def test_multi_surrogate_models_bad_surrogate_dict(self):
metamodel = MetaModel()
metamodel.name = 'meta'
metamodel.sur_d = KrigingSurrogate()
try:
metamodel.model = Simple()
except RuntimeError,err:
self.assertEqual("meta: No default surrogate model is defined and the following outputs do not have a surrogate model: ['c']. "
"Either specify default_surrogate, or specify a surrogate model for all "
"outputs.",str(err))
def test_multi_surrogate_models_bad_surrogate_dict(self):
metamodel = MetaModel()
metamodel.name = 'meta'
metamodel.surrogate = {'d':KrigingSurrogate()}
try:
metamodel.model = Simple()
except ValueError,err:
self.assertEqual('meta: No default surrogate model was specified. '
'Either specify a default, or specify a surrogate model for all '
'outputs',str(err))
def test_multi_surrogate_models_bad_surrogate_dict(self):
metamodel = MetaModel()
metamodel.name = 'meta'
metamodel.surrogate = {'d': KrigingSurrogate()}
try:
metamodel.model = Simple()
except ValueError, err:
self.assertEqual(
'meta: No default surrogate model was specified. '
'Either specify a default, or specify a surrogate model for all '
'outputs', str(err))
def test_multi_surrogate_models_bad_surrogate_dict(self):
metamodel = MetaModel()
metamodel.name = "meta"
metamodel.surrogate = {"d": KrigingSurrogate()}
try:
metamodel.model = Simple()
except ValueError, err:
self.assertEqual(
"meta: No default surrogate model was specified. "
"Either specify a default, or specify a surrogate model for all "
"outputs",
str(err),
)
def test_warm_start(self):
model = set_as_top(Assembly())
model.add('meta', MetaModel(params=('x1', 'x2'),
responses=('y1', 'y2')))
model.driver.workflow.add('meta')
model.meta.default_surrogate = ResponseSurface()
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.default_surrogate = ResponseSurface()
model.meta.x1 = 2.0
model.meta.x2 = 3.0
model.meta.run()
assert_rel_error(self, model.meta.y1, 1.9085, .001)
assert_rel_error(self, model.meta.y2, 3.9203, .001)
# Adding the 3rd point moves the estimate for that point
# back to where it should be.
model.meta.params.x1 = [2.0]
model.meta.params.x2 = [3.0]
model.meta.responses.y1 = [2.0]
model.meta.responses.y2 = [4.0]
self.assertTrue(model.meta._train == True)
model.meta.run()
assert_rel_error(self, model.meta.y1, 2.0, .00001)
assert_rel_error(self, model.meta.y2, 4.0, .00001)
def test_basics(self):
model = set_as_top(Assembly())
model.add('meta', MetaModel(params=('x1', 'x2'),
responses=('y1', 'y2')))
model.driver.workflow.add('meta')
model.meta.params.x1 = [1.0, 2.0, 3.0]
model.meta.params.x2 = [1.0, 3.0, 4.0]
model.meta.responses.y1 = [3.0, 2.0, 1.0]
model.meta.responses.y2 = [1.0, 4.0, 7.0]
model.meta.default_surrogate = ResponseSurface()
model.meta.x1 = 2.0
model.meta.x2 = 3.0
model.meta.run()
assert_rel_error(self, model.meta.y1, 2.0, .00001)
assert_rel_error(self, model.meta.y2, 4.0, .00001)
model.meta.x1 = 2.5
model.meta.x2 = 3.5
model.meta.run()
assert_rel_error(self, model.meta.y1, 1.5934, .001)
# Slot a new surrogate.
model.meta.default_surrogate = FloatKrigingSurrogate()
self.assertTrue(model.meta._train == True)
model.meta.run()
assert_rel_error(self, model.meta.y1, 1.4609, .001)
def test_model_change(self):
metamodel = MetaModel()
mmins = set(metamodel.list_inputs())
mmouts = set(metamodel.list_outputs())
metamodel.surrogate = {'default':KrigingSurrogate()}
metamodel.model = Simple()
inputs = set(metamodel.list_inputs())
outputs = set(metamodel.list_outputs())
self.assertEquals(inputs-mmins, set(['a','b']))
self.assertEquals(outputs-mmouts, set(['c','d']))
# now put a different model in
metamodel.model = Simple2()
inputs = set(metamodel.list_inputs())
outputs = set(metamodel.list_outputs())
self.assertEquals(inputs-mmins, set(['w','x']))
self.assertEquals(outputs-mmouts, set(['y','z']))
def test_model_change(self):
metamodel = MetaModel()
mmins = set(metamodel.list_inputs())
mmouts = set(metamodel.list_outputs())
metamodel.surrogate = {"default": KrigingSurrogate()}
metamodel.model = Simple()
inputs = set(metamodel.list_inputs())
outputs = set(metamodel.list_outputs())
self.assertEquals(inputs - mmins, set(["a", "b"]))
self.assertEquals(outputs - mmouts, set(["c", "d"]))
# now put a different model in
metamodel.model = Simple2()
inputs = set(metamodel.list_inputs())
outputs = set(metamodel.list_outputs())
self.assertEquals(inputs - mmins, set(["w", "x"]))
self.assertEquals(outputs - mmouts, set(["y", "z"]))
def test_proxy(self):
meta = MetaModel()
meta.default_surrogate = KrigingSurrogate()
meta.model = SimpleProxy()
for i in range(3):
meta.train_next = True
meta.run()
self.assertEqual(len(meta._training_data['f_x']), 3)
def _get_assembly(self):
asm = set_as_top(Assembly())
asm.add('metamodel', MetaModel())
asm.add('comp1', Simple())
asm.add('comp2', Simple())
asm.metamodel.surrogate = {'default': KrigingSurrogate()}
asm.metamodel.model = Simple()
asm.metamodel.recorder = DumbRecorder()
asm.driver.workflow.add(['metamodel', 'comp1', 'comp2'])
asm.connect('comp1.c', 'metamodel.a')
asm.connect('comp1.d', 'metamodel.b')
asm.connect('metamodel.c', 'comp2.a')
asm.connect('metamodel.d', 'comp2.b')
return asm
def test_comp_error(self):
a = Assembly()
a.add('m', MetaModel())
a.m.surrogate = {'default': KrigingSurrogate()}
a.m.model = DummyError()
a.m.train_next = True
a.driver.workflow.add('m')
try:
a.run()
except RuntimeError as err:
self.assertEqual("m.model: Test Error", str(err))
else:
self.fail("RuntimeError expected")
def _trained_asm(self, avals, bvals):
asm = set_as_top(Assembly())
asm.add('metamodel', MetaModel())
asm.metamodel.surrogate = {'default': KrigingSurrogate()}
asm.metamodel.model = Simple()
asm.metamodel.recorder = DumbRecorder()
asm.driver.workflow.add(['metamodel'])
for a, b in zip(avals, bvals):
asm.metamodel.a = a
asm.metamodel.b = b
asm.metamodel.train_next = 1
asm.metamodel.run()
return asm
def test_proxy(self):
meta = MetaModel()
meta.default_surrogate = KrigingSurrogate()
meta.model = SimpleProxy()
for i in range(3):
meta.train_next = True
meta.run()
self.assertEqual(len(meta._training_data['f_x']), 3)
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 _get_assembly(self, default=True, meta=True):
asm = set_as_top(Assembly())
asm.add('comp1', Simple())
asm.add('comp2', Simple())
if meta:
asm.add('metamodel', MetaModel())
if default:
asm.metamodel.default_surrogate = KrigingSurrogate()
asm.metamodel.model = Simple()
asm.metamodel.recorder = DumbRecorder()
else:
asm.add('metamodel', Simple()) # put a real Simple comp in place of metamodel
asm.driver.workflow.add(['metamodel','comp1','comp2'])
asm.connect('comp1.c','metamodel.a')
asm.connect('comp1.d','metamodel.b')
asm.connect('metamodel.c','comp2.a')
asm.connect('metamodel.d','comp2.b')
return asm
def test_assy_promotion(self):
class Trig(Component):
x = Float(0,iotype="in",units="rad")
f_x_sin = Float(0.0,iotype="out")
f_x_cos = Float(0.0,iotype="out")
def execute(self):
self.f_x_sin = .5*sin(self.x)
self.f_x_cos = .5*cos(self.x)
class TrigAsmb(Assembly):
x = Float(0,iotype="in",units="rad")
def configure(self):
self.add('trig1', Trig())
self.add('trig2', Trig())
self.connect('x', 'trig1.x')
self.connect('trig1.f_x_sin', 'trig2.x')
self.create_passthrough('trig2.f_x_sin')
self.create_passthrough('trig2.f_x_cos')
self.driver.workflow.add(['trig1', 'trig2'])
top = set_as_top(Assembly())
top.add("trig_meta_model", MetaModel())
top.trig_meta_model.model = TrigAsmb()
top.trig_meta_model.surrogates['f_x_sin'] = FloatKrigingSurrogate()
top.trig_meta_model.surrogates['f_x_cos'] = FloatKrigingSurrogate()
# Will error if vars don't promote correctly
top.run()
def test_multi_surrogate_models_bad_surrogate_dict(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()
try:
model.meta.run()
except RuntimeError as err:
self.assertEqual("meta: No default surrogate model is defined and "
"the following outputs do not have a surrogate "
"model: ['y2']. Either specify default_surrogate, "
"or specify a surrogate model for all outputs.",
str(err))
else:
self.fail('ValueError expected')
def test_warm_start(self):
metamodel = MetaModel()
metamodel.name = 'meta'
metamodel.surrogate = {'default': KrigingSurrogate()}
metamodel.model = Simple()
metamodel.recorder = DumbRecorder()
simple = Simple()
cases = []
metamodel.a = 1.
metamodel.b = 2.
metamodel.train_next = True
metamodel.run()
inputs = [('meta2.a', metamodel.a), ('meta2.b', metamodel.b)]
outputs = [('meta2.c', metamodel.c.mu), ('meta2.d', metamodel.d.mu)]
cases.append(Case(inputs=inputs, outputs=outputs))
metamodel.a = 3.
metamodel.b = 5.
metamodel.train_next = True
metamodel.run()
inputs = [('meta2.a', metamodel.a), ('meta2.b', metamodel.b)]
outputs = [('meta2.c', metamodel.c.mu), ('meta2.d', metamodel.d.mu)]
cases.append(Case(inputs=inputs, outputs=outputs))
case_iter = ListCaseIterator(cases)
metamodel2 = MetaModel()
metamodel2.name = 'meta2'
metamodel2.surrogate = {'default': KrigingSurrogate()}
metamodel2.model = Simple()
metamodel2.recorder = DumbRecorder()
metamodel2.warm_start_data = case_iter
metamodel2.a = simple.a = 1
metamodel2.b = simple.b = 2
metamodel2.run()
simple.run()
self.assertEqual(metamodel2.c.getvalue(), 3.)
self.assertEqual(metamodel2.d.getvalue(), -1.)
self.assertEqual(metamodel2.c.getvalue(), simple.c)
self.assertEqual(metamodel2.d.getvalue(), simple.d)
metamodel = MetaModel()
metamodel.name = "meta"
metamodel.surrogate = {"d": KrigingSurrogate()}
try:
metamodel.model = Simple()
except ValueError, err:
self.assertEqual(
"meta: No default surrogate model was specified. "
"Either specify a default, or specify a surrogate model for all "
"outputs",
str(err),
)
else:
self.fail("ValueError expected")
metamodel = MetaModel()
metamodel.name = "meta"
metamodel.surrogate = {"d": KrigingSurrogate()}
metamodel.includes = ["a", "b", "d"]
try:
metamodel.model = Simple()
except ValueError, err:
if (
'meta: Dict provided for "surrogates" does not include a value for "c". All outputs must be specified'
== str(err)
):
self.fail("should not get a value error for variable c. It is not included in the metamodel")
def test_multi_surrogate_models(self):
metamodel = MetaModel()
metamodel.name = "meta"
def test_model_change(self):
metamodel = MetaModel()
mmins = set(metamodel.list_inputs())
mmouts = set(metamodel.list_outputs())
metamodel.surrogate = {'default': KrigingSurrogate()}
metamodel.model = Simple()
inputs = set(metamodel.list_inputs())
outputs = set(metamodel.list_outputs())
self.assertEquals(inputs - mmins, set(['a', 'b']))
self.assertEquals(outputs - mmouts, set(['c', 'd']))
# now put a different model in
metamodel.model = Simple2()
inputs = set(metamodel.list_inputs())
outputs = set(metamodel.list_outputs())
self.assertEquals(inputs - mmins, set(['w', 'x']))
self.assertEquals(outputs - mmouts, set(['y', 'z']))
def test_reset_training_data_event(self):
metamodel = MetaModel()
metamodel.name = 'meta'
metamodel.surrogate = {'default': KrigingSurrogate()}
metamodel.model = Simple()
metamodel.recorder = DumbRecorder()
simple = Simple()
metamodel.a = 1.
metamodel.b = 2.
metamodel.train_next = True
metamodel.run()
metamodel.a = 2.
metamodel.b = 3.
metamodel.train_next = True
metamodel.run()
def test_multi_surrogate_models(self):
metamodel = MetaModel()
metamodel.name = 'meta'
metamodel.surrogate = {
'd': KrigingSurrogate(),
'c': LogisticRegression()
}
metamodel.model = Simple()
metamodel.recorder = DumbRecorder()
simple = Simple()
metamodel.a = simple.a = 1.
metamodel.b = simple.b = 2.
metamodel.train_next = True
simple.run()
metamodel.run()
metamodel.a = simple.a = 3.
metamodel.b = simple.b = 4.
metamodel.train_next = True
simple.run()
metamodel.run()
self.assertTrue(isinstance(metamodel.d, NormalDistribution))
self.assertTrue(isinstance(metamodel.c, float))
def test_multi_surrogate_models_bad_surrogate_dict(self):
metamodel = MetaModel()
metamodel.name = 'meta'
metamodel.surrogate = {'d': KrigingSurrogate()}
try:
metamodel.model = Simple()
except ValueError, err:
self.assertEqual(
'meta: No default surrogate model was specified. '
'Either specify a default, or specify a surrogate model for all '
'outputs', str(err))
else:
self.fail('ValueError expected')
metamodel = MetaModel()
metamodel.name = 'meta'
metamodel.surrogate = {'d': KrigingSurrogate()}
metamodel.includes = ['a', 'b', 'd']
try:
metamodel.model = Simple()
except ValueError, err:
if 'meta: Dict provided for "surrogates" does not include a value for "c". All outputs must be specified' == str(
err):
self.fail(
'should not get a value error for variable c. It is not included in the metamodel'
)
def test_multi_surrogate_models(self):
metamodel = MetaModel()
metamodel.name = 'meta'
def configure(self):
self.add('mm', MetaModel())
self.mm.default_surrogate = KrigingSurrogate()
self.mm.model = Dummy()
def test_default_surrogate_change(self):
metamodel = MetaModel()
mmins = set(metamodel.list_inputs())
mmouts = set(metamodel.list_outputs())
metamodel.default_surrogate = KrigingSurrogate()
metamodel.model = Simple()
metamodel.default_surrogate = LogisticRegression()
attrs = metamodel.get_attributes(io_only=False)
for s in attrs['Slots']:
self.assertNotEqual(s['name'], 'c')
self.assertNotEqual(s['name'], 'd')
inputs = set(metamodel.list_inputs())
outputs = set(metamodel.list_outputs())
self.assertEquals(inputs - mmins, set(['a', 'b']))
self.assertEquals(outputs - mmouts, set(['c', 'd']))
for i in range(3):
metamodel.train_next = True
metamodel.run()
self.assertTrue(len(metamodel._training_data['c']) == 3)
self.assertTrue(len(metamodel._training_data['d']) == 3)
self.assertTrue(len(metamodel._training_input_history) == 3)
metamodel.includes = ['a', 'b', 'c', 'd']
self.assertTrue(len(metamodel._training_data['c']) == 3)
self.assertTrue(len(metamodel._training_data['d']) == 3)
self.assertTrue(len(metamodel._training_input_history) == 3)
# removing an output should not clobber the rest of the training data
metamodel.includes = ['a', 'b', 'c']
self.assertTrue(len(metamodel._training_data['c']) == 3)
self.assertTrue('d' not in metamodel._training_data)
self.assertTrue(len(metamodel._training_input_history) == 3)
# now put a different model in with the same inputs/outputs
metamodel.model = SimpleMatch()
metamodel.includes = ['a', 'b', 'c', 'd']
inputs = set(metamodel.list_inputs())
outputs = set(metamodel.list_outputs())
self.assertEquals(inputs - mmins, set(['a', 'b']))
self.assertEquals(outputs - mmouts, set(['c', 'd']))
self.assertTrue(len(metamodel._training_data['c']) == 0)
self.assertTrue(len(metamodel._training_data['d']) == 0)
self.assertTrue(len(metamodel._training_input_history) == 0)
# now put a different model in
metamodel.model = Simple2()
metamodel.includes = ['w', 'x', 'y', 'z']
inputs = set(metamodel.list_inputs())
outputs = set(metamodel.list_outputs())
self.assertEquals(inputs - mmins, set(['w', 'x']))
self.assertEquals(outputs - mmouts, set(['y', 'z']))
def test_reset_training_data_event(self):
metamodel = MetaModel()
metamodel.name = 'meta'
metamodel.surrogate = {'default':KrigingSurrogate()}
metamodel.model = Simple()
metamodel.recorder = DumbRecorder()
simple = Simple()
metamodel.a = 1.
metamodel.b = 2.
metamodel.train_next = True
metamodel.run()
metamodel.a = 2.
metamodel.b = 3.
metamodel.train_next = True
metamodel.run()
def __init__(self):
super(Sim, self).__init__()
self.add('mm', MetaModel())
self.mm.surrogate = {'default': KrigingSurrogate()}
self.mm.model = Dummy()
def test_multi_surrogate_models(self):
metamodel = MetaModel()
metamodel.name = 'meta'
metamodel.surrogate = {'d':KrigingSurrogate(),
'c':LogisticRegression()}
metamodel.model = Simple()
metamodel.recorder = DumbRecorder()
simple = Simple()
metamodel.a = simple.a = 1.
metamodel.b = simple.b = 2.
metamodel.train_next = True
simple.run()
metamodel.run()
metamodel.a = simple.a = 3.
metamodel.b = simple.b = 4.
metamodel.train_next = True
simple.run()
metamodel.run()
self.assertTrue(isinstance(metamodel.d,NormalDistribution))
self.assertTrue(isinstance(metamodel.c,float))
def test_multi_surrogate_models_bad_surrogate_dict(self):
metamodel = MetaModel()
metamodel.name = 'meta'
metamodel.model = Simple()
metamodel.surrogates[ 'd' ] = KrigingSurrogate()
try:
metamodel.run()
except RuntimeError,err:
self.assertEqual("meta: No default surrogate model is defined and the following outputs do not have a surrogate model: ['c']. "
"Either specify default_surrogate, or specify a surrogate model for all "
"outputs.",str(err))
else:
self.fail('ValueError expected')
metamodel = MetaModel()
metamodel.name = 'meta'
metamodel.surrogates['d'] = KrigingSurrogate()
metamodel.includes = ['a','b','d']
try:
metamodel.model = Simple()
except ValueError,err:
if 'meta: No surrogate was provided for "c". All outputs must have a surrogate' == str(err):
self.fail('should not get a value error for variable c. It is not included in the metamodel')
def test_multi_surrogate_models(self):
metamodel = MetaModel()
metamodel.name = 'meta'
metamodel.model = Simple()
metamodel.surrogates['d'] = KrigingSurrogate()
metamodel.surrogates['c'] = LogisticRegression()
def test_model_change(self):
metamodel = MetaModel()
mmins = set(metamodel.list_inputs())
mmouts = set(metamodel.list_outputs())
metamodel.default_surrogate = KrigingSurrogate()
metamodel.model = Simple()
attrs = metamodel.get_attributes(io_only=False)
for s in attrs['Slots']:
self.assertNotEqual(s['name'], 'c')
self.assertNotEqual(s['name'], 'd')
inputs = set(metamodel.list_inputs())
outputs = set(metamodel.list_outputs())
self.assertEquals(inputs-mmins, set(['a','b']))
self.assertEquals(outputs-mmouts, set(['c','d']))
for i in range(3):
metamodel.train_next = True
metamodel.run()
self.assertTrue(len(metamodel._training_data['c']) == 3)
self.assertTrue(len(metamodel._training_data['d']) == 3)
self.assertTrue(len(metamodel._training_input_history) == 3)
metamodel.includes = ['a','b','c','d']
self.assertTrue(len(metamodel._training_data['c']) == 3)
self.assertTrue(len(metamodel._training_data['d']) == 3)
self.assertTrue(len(metamodel._training_input_history) == 3)
# removing an output shouldn't clobber the rest of the training data
metamodel.includes = ['a','b','c']
self.assertTrue(len(metamodel._training_data['c']) == 3)
self.assertTrue('d' not in metamodel._training_data)
self.assertTrue(len(metamodel._training_input_history) == 3)
# now put a different model in with the same inputs/outputs
metamodel.model = SimpleMatch()
metamodel.includes = ['a','b','c','d']
inputs = set(metamodel.list_inputs())
outputs = set(metamodel.list_outputs())
self.assertEquals(inputs-mmins, set(['a','b']))
self.assertEquals(outputs-mmouts, set(['c', 'd']))
self.assertTrue(len(metamodel._training_data['c']) == 0)
self.assertTrue(len(metamodel._training_data['d']) == 0)
self.assertTrue(len(metamodel._training_input_history) == 0)
# now put a different model in
metamodel.model = Simple2()
metamodel.includes = ['w','x','y','z']
inputs = set(metamodel.list_inputs())
outputs = set(metamodel.list_outputs())
self.assertEquals(inputs-mmins, set(['w','x']))
self.assertEquals(outputs-mmouts, set(['y','z']))
def test_default_execute(self):
metamodel = MetaModel()
metamodel.name = 'meta'
metamodel.surrogate = {'default': KrigingSurrogate()}
metamodel.model = Simple()
metamodel.recorder = DumbRecorder()
simple = Simple()
metamodel.a = simple.a = 1.
metamodel.b = simple.b = 2.
metamodel.train_next = True
simple.run()
metamodel.run()
metamodel.a = simple.a = 1.
metamodel.b = simple.b = 2.
metamodel.train_next = True
simple.run()
metamodel.run()
self.assertEqual(metamodel.c.getvalue(), 3.)
self.assertEqual(metamodel.d.getvalue(), -1.)
self.assertEqual(metamodel.c.getvalue(), simple.c)
self.assertEqual(metamodel.d.getvalue(), simple.d)
def configure(self):
mm = MetaModel()
self.add('mm', mm)
self.mm.default_surrogate = FloatKrigingSurrogate()
self.driver.workflow.add('mm')
def test_warm_start(self):
metamodel = MetaModel()
metamodel.name = 'meta'
metamodel.surrogate = {'default':KrigingSurrogate()}
metamodel.model = Simple()
metamodel.recorder = DumbRecorder()
simple = Simple()
cases = []
metamodel.a = 1.
metamodel.b = 2.
metamodel.train_next = True
metamodel.run()
inputs = [('meta2.a',metamodel.a),('meta2.b',metamodel.b)]
outputs = [('meta2.c',metamodel.c.mu),('meta2.d',metamodel.d.mu)]
cases.append(Case(inputs=inputs,outputs=outputs))
metamodel.a = 3.
metamodel.b = 5.
metamodel.train_next = True
metamodel.run()
inputs = [('meta2.a',metamodel.a),('meta2.b',metamodel.b)]
outputs = [('meta2.c',metamodel.c.mu),('meta2.d',metamodel.d.mu)]
cases.append(Case(inputs=inputs,outputs=outputs))
case_iter = ListCaseIterator(cases)
metamodel2 = MetaModel()
metamodel2.name = 'meta2'
metamodel2.surrogate = {'default':KrigingSurrogate()}
metamodel2.model = Simple()
metamodel2.recorder = DumbRecorder()
metamodel2.warm_start_data = case_iter
metamodel2.a = simple.a = 1
metamodel2.b = simple.b = 2
metamodel2.run()
simple.run()
self.assertEqual(metamodel2.c.getvalue(), 3.)
self.assertEqual(metamodel2.d.getvalue(), -1.)
self.assertEqual(metamodel2.c.getvalue(), simple.c)
self.assertEqual(metamodel2.d.getvalue(), simple.d)
self.assertEqual(metamodel.d.getvalue(), simple.d)
def test_multi_surrogate_models_bad_surrogate_dict(self):
metamodel = MetaModel()
metamodel.name = 'meta'
metamodel.surrogate = {'d':KrigingSurrogate()}
try:
metamodel.model = Simple()
except ValueError,err:
self.assertEqual('meta: No default surrogate model was specified. '
'Either specify a default, or specify a surrogate model for all '
'outputs',str(err))
else:
self.fail('ValueError expected')
metamodel = MetaModel()
metamodel.name = 'meta'
metamodel.surrogate = {'d':KrigingSurrogate()}
metamodel.includes = ['a','b','d']
try:
metamodel.model = Simple()
except ValueError,err:
if 'meta: Dict provided for "surrogates" does not include a value for "c". All outputs must be specified'==str(err):
self.fail('should not get a value error for variable c. It is not included in the metamodel')
def test_multi_surrogate_models(self):
metamodel = MetaModel()
metamodel.name = 'meta'
metamodel.surrogate = {'d':KrigingSurrogate(),
'c':LogisticRegression()}
def configure(self):
self.add('mm', MetaModel())
self.mm.surrogate = {'default': KrigingSurrogate()}
self.mm.model = Dummy()
def test_default_execute(self):
metamodel = MetaModel()
metamodel.name = 'meta'
metamodel.surrogate = {'default':KrigingSurrogate()}
metamodel.model = Simple()
metamodel.recorder = DumbRecorder()
simple = Simple()
metamodel.a = simple.a = 1.
metamodel.b = simple.b = 2.
metamodel.train_next = True
simple.run()
metamodel.run()
metamodel.a = simple.a = 1.
metamodel.b = simple.b = 2.
metamodel.train_next = True
simple.run()
metamodel.run()
self.assertEqual(metamodel.c.getvalue(), 3.)
self.assertEqual(metamodel.d.getvalue(), -1.)
self.assertEqual(metamodel.c.getvalue(), simple.c)
self.assertEqual(metamodel.d.getvalue(), simple.d)