def test_multi_surrogate_models(self):
model = set_as_top(Assembly())
model.add('meta', MetaModel(params=('x1', 'x2'),
responses=('y1', 'y2')))
model.driver.workflow.add('meta')
model.meta.warm_restart = True
model.meta.params.x1 = [1.0, 3.0]
model.meta.params.x2 = [1.0, 4.0]
model.meta.responses.y1 = [3.0, 1.0]
model.meta.responses.y2 = [1.0, 7.0]
model.meta.surrogates['y1'] = KrigingSurrogate()
model.meta.surrogates['y2'] = LogisticRegression()
model.meta.x1 = 2.0
model.meta.x2 = 3.0
model.meta.run()
self.assertTrue(isinstance(model.meta.y1, NormalDistribution))
self.assertTrue(isinstance(model.meta.y2, float))
model.meta.surrogates['y1'] = LogisticRegression()
model.meta.surrogates['y2'] = KrigingSurrogate()
model.meta.run()
self.assertTrue(isinstance(model.meta.y1, float))
self.assertTrue(isinstance(model.meta.y2, NormalDistribution))
model.meta.default_surrogate = KrigingSurrogate()
model.meta.surrogates['y1'] = None
model.meta.surrogates['y2'] = None
model.meta.run()
self.assertTrue(isinstance(model.meta.y1, NormalDistribution))
self.assertTrue(isinstance(model.meta.y2, NormalDistribution))
model.meta.surrogates['y1'] = LogisticRegression()
model.meta.run()
self.assertTrue(isinstance(model.meta.y1, float))
self.assertTrue(isinstance(model.meta.y2, NormalDistribution))
def __init__(self):
super(Simulation, self).__init__()
#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()]
self.DOE_Trainer.force_execute = True
#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()]
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('trig_meta_model')
self.DOE_Validate.workflow.add('trig_meta_model')
self.DOE_Validate.workflow.add('trig_calc')
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):
#Components
self.add("trig_meta_model", MetaModel())
self.trig_meta_model.model = Trig()
self.trig_meta_model.surrogates['f_x_sin'] = LogisticRegression()
self.trig_meta_model.surrogates['f_x_cos'] = FloatKrigingSurrogate()
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')