case
self.driver.add_parameter('paraboloid.x', low=0, high=1)
self.driver.add_parameter('paraboloid.y', low=0, high=1)
#tell the DOEdriver to also record any other variables you want to know for each case
self.driver.case_outputs = [
'paraboloid.f_xy',
]
#Simple recorder which stores the cases in memory.
self.driver.recorders = [
ListCaseRecorder(),
]
self.driver.workflow.add('paraboloid')
def __init__(self):
super(Analysis, self).__init__()
# --------------------------------------------------------------------------- #
# --- Instantiate LHC DOE Driver
# --------------------------------------------------------------------------- #
self.add('doe_driver', DOEdriver())
self.doe_driver.DOEgenerator = Uniform(num_samples = 5)
self.doe_driver.workflow = SequentialWorkflow()
# --------------------------------------------------------------------------- #
# --- Instantiate Geometry Component
# --------------------------------------------------------------------------- #
self.add('geometry', GeometryComp())
# 1--- Top Level Workflow
self.driver.workflow.add(['doe_driver'])
self.doe_driver.workflow.add(['geometry'])
# --------------------------------------------------------------------------- #
# --- Add parameters to DOE driver
# --------------------------------------------------------------------------- #
self.doe_driver.add_parameter('geometry.dC1P_X')
self.doe_driver.add_parameter('geometry.dC2P_X')
self.doe_driver.add_parameter('geometry.dC3P_X')
self.doe_driver.add_parameter('geometry.dC4P_X')
self.doe_driver.add_parameter('geometry.dC1P_R')
self.doe_driver.add_parameter('geometry.dC2P_R')
self.doe_driver.add_parameter('geometry.dC3P_R')
self.doe_driver.add_parameter('geometry.dC4P_R')
self.doe_driver.add_parameter('geometry.dC1S_X')
self.doe_driver.add_parameter('geometry.dC2S_X')
self.doe_driver.add_parameter('geometry.dC3S_X')
self.doe_driver.add_parameter('geometry.dC4S_X')
self.doe_driver.add_parameter('geometry.dC1S_R')
self.doe_driver.add_parameter('geometry.dC2S_R')
self.doe_driver.add_parameter('geometry.dC3S_R')
self.doe_driver.add_parameter('geometry.dC4S_R')
self.doe_driver.add_parameter('geometry.dC1S_T')
self.doe_driver.add_parameter('geometry.dC2S_T')
self.doe_driver.add_parameter('geometry.dC3S_T')
self.doe_driver.add_parameter('geometry.dC1C_X')
self.doe_driver.add_parameter('geometry.dC2C_X')
self.doe_driver.add_parameter('geometry.dC3C_X')
self.doe_driver.add_parameter('geometry.dC4C_X')
self.doe_driver.add_parameter('geometry.dC1C_R')
self.doe_driver.add_parameter('geometry.dC2C_R')
self.doe_driver.add_parameter('geometry.dC3C_R')
self.doe_driver.add_parameter('geometry.dC4C_R')
self.doe_driver.add_parameter('geometry.dC1C_T')
self.doe_driver.add_parameter('geometry.dC2C_T')
self.doe_driver.add_parameter('geometry.dC3C_T')
def configure(self):
self.add('paraboloid', Paraboloid())
doe = self.add('driver', DOEdriver())
doe.DOEgenerator = Uniform(num_samples=1000)
doe.add_parameter('paraboloid.x', low=-50, high=50)
doe.add_parameter('paraboloid.y', low=-50, high=50)
doe.case_outputs = ['paraboloid.f_xy']
doe.workflow.add('paraboloid')
def configure(self):
# Our component to be meta-modeled
self.add("trig_calc", Trig())
# Create meta_model for two responsese
self.add("trig_meta_model",
MetaModel(params=('x', ), responses=('f_x_sin', 'f_x_cos')))
# Use Kriging for the f_x output
self.trig_meta_model.surrogates['f_x_sin'] = LogisticRegression()
self.trig_meta_model.surrogates['f_x_cos'] = FloatKrigingSurrogate()
# Training the MetaModel
self.add("DOE_Trainer", DOEdriver())
self.DOE_Trainer.DOEgenerator = FullFactorial()
self.DOE_Trainer.DOEgenerator.num_levels = 20
self.DOE_Trainer.add_parameter("trig_calc.x", low=0, high=20)
self.DOE_Trainer.add_response('trig_calc.f_x_sin')
self.DOE_Trainer.add_response('trig_calc.f_x_cos')
# Pass training data to the meta model.
self.connect('DOE_Trainer.case_inputs.trig_calc.x',
'trig_meta_model.params.x')
self.connect('DOE_Trainer.case_outputs.trig_calc.f_x_sin',
'trig_meta_model.responses.f_x_sin')
self.connect('DOE_Trainer.case_outputs.trig_calc.f_x_cos',
'trig_meta_model.responses.f_x_cos')
#MetaModel Validation
self.add("DOE_Validate", DOEdriver())
self.DOE_Validate.DOEgenerator = Uniform()
self.DOE_Validate.DOEgenerator.num_samples = 20
self.DOE_Validate.add_parameter(("trig_meta_model.x", "trig_calc.x"),
low=0,
high=20)
self.DOE_Validate.add_response("trig_calc.f_x_sin")
self.DOE_Validate.add_response("trig_calc.f_x_cos")
self.DOE_Validate.add_response("trig_meta_model.f_x_sin")
self.DOE_Validate.add_response("trig_meta_model.f_x_cos")
#Iteration Hierarchy
self.driver.workflow.add(['DOE_Trainer', 'DOE_Validate'])
self.DOE_Trainer.workflow.add('trig_calc')
self.DOE_Validate.workflow.add(['trig_calc', 'trig_meta_model'])
def configure(self):
# Our component to be meta-modeled
self.add("sin_calc", Sin())
# Another instance of our component for head-to-head comparison with
# the metamodel.
self.add("sin_verify", Sin())
# Create meta_model for f_x as the response
self.add("sin_meta_model",
MetaModel(params=('x', ), responses=('f_x', )))
# Use Kriging for the f_x output
self.sin_meta_model.default_surrogate = FloatKrigingSurrogate()
# Training the MetaModel
self.add("DOE_Trainer", DOEdriver())
self.DOE_Trainer.DOEgenerator = FullFactorial()
self.DOE_Trainer.DOEgenerator.num_levels = 25
self.DOE_Trainer.add_parameter("sin_calc.x", low=0, high=20)
self.DOE_Trainer.add_response('sin_calc.f_x')
# Pass training data to the meta model.
self.connect('DOE_Trainer.case_inputs.sin_calc.x',
'sin_meta_model.params.x')
self.connect('DOE_Trainer.case_outputs.sin_calc.f_x',
'sin_meta_model.responses.f_x')
# Cross-validate the metamodel using random data
self.add("DOE_Validate", DOEdriver())
self.DOE_Validate.DOEgenerator = Uniform()
self.DOE_Validate.DOEgenerator.num_samples = 100
self.DOE_Validate.add_parameter(("sin_meta_model.x", "sin_verify.x"),
low=0,
high=20) # , name="combined_input"
self.DOE_Validate.add_response("sin_verify.f_x")
self.DOE_Validate.add_response("sin_meta_model.f_x")
#Iteration Hierarchy
self.driver.workflow.add(['DOE_Trainer', 'DOE_Validate'])
self.DOE_Trainer.workflow.add('sin_calc')
self.DOE_Validate.workflow.add(['sin_verify', 'sin_meta_model'])
def configure(self):
#Components
self.add("trig_meta_model", MetaModel())
self.trig_meta_model.surrogate = {
"f_x_sin": LogisticRegression(),
"f_x_cos": KrigingSurrogate()
}
self.trig_meta_model.model = Trig()
self.trig_meta_model.recorder = DBCaseRecorder()
#Training the MetaModel
self.add("DOE_Trainer", DOEdriver())
self.DOE_Trainer.DOEgenerator = FullFactorial()
self.DOE_Trainer.DOEgenerator.num_levels = 20
self.DOE_Trainer.add_parameter("trig_meta_model.x", low=0, high=20)
self.DOE_Trainer.case_outputs = [
"trig_meta_model.f_x_sin", "trig_meta_model.f_x_cos"
]
self.DOE_Trainer.add_event("trig_meta_model.train_next")
self.DOE_Trainer.recorders = [DBCaseRecorder()]
#MetaModel Validation
self.add("trig_calc", Trig())
self.add("DOE_Validate", DOEdriver())
self.DOE_Validate.DOEgenerator = Uniform()
self.DOE_Validate.DOEgenerator.num_samples = 20
self.DOE_Validate.add_parameter(("trig_meta_model.x", "trig_calc.x"),
low=0,
high=20)
self.DOE_Validate.case_outputs = [
"trig_calc.f_x_sin", "trig_calc.f_x_cos",
"trig_meta_model.f_x_sin", "trig_meta_model.f_x_cos"
]
self.DOE_Validate.recorders = [DBCaseRecorder()]
#Iteration Hierarchy
self.driver.workflow = SequentialWorkflow()
self.driver.workflow.add(['DOE_Trainer', 'DOE_Validate'])
self.DOE_Trainer.workflow.add('trig_meta_model')
self.DOE_Validate.workflow.add('trig_meta_model')
self.DOE_Validate.workflow.add('trig_calc')
def configure(self):
self.add('paraboloid', Paraboloid())
self.add('driver', DOEdriver())
self.driver.DOEgenerator = Uniform(1000)
self.driver.add_parameter('paraboloid.x', low=-50, high=50)
self.driver.add_parameter('paraboloid.y', low=-50, high=50)
self.driver.add_response('paraboloid.f_xy')
self.recorders = [JSONCaseRecorder(out='doe.json')]
def __init__(self):
super(Analysis, self).__init__()
# --------------------------------------------------------------------------- #
# --- Instantiate LHC DOE Driver
# --------------------------------------------------------------------------- #
self.add('doe_driver', DOEdriver())
self.doe_driver.DOEgenerator = OptLatinHypercube(num_samples=10)
self.doe_driver.workflow = SequentialWorkflow()
# --------------------------------------------------------------------------- #
# --- Instantiate LHC Adapter Component
# --- Modifies LHC to convert continuous design variable to discrete
# --------------------------------------------------------------------------- #
self.add('adapter', AdapterComp())
# --------------------------------------------------------------------------- #
# --- Instantiate Geometry Component
# --------------------------------------------------------------------------- #
self.add('geometry', GeometryComp())
# 1--- Top Level Workflow
self.driver.workflow.add(['doe_driver'])
self.doe_driver.workflow.add(['adapter', 'geometry'])
# --------------------------------------------------------------------------- #
# --- Add parameters to DOE driver
# --------------------------------------------------------------------------- #
self.doe_driver.add_parameter('adapter.vane_num')
self.doe_driver.add_parameter('adapter.injector_loc')
self.doe_driver.add_parameter('adapter.injector_dia')
self.doe_driver.add_parameter('geometry.vane_pitch')
self.doe_driver.add_parameter('geometry.venturi_angle')
# --------------------------------------------------------------------------- #
# Specify DBcaseRecorder for DOE
# --------------------------------------------------------------------------- #
self.doe_driver.case_outputs = [
'adapter.vane_num_out', 'adapter.injector_loc_out',
'adapter.injector_dia_out', 'geometry.vane_num',
'geometry.injector_loc', 'geometry.injector_dia'
]
self.doe_driver.recorder = DBCaseRecorder(DOE_OUT_DB)
#self.add('DOE_restart', DBCaseIterator(DOE_OUT_DB))
# --------------------------------------------------------------------------- #
# --- Specify Non-NPSS Data Connections
# --------------------------------------------------------------------------- #
self.connect('adapter.vane_num_out', 'geometry.vane_num')
self.connect('adapter.injector_loc_out', 'geometry.injector_loc')
self.connect('adapter.injector_dia_out', 'geometry.injector_dia')
def __init__(self):
super(Simulation, self).__init__()
#Components
self.add("sin_calc", Sin())
self.add("sin_verify", Sin())
self.add("sin_meta_model", MetaModel(params=('x',),
responses=('f_x',)))
self.sin_meta_model.default_surrogate = NeuralNet(n_hidden_nodes=5)
#Training the MetaModel
self.add("DOE_Trainer", DOEdriver())
self.DOE_Trainer.DOEgenerator = FullFactorial()
# Seems to need a lot of training data for decent prediction of sin(x),
# at least with default 'cg' method.
self.DOE_Trainer.DOEgenerator.num_levels = 2500
self.DOE_Trainer.add_parameter("sin_calc.x", low=0, high=20)
self.DOE_Trainer.add_response("sin_calc.f_x")
self.connect('DOE_Trainer.case_inputs.sin_calc.x',
'sin_meta_model.params.x')
self.connect('DOE_Trainer.case_outputs.sin_calc.f_x',
'sin_meta_model.responses.f_x')
#MetaModel Validation
self.add("DOE_Validate", DOEdriver())
self.DOE_Validate.DOEgenerator = Uniform()
self.DOE_Validate.DOEgenerator.num_samples = 100
self.DOE_Validate.add_parameter(("sin_meta_model.x", "sin_verify.x"),
low=0, high=20)
self.DOE_Validate.add_response("sin_verify.f_x")
self.DOE_Validate.add_response("sin_meta_model.f_x")
#Iteration Hierarchy
self.driver.workflow.add(['DOE_Trainer', 'DOE_Validate'])
self.DOE_Trainer.workflow.add('sin_calc')
self.DOE_Validate.workflow.add(('sin_verify', 'sin_meta_model'))
def configure(self):
self.add("driver", DOEdriver())
self.recorders = [ListCaseRecorder()]
self.driver.DOEgenerator = FullFactorial()
# configure the specific DOE options
self.driver.DOEgenerator.num_levels = 3
self.add("dis1", sellar.Discipline1())
self.add("dis2", sellar.Discipline2())
# setting some variables to fixed values
self.dis1.y2 = 3.15
self.dis2.y1 = 3.78
# adding three parameters to the DOEDriver
self.driver.add_parameter(("dis1.z1", "dis2.z1"), low=-10.0, high=10.0)
self.driver.add_parameter(("dis1.z2", "dis2.z2"), low=0.0, high=10.0)
self.driver.add_parameter("dis1.x1", low=0.0, high=10.0)
def configure(self):
self.add('paraboloid', Paraboloid())
self.add('driver', DOEdriver())
# There are a number of different kinds of DOE available in openmdao.lib.doegenerators
# self.driver.DOEgenerator = FullFactorial(10) #Full Factorial DOE with 10 levels for each variable
self.driver.DOEgenerator = Uniform(1000)
# DOEdriver will automatically record the values of any parameters for each case
self.driver.add_parameter('paraboloid.x', low=-50, high=50)
self.driver.add_parameter('paraboloid.y', low=-50, high=50)
# tell the DOEdriver to also record any other variables you want to know for each case
self.driver.add_response('paraboloid.f_xy')
self.recorders = [
JSONCaseRecorder('doe.json'),
BSONCaseRecorder('doe.bson')
]
def configure(self):
self.add('paraboloid',Paraboloid())
self.add('driver',DOEdriver())
#There are a number of different kinds of DOE available in openmdao.lib.doegenerators
self.driver.DOEgenerator = FullFactorial(10) #Full Factorial DOE with 10 levels for each variable
#DOEdriver will automatically record the values of any parameters for each case
self.driver.add_parameter('paraboloid.x',low=-50,high=50)
self.driver.add_parameter('paraboloid.y',low=-50,high=50)
#tell the DOEdriver to also record any other variables you want to know for each case
self.driver.case_outputs = ['paraboloid.f_xy',]
#Simple recorder which stores the cases in memory.
self.driver.recorders = [ListCaseRecorder(),]
self.driver.workflow.add('paraboloid')
def configure(self):
"""Creates a new Assembly containing a PlugNozzleGeometry and DOE"""
self.replace("driver", DOEdriver())
self.driver.add("DOEgenerator", LatinHypercube(num_samples=5))
self.add("plug_noz", PlugNozzleGeometry())
self.driver.add_parameter('plug_noz.cowl.thickness[7]',
low=0.0,
high=0.5)
self.driver.add_parameter('plug_noz.cowl.thickness[8]',
low=0.0,
high=0.5)
self.driver.add_parameter('plug_noz.cowl.R[7]', low=-0.1, high=0.2)
self.driver.add_parameter('plug_noz.cowl.R[8]', low=-0.1, high=0.2)
self.driver.add_parameter('plug_noz.plug.R[7]', low=-0.1, high=0.5)
self.driver.add_parameter('plug_noz.plug.R[8]', low=-0.1, high=0.5)
def __init__(self, *args, **kwargs):
super(Analysis, self).__init__(self, *args, **kwargs)
self._tdir = mkdtemp()
#Components
self.add("A", MetaModel())
self.A.surrogate = {'default': KrigingSurrogate()}
self.A.model = ConceptA()
self.A.recorder = DBCaseRecorder(':memory:')
self.add('DOE_maker', DOE_Maker())
self.DOE_maker.cases = [10] * 3
#self.DOE_maker.force_execute = True
#Drivers
self.add("trainA", DOEdriver())
self.trainA.sequential = True
self.trainA.add_parameter("A.x")
self.trainA.add_parameter("A.y")
self.trainA.add_parameter("A.z")
self.trainA.add_event("A.train_next")
self.trainA.case_outputs = ['A.f1', 'A.f2']
self.trainA.recorder = DBCaseRecorder(os.path.join(self._tdir, 'A.db'))
self.add('driver', Iterator())
self.driver.add_stop_condition('len(DOE_maker.cases)==0')
self.driver.add_event('A.reset_training_data')
self.add('res', Res())
self.res.force_execute = True
#Iteration Hierarchy
self.driver.workflow.add(['DOE_maker', 'trainA', 'res'])
#self.driver.workflow.add(['DOE_maker','trainA'])
self.trainA.workflow.add('A')
#Data Connections
self.connect('DOE_maker.DOEgen', 'trainA.DOEgenerator')
def configure(self):
self._tdir = mkdtemp()
self.comp_name = None
#check to make sure no more than one component is being referenced
compnames = set()
for param in self.parent.get_parameters().values():
compnames.update(param.get_referenced_compnames())
if len(compnames) > 1:
self.parent.raise_exception('The EGO architecture can only be used on one'
'component at a time, but parameters from %s '
'were added to the problem formulation.' %compnames,
ValueError)
self.comp_name = compnames.pop()
#change name of component to add '_model' to it.
# lets me name the metamodel as the old name
self.comp= getattr(self.parent,self.comp_name)
self.comp.name = "%s_model"%self.comp_name
#add in the metamodel
meta_model = self.parent.add(self.comp_name,MetaModel()) #metamodel now replaces old component with same name
meta_model.default_surrogate = KrigingSurrogate()
meta_model.model = self.comp
meta_model_recorder = DBCaseRecorder(os.path.join(self._tdir,'trainer.db'))
meta_model.recorder = meta_model_recorder
EI = self.parent.add("EI",ExpectedImprovement())
self.objective = self.parent.get_objectives().keys()[0]
EI.criteria = self.objective
pfilter = self.parent.add("filter",ParetoFilter())
pfilter.criteria = [self.objective]
pfilter.case_sets = [meta_model_recorder.get_iterator(),]
#Driver Configuration
DOE_trainer = self.parent.add("DOE_trainer",DOEdriver())
DOE_trainer.sequential = True
DOE_trainer.DOEgenerator = OptLatinHypercube(num_samples=self.initial_DOE_size)
for name,param in self.parent.get_parameters().iteritems():
DOE_trainer.add_parameter(param)
DOE_trainer.add_event("%s.train_next"%self.comp_name)
DOE_trainer.case_outputs = [self.objective]
DOE_trainer.recorders = [DBCaseRecorder(':memory:')]
EI_opt = self.parent.add("EI_opt",Genetic())
EI_opt.opt_type = "maximize"
EI_opt.population_size = 100
EI_opt.generations = 10
#EI_opt.selection_method = "tournament"
for name,param in self.parent.get_parameters().iteritems():
EI_opt.add_parameter(param)
EI_opt.add_objective("EI.%s"%self.EI_PI)
retrain = self.parent.add("retrain",Driver())
retrain.recorders = self.data_recorders
retrain.add_event("%s.train_next"%self.comp_name)
iter = self.parent.add("iter",IterateUntil())
iter.max_iterations = self.sample_iterations
iter.add_stop_condition('EI.PI <= %s'%self.min_ei_pi)
#Data Connections
self.parent.connect("filter.pareto_set","EI.best_case")
self.parent.connect(self.objective,"EI.predicted_value")
#Iteration Heirarchy
self.parent.driver.workflow.add(['DOE_trainer', 'iter'])
#DOE_trainer.workflow.add(self.comp_name)
iter.workflow = SequentialWorkflow()
iter.workflow.add(['filter', 'EI_opt', 'retrain'])
#EI_opt.workflow.add([self.comp_name,'EI'])
retrain.workflow.add(self.comp_name)
def configure(self):
self._tdir = mkdtemp()
#Components
self.add("branin_meta_model", MetaModel())
self.branin_meta_model.default_surrogate = KrigingSurrogate()
self.branin_meta_model.model = BraninComponent()
self.branin_meta_model.recorder = DBCaseRecorder(':memory:')
self.branin_meta_model.force_execute = True
self.add("EI", ExpectedImprovement())
self.EI.criteria = "branin_meta_model.f_xy"
self.add("filter", ParetoFilter())
self.filter.criteria = ['branin_meta_model.f_xy']
self.filter.case_sets = [
self.branin_meta_model.recorder.get_iterator(),
]
self.filter.force_execute = True
#Driver Configuration
self.add("DOE_trainer", DOEdriver())
self.DOE_trainer.sequential = True
self.DOE_trainer.DOEgenerator = OptLatinHypercube(num_samples=15)
#self.DOE_trainer.DOEgenerator = FullFactorial(num_levels=5)
self.DOE_trainer.add_parameter("branin_meta_model.x",
low=-5.,
high=10.)
self.DOE_trainer.add_parameter("branin_meta_model.y", low=0., high=15.)
self.DOE_trainer.add_event("branin_meta_model.train_next")
self.DOE_trainer.case_outputs = ["branin_meta_model.f_xy"]
self.DOE_trainer.recorders = [
DBCaseRecorder(os.path.join(self._tdir, 'trainer.db'))
]
self.add("EI_opt", Genetic())
self.EI_opt.opt_type = "maximize"
self.EI_opt.population_size = 100
self.EI_opt.generations = 10
#self.EI_opt.selection_method = "tournament"
self.EI_opt.add_parameter("branin_meta_model.x", low=-5., high=10.)
self.EI_opt.add_parameter("branin_meta_model.y", low=0., high=15.)
self.EI_opt.add_objective("EI.PI")
self.add("retrain", MyDriver())
self.retrain.add_event("branin_meta_model.train_next")
self.retrain.recorders = [
DBCaseRecorder(os.path.join(self._tdir, 'retrain.db'))
]
self.add("iter", IterateUntil())
self.iter.max_iterations = 30
self.iter.add_stop_condition('EI.EI <= .0001')
#Iteration Heirarchy
self.driver.workflow.add(['DOE_trainer', 'iter'])
self.DOE_trainer.workflow.add('branin_meta_model')
self.iter.workflow = SequentialWorkflow()
self.iter.workflow.add(['filter', 'EI_opt', 'retrain'])
self.EI_opt.workflow.add(['branin_meta_model', 'EI'])
self.retrain.workflow.add('branin_meta_model')
#Data Connections
self.connect("filter.pareto_set", "EI.best_case")
self.connect("branin_meta_model.f_xy", "EI.predicted_value")
def configure(self):
self._tdir = mkdtemp()
#Components
self.add("spiral_meta_model", MetaModel())
self.spiral_meta_model.surrogate = {'default': KrigingSurrogate()}
self.spiral_meta_model.model = SpiralComponent()
self.spiral_meta_model.recorder = DBCaseRecorder(':memory:')
self.spiral_meta_model.force_execute = True
self.add("MOEI", MultiObjExpectedImprovement())
self.MOEI.criteria = [
'spiral_meta_model.f1_xy', 'spiral_meta_model.f2_xy'
]
self.add("filter", ParetoFilter())
self.filter.criteria = [
'spiral_meta_model.f1_xy', 'spiral_meta_model.f2_xy'
]
self.filter.case_sets = [
self.spiral_meta_model.recorder.get_iterator()
]
self.filter.force_execute = True
#Driver Configuration
self.add("DOE_trainer", DOEdriver())
self.DOE_trainer.sequential = True
self.DOE_trainer.DOEgenerator = OptLatinHypercube(num_samples=25)
self.DOE_trainer.add_parameter("spiral_meta_model.x")
self.DOE_trainer.add_parameter("spiral_meta_model.y")
self.DOE_trainer.add_event("spiral_meta_model.train_next")
self.DOE_trainer.case_outputs = [
'spiral_meta_model.f1_xy', 'spiral_meta_model.f2_xy'
]
self.DOE_trainer.recorders = [
DBCaseRecorder(os.path.join(self._tdir, 'trainer.db'))
]
self.add("MOEI_opt", Genetic())
self.MOEI_opt.opt_type = "maximize"
self.MOEI_opt.population_size = 100
self.MOEI_opt.generations = 10
#self.MOEI_opt.selection_method = "tournament"
self.MOEI_opt.add_parameter("spiral_meta_model.x")
self.MOEI_opt.add_parameter("spiral_meta_model.y")
self.MOEI_opt.add_objective("MOEI.PI")
self.add("retrain", MyDriver())
self.retrain.add_event("spiral_meta_model.train_next")
self.retrain.recorders = [
DBCaseRecorder(os.path.join(self._tdir, 'retrain.db'))
]
self.add("iter", IterateUntil())
self.iter.iterations = 30
self.iter.add_stop_condition('MOEI.PI <= .0001')
#Iteration Heirarchy
self.driver.workflow.add(['DOE_trainer', 'iter'])
self.DOE_trainer.workflow.add('spiral_meta_model')
self.iter.workflow = SequentialWorkflow()
self.iter.workflow.add(['filter', 'MOEI_opt', 'retrain'])
self.MOEI_opt.workflow.add(['spiral_meta_model', 'MOEI'])
self.retrain.workflow.add('spiral_meta_model')
#Data Connections
self.connect("filter.pareto_set", "MOEI.best_cases")
self.connect("spiral_meta_model.f1_xy", "MOEI.predicted_values[0]")
self.connect("spiral_meta_model.f2_xy", "MOEI.predicted_values[1]")
