def test_unequal_training_outputs(self):
meta = MetaModel()
meta.add_param('x', 0.)
meta.add_param('y', 0.)
meta.add_output('f', 0.)
meta.default_surrogate = FloatKrigingSurrogate()
prob = Problem(Group())
prob.root.add('meta', meta)
prob.setup(check=False)
prob['meta.train:x'] = [1.0, 1.0, 1.0, 1.0]
prob['meta.train:y'] = [1.0, 2.0, 3.0, 4.0]
prob['meta.train:f'] = [1.0, 1.0]
prob['meta.x'] = 1.0
prob['meta.y'] = 1.0
with self.assertRaises(RuntimeError) as cm:
prob.run()
expected = "MetaModel: Each variable must have the same number" \
" of training points. Expected 4 but found" \
" 2 points for 'f'."
self.assertEqual(str(cm.exception), expected)
def test_unequal_training_inputs(self):
meta = MetaModel()
meta.add_param('x', 0.)
meta.add_param('y', 0.)
meta.add_output('f', 0.)
meta.default_surrogate = FloatKrigingSurrogate()
prob = Problem(Group())
prob.root.add('meta', meta)
prob.setup(check=False)
prob['meta.train:x'] = [1.0, 1.0, 1.0, 1.0]
prob['meta.train:y'] = [1.0, 2.0]
prob['meta.train:f'] = [1.0, 1.0, 1.0, 1.0]
prob['meta.x'] = 1.0
prob['meta.y'] = 1.0
with self.assertRaises(RuntimeError) as cm:
prob.run()
expected = "MetaModel: Each variable must have the same number" \
" of training points. Expected 4 but found" \
" 2 points for 'y'."
self.assertEqual(str(cm.exception), expected)
def test_array_outputs(self):
meta = MetaModel()
meta.add_param('x', np.zeros((2, 2)))
meta.add_output('y', np.zeros(2,))
meta.default_surrogate = FloatKrigingSurrogate()
prob = Problem(Group())
prob.root.add('meta', meta)
prob.setup(check=False)
prob['meta.train:x'] = [
[[1.0, 1.0], [1.0, 1.0]],
[[2.0, 1.0], [1.0, 1.0]],
[[1.0, 2.0], [1.0, 1.0]],
[[1.0, 1.0], [2.0, 1.0]],
[[1.0, 1.0], [1.0, 2.0]]
]
prob['meta.train:y'] = [[3.0, 1.0],
[2.0, 4.0],
[1.0, 7.0],
[6.0, -3.0],
[-2.0, 3.0]]
prob['meta.x'] = [[1.0, 2.0], [1.0, 1.0]]
prob.run()
assert_rel_error(self, prob['meta.y'], np.array([1.0, 7.0]), .00001)
def test_vector_inputs(self):
meta = MetaModel()
meta.add_param('x', np.zeros(4))
meta.add_output('y1', 0.)
meta.add_output('y2', 0.)
meta.default_surrogate = FloatKrigingSurrogate()
prob = Problem(Group())
prob.root.add('meta', meta)
prob.setup(check=False)
prob['meta.train:x'] = [
[1.0, 1.0, 1.0, 1.0],
[2.0, 1.0, 1.0, 1.0],
[1.0, 2.0, 1.0, 1.0],
[1.0, 1.0, 2.0, 1.0],
[1.0, 1.0, 1.0, 2.0]
]
prob['meta.train:y1'] = [3.0, 2.0, 1.0, 6.0, -2.0]
prob['meta.train:y2'] = [1.0, 4.0, 7.0, -3.0, 3.0]
prob['meta.x'] = [1.0, 2.0, 1.0, 1.0]
prob.run()
assert_rel_error(self, prob['meta.y1'], 1.0, .00001)
assert_rel_error(self, prob['meta.y2'], 7.0, .00001)
def test_derivatives(self):
meta = MetaModel()
meta.add_param('x', 0.)
meta.add_output('f', 0.)
meta.default_surrogate = FloatKrigingSurrogate()
prob = Problem(Group())
prob.root.add('meta', meta, promotes=['x'])
prob.root.add('p', IndepVarComp('x', 0.), promotes=['x'])
prob.setup(check=False)
prob['meta.train:x'] = [0., .25, .5, .75, 1.]
prob['meta.train:f'] = [1., .75, .5, .25, 0.]
prob['x'] = 0.125
prob.run()
Jf = prob.calc_gradient(['x'], ['meta.f'], mode='fwd')
Jr = prob.calc_gradient(['x'], ['meta.f'], mode='rev')
assert_rel_error(self, Jf[0][0], -1.00011, 1.0e-5)
assert_rel_error(self, Jr[0][0], -1.00011, 1.0e-5)
stream = cStringIO()
prob.check_partial_derivatives(out_stream=stream)
abs_errors = findall('Absolute Error \(.+\) : (.+)', stream.getvalue())
self.assertTrue(len(abs_errors) > 0)
for match in abs_errors:
abs_error = float(match)
self.assertTrue(abs_error < 1e-6)
def test_unequal_training_outputs(self):
meta = MetaModel()
meta.add_param("x", 0.0)
meta.add_param("y", 0.0)
meta.add_output("f", 0.0)
meta.default_surrogate = FloatKrigingSurrogate()
prob = Problem(Group())
prob.root.add("meta", meta)
prob.setup(check=False)
prob["meta.train:x"] = [1.0, 1.0, 1.0, 1.0]
prob["meta.train:y"] = [1.0, 2.0, 3.0, 4.0]
prob["meta.train:f"] = [1.0, 1.0]
prob["meta.x"] = 1.0
prob["meta.y"] = 1.0
with self.assertRaises(RuntimeError) as cm:
prob.run()
expected = (
"MetaModel: Each variable must have the same number"
" of training points. Expected 4 but found"
" 2 points for 'f'."
)
self.assertEqual(str(cm.exception), expected)
def test_array_inputs(self):
meta = MetaModel()
meta.add_param("x", np.zeros((2, 2)))
meta.add_output("y1", 0.0)
meta.add_output("y2", 0.0)
meta.default_surrogate = FloatKrigingSurrogate()
prob = Problem(Group())
prob.root.add("meta", meta)
prob.setup(check=False)
prob["meta.train:x"] = [
[[1.0, 1.0], [1.0, 1.0]],
[[2.0, 1.0], [1.0, 1.0]],
[[1.0, 2.0], [1.0, 1.0]],
[[1.0, 1.0], [2.0, 1.0]],
[[1.0, 1.0], [1.0, 2.0]],
]
prob["meta.train:y1"] = [3.0, 2.0, 1.0, 6.0, -2.0]
prob["meta.train:y2"] = [1.0, 4.0, 7.0, -3.0, 3.0]
prob["meta.x"] = [[1.0, 2.0], [1.0, 1.0]]
prob.run()
assert_rel_error(self, prob["meta.y1"], 1.0, 0.00001)
assert_rel_error(self, prob["meta.y2"], 7.0, 0.00001)
def test_warm_start(self):
# create metamodel with warm_restart = True
meta = MetaModel()
meta.add_param('x1', 0.)
meta.add_param('x2', 0.)
meta.add_output('y1', 0.)
meta.add_output('y2', 0.)
meta.default_surrogate = ResponseSurface()
meta.warm_restart = True
# add to problem
prob = Problem(Group())
prob.root.add('meta', meta)
prob.setup(check=False)
# provide initial training data
prob['meta.train:x1'] = [1.0, 3.0]
prob['meta.train:x2'] = [1.0, 4.0]
prob['meta.train:y1'] = [3.0, 1.0]
prob['meta.train:y2'] = [1.0, 7.0]
# run against a data point and check result
prob['meta.x1'] = 2.0
prob['meta.x2'] = 3.0
prob.run()
assert_rel_error(self, prob['meta.y1'], 1.9085, .001)
assert_rel_error(self, prob['meta.y2'], 3.9203, .001)
# Add 3rd training point, moves the estimate for that point
# back to where it should be.
prob['meta.train:x1'] = [2.0]
prob['meta.train:x2'] = [3.0]
prob['meta.train:y1'] = [2.0]
prob['meta.train:y2'] = [4.0]
meta.train = True # currently need to tell meta to re-train
prob.run()
assert_rel_error(self, prob['meta.y1'], 2.0, .00001)
assert_rel_error(self, prob['meta.y2'], 4.0, .00001)
def test_warm_start(self):
# create metamodel with warm_restart = True
meta = MetaModel()
meta.add_param("x1", 0.0)
meta.add_param("x2", 0.0)
meta.add_output("y1", 0.0)
meta.add_output("y2", 0.0)
meta.default_surrogate = ResponseSurface()
meta.warm_restart = True
# add to problem
prob = Problem(Group())
prob.root.add("meta", meta)
prob.setup(check=False)
# provide initial training data
prob["meta.train:x1"] = [1.0, 3.0]
prob["meta.train:x2"] = [1.0, 4.0]
prob["meta.train:y1"] = [3.0, 1.0]
prob["meta.train:y2"] = [1.0, 7.0]
# run against a data point and check result
prob["meta.x1"] = 2.0
prob["meta.x2"] = 3.0
prob.run()
assert_rel_error(self, prob["meta.y1"], 1.9085, 0.001)
assert_rel_error(self, prob["meta.y2"], 3.9203, 0.001)
# Add 3rd training point, moves the estimate for that point
# back to where it should be.
prob["meta.train:x1"] = [2.0]
prob["meta.train:x2"] = [3.0]
prob["meta.train:y1"] = [2.0]
prob["meta.train:y2"] = [4.0]
meta.train = True # currently need to tell meta to re-train
prob.run()
assert_rel_error(self, prob["meta.y1"], 2.0, 0.00001)
assert_rel_error(self, prob["meta.y2"], 4.0, 0.00001)
def test_sin_metamodel(self):
class Sin(Component):
""" Simple sine calculation. """
def __init__(self):
self.add_param('x', 0., units="rad")
self.add_output('f_x', 0.)
def solve_nonlinear(self, params, unknowns, resids):
unknowns['f_x'] = .5*sin(params['x'])
# create a MetaModel for Sin and add it to a Problem
sin_mm = MetaModel()
sin_mm.add_param('x', 0.)
sin_mm.add_output('f_x', 0.)
prob = Problem(Group())
prob.root.add('sin_mm', sin_mm)
# check that missing surrogate is detected in check_setup
stream = cStringIO()
prob.setup(out_stream=stream)
msg = ("No default surrogate model is defined and the "
"following outputs do not have a surrogate model:\n"
"['f_x']\n"
"Either specify a default_surrogate, or specify a "
"surrogate model for all outputs.")
self.assertTrue(msg in stream.getvalue())
# check that output with no specified surrogate gets the default
sin_mm.default_surrogate = FloatKrigingSurrogate()
prob.setup(check=False)
surrogate = prob.root.unknowns.metadata('sin_mm.f_x').get('surrogate')
self.assertTrue(isinstance(surrogate, FloatKrigingSurrogate),
'sin_mm.f_x should get the default surrogate')
# train the surrogate and check predicted value
prob['sin_mm.train:x'] = np.linspace(0,10,200)
prob['sin_mm.train:f_x'] = .5*np.sin(prob['sin_mm.train:x'])
prob['sin_mm.x'] = 2.22
prob.run()
self.assertAlmostEqual(prob['sin_mm.f_x'],
.5*np.sin(prob['sin_mm.x']),
places=5)
def test_sin_metamodel(self):
# create a MetaModel for Sin and add it to a Problem
sin_mm = MetaModel()
sin_mm.add_param('x', 0.)
sin_mm.add_output('f_x', 0.)
prob = Problem(Group())
prob.root.add('sin_mm', sin_mm)
# check that missing surrogate is detected in check_setup
stream = cStringIO()
prob.setup(out_stream=stream)
msg = ("No default surrogate model is defined and the "
"following outputs do not have a surrogate model:\n"
"['f_x']\n"
"Either specify a default_surrogate, or specify a "
"surrogate model for all outputs.")
self.assertTrue(msg in stream.getvalue())
# check that output with no specified surrogate gets the default
sin_mm.default_surrogate = FloatKrigingSurrogate()
prob.setup(check=False)
surrogate = prob.root.unknowns.metadata('sin_mm.f_x').get('surrogate')
self.assertTrue(isinstance(surrogate, FloatKrigingSurrogate),
'sin_mm.f_x should get the default surrogate')
# train the surrogate and check predicted value
prob['sin_mm.train:x'] = np.linspace(0, 10, 200)
prob['sin_mm.train:f_x'] = .5 * np.sin(prob['sin_mm.train:x'])
prob['sin_mm.x'] = 2.22
prob.run()
self.assertAlmostEqual(prob['sin_mm.f_x'],
.5 * np.sin(prob['sin_mm.x']),
places=5)
def test_array_outputs(self):
meta = MetaModel()
meta.add_param('x', np.zeros((2, 2)))
meta.add_output('y', np.zeros(2, ))
meta.default_surrogate = FloatKrigingSurrogate()
prob = Problem(Group())
prob.root.add('meta', meta)
prob.setup(check=False)
prob['meta.train:x'] = [[[1.0, 1.0], [1.0, 1.0]],
[[2.0, 1.0], [1.0, 1.0]],
[[1.0, 2.0], [1.0, 1.0]],
[[1.0, 1.0], [2.0, 1.0]],
[[1.0, 1.0], [1.0, 2.0]]]
prob['meta.train:y'] = [[3.0, 1.0], [2.0, 4.0], [1.0, 7.0],
[6.0, -3.0], [-2.0, 3.0]]
prob['meta.x'] = [[1.0, 2.0], [1.0, 1.0]]
prob.run()
assert_rel_error(self, prob['meta.y'], np.array([1.0, 7.0]), .00001)
def test_vector_inputs(self):
meta = MetaModel()
meta.add_param('x', np.zeros(4))
meta.add_output('y1', 0.)
meta.add_output('y2', 0.)
meta.default_surrogate = FloatKrigingSurrogate()
prob = Problem(Group())
prob.root.add('meta', meta)
prob.setup(check=False)
prob['meta.train:x'] = [[1.0, 1.0, 1.0, 1.0], [2.0, 1.0, 1.0, 1.0],
[1.0, 2.0, 1.0, 1.0], [1.0, 1.0, 2.0, 1.0],
[1.0, 1.0, 1.0, 2.0]]
prob['meta.train:y1'] = [3.0, 2.0, 1.0, 6.0, -2.0]
prob['meta.train:y2'] = [1.0, 4.0, 7.0, -3.0, 3.0]
prob['meta.x'] = [1.0, 2.0, 1.0, 1.0]
prob.run()
assert_rel_error(self, prob['meta.y1'], 1.0, .00001)
assert_rel_error(self, prob['meta.y2'], 7.0, .00001)
def test_sin_metamodel_obj_return(self):
# create a MetaModel for Sin and add it to a Problem
sin_mm = MetaModel()
sin_mm.add_param('x', 0.)
sin_mm.add_output('f_x', (0., 0.))
prob = Problem(Group())
prob.root.add('sin_mm', sin_mm)
# check that missing surrogate is detected in check_setup
stream = cStringIO()
prob.setup(out_stream=stream)
msg = ("No default surrogate model is defined and the "
"following outputs do not have a surrogate model:\n"
"['f_x']\n"
"Either specify a default_surrogate, or specify a "
"surrogate model for all outputs.")
self.assertTrue(msg in stream.getvalue())
# check that output with no specified surrogate gets the default
sin_mm.default_surrogate = KrigingSurrogate()
prob.setup(check=False)
surrogate = prob.root.unknowns.metadata('sin_mm.f_x').get('surrogate')
self.assertTrue(isinstance(surrogate, KrigingSurrogate),
'sin_mm.f_x should get the default surrogate')
# train the surrogate and check predicted value
prob['sin_mm.train:x'] = np.linspace(0, 10, 20)
prob['sin_mm.train:f_x'] = np.sin(prob['sin_mm.train:x'])
prob['sin_mm.x'] = 2.1
prob.run()
assert_rel_error(self, prob['sin_mm.f_x'][0], 0.86323233, 1e-4) # mean
self.assertTrue(self, prob['sin_mm.f_x'][1] < 1e-5) #std deviation
def test_derivatives(self):
meta = MetaModel()
meta.add_param('x', 0.)
meta.add_output('f', 0.)
meta.default_surrogate = FloatKrigingSurrogate()
prob = Problem(Group())
prob.root.add('meta', meta, promotes=['x'])
prob.root.add('p', ParamComp('x', 0.), promotes=['x'])
prob.setup(check=False)
prob['meta.train:x'] = [0., .25, .5, .75, 1.]
prob['meta.train:f'] = [1., .75, .5, .25, 0.]
prob['x'] = 0.125
prob.run()
stream = cStringIO()
prob.check_partial_derivatives(out_stream=stream)
abs_errors = findall('Absolute Error \(.+\) : (.+)', stream.getvalue())
self.assertTrue(len(abs_errors) > 0)
for match in abs_errors:
abs_error = float(match)
self.assertTrue(abs_error < 1e-6)
def test_sin_metamodel_obj_return(self):
# create a MetaModel for Sin and add it to a Problem
sin_mm = MetaModel()
sin_mm.add_param("x", 0.0)
sin_mm.add_output("f_x", (0.0, 0.0))
prob = Problem(Group())
prob.root.add("sin_mm", sin_mm)
# check that missing surrogate is detected in check_setup
stream = cStringIO()
prob.setup(out_stream=stream)
msg = (
"No default surrogate model is defined and the "
"following outputs do not have a surrogate model:\n"
"['f_x']\n"
"Either specify a default_surrogate, or specify a "
"surrogate model for all outputs."
)
self.assertTrue(msg in stream.getvalue())
# check that output with no specified surrogate gets the default
sin_mm.default_surrogate = KrigingSurrogate()
prob.setup(check=False)
surrogate = prob.root.unknowns.metadata("sin_mm.f_x").get("surrogate")
self.assertTrue(isinstance(surrogate, KrigingSurrogate), "sin_mm.f_x should get the default surrogate")
# train the surrogate and check predicted value
prob["sin_mm.train:x"] = np.linspace(0, 10, 20)
prob["sin_mm.train:f_x"] = np.sin(prob["sin_mm.train:x"])
prob["sin_mm.x"] = 2.1
prob.run()
assert_rel_error(self, prob["sin_mm.f_x"][0], 0.86323233, 1e-4) # mean
self.assertTrue(self, prob["sin_mm.f_x"][1] < 1e-5) # std deviation
def test_sin_metamodel(self):
# create a MetaModel for Sin and add it to a Problem
sin_mm = MetaModel()
sin_mm.add_param("x", 0.0)
sin_mm.add_output("f_x", 0.0)
prob = Problem(Group())
prob.root.add("sin_mm", sin_mm)
# check that missing surrogate is detected in check_setup
stream = cStringIO()
prob.setup(out_stream=stream)
msg = (
"No default surrogate model is defined and the "
"following outputs do not have a surrogate model:\n"
"['f_x']\n"
"Either specify a default_surrogate, or specify a "
"surrogate model for all outputs."
)
self.assertTrue(msg in stream.getvalue())
# check that output with no specified surrogate gets the default
sin_mm.default_surrogate = FloatKrigingSurrogate()
prob.setup(check=False)
surrogate = prob.root.unknowns.metadata("sin_mm.f_x").get("surrogate")
self.assertTrue(isinstance(surrogate, FloatKrigingSurrogate), "sin_mm.f_x should get the default surrogate")
# train the surrogate and check predicted value
prob["sin_mm.train:x"] = np.linspace(0, 10, 200)
prob["sin_mm.train:f_x"] = 0.5 * np.sin(prob["sin_mm.train:x"])
prob["sin_mm.x"] = 2.22
prob.run()
self.assertAlmostEqual(prob["sin_mm.f_x"], 0.5 * np.sin(prob["sin_mm.x"]), places=5)
def test_derivatives(self):
meta = MetaModel()
meta.add_param("x", 0.0)
meta.add_output("f", 0.0)
meta.default_surrogate = FloatKrigingSurrogate()
prob = Problem(Group())
prob.root.add("meta", meta, promotes=["x"])
prob.root.add("p", ParamComp("x", 0.0), promotes=["x"])
prob.setup(check=False)
prob["meta.train:x"] = [0.0, 0.25, 0.5, 0.75, 1.0]
prob["meta.train:f"] = [1.0, 0.75, 0.5, 0.25, 0.0]
prob["x"] = 0.125
prob.run()
stream = cStringIO()
prob.check_partial_derivatives(out_stream=stream)
abs_errors = findall("Absolute Error \(.+\) : (.+)", stream.getvalue())
self.assertTrue(len(abs_errors) > 0)
for match in abs_errors:
abs_error = float(match)
self.assertTrue(abs_error < 1e-6)
def test_derivatives(self):
meta = MetaModel()
meta.add_param('x', 0.)
meta.add_output('f', 0.)
meta.default_surrogate = FloatKrigingSurrogate()
prob = Problem(Group())
prob.root.add('meta', meta, promotes=['x'])
prob.root.add('p', ParamComp('x', 0.), promotes=['x'])
prob.setup(check=False)
prob['meta.train:x'] = [0., .25, .5, .75, 1.]
prob['meta.train:f'] = [1., .75, .5, .25, 0.]
prob['x'] = 0.125
prob.run()
stream = cStringIO()
prob.check_partial_derivatives(out_stream=stream)
abs_errors = findall('Absolute Error \(.+\) : (.+)', stream.getvalue())
self.assertTrue(len(abs_errors) > 0)
for match in abs_errors:
abs_error = float(match)
self.assertTrue(abs_error < 1e-6)
def test_basics(self):
# create a metamodel component
mm = MetaModel()
mm.add_param('x1', 0.)
mm.add_param('x2', 0.)
mm.add_output('y1', 0.)
mm.add_output('y2', 0., surrogate=FloatKrigingSurrogate())
mm.default_surrogate = ResponseSurface()
# add metamodel to a problem
prob = Problem(root=Group())
prob.root.add('mm', mm)
prob.setup(check=False)
# check that surrogates were properly assigned
surrogate = prob.root.unknowns.metadata('mm.y1').get('surrogate')
self.assertTrue(isinstance(surrogate, ResponseSurface))
surrogate = prob.root.unknowns.metadata('mm.y2').get('surrogate')
self.assertTrue(isinstance(surrogate, FloatKrigingSurrogate))
# populate training data
prob['mm.train:x1'] = [1.0, 2.0, 3.0]
prob['mm.train:x2'] = [1.0, 3.0, 4.0]
prob['mm.train:y1'] = [3.0, 2.0, 1.0]
prob['mm.train:y2'] = [1.0, 4.0, 7.0]
# run problem for provided data point and check prediction
prob['mm.x1'] = 2.0
prob['mm.x2'] = 3.0
self.assertTrue(mm.train) # training will occur before 1st run
prob.run()
assert_rel_error(self, prob['mm.y1'], 2.0, .00001)
assert_rel_error(self, prob['mm.y2'], 4.0, .00001)
# run problem for interpolated data point and check prediction
prob['mm.x1'] = 2.5
prob['mm.x2'] = 3.5
self.assertFalse(mm.train) # training will not occur before 2nd run
prob.run()
assert_rel_error(self, prob['mm.y1'], 1.5934, .001)
# change default surrogate, re-setup and check that metamodel re-trains
mm.default_surrogate = FloatKrigingSurrogate()
prob.setup(check=False)
surrogate = prob.root.unknowns.metadata('mm.y1').get('surrogate')
self.assertTrue(isinstance(surrogate, FloatKrigingSurrogate))
self.assertTrue(mm.train) # training will occur after re-setup
mm.warm_restart = True # use existing training data
prob['mm.x1'] = 2.5
prob['mm.x2'] = 3.5
prob.run()
assert_rel_error(self, prob['mm.y1'], 1.4609, .001)
def test_basics(self):
# create a metamodel component
mm = MetaModel()
mm.add_param("x1", 0.0)
mm.add_param("x2", 0.0)
mm.add_output("y1", 0.0)
mm.add_output("y2", 0.0, surrogate=FloatKrigingSurrogate())
mm.default_surrogate = ResponseSurface()
# add metamodel to a problem
prob = Problem(root=Group())
prob.root.add("mm", mm)
prob.setup(check=False)
# check that surrogates were properly assigned
surrogate = prob.root.unknowns.metadata("mm.y1").get("surrogate")
self.assertTrue(isinstance(surrogate, ResponseSurface))
surrogate = prob.root.unknowns.metadata("mm.y2").get("surrogate")
self.assertTrue(isinstance(surrogate, FloatKrigingSurrogate))
# populate training data
prob["mm.train:x1"] = [1.0, 2.0, 3.0]
prob["mm.train:x2"] = [1.0, 3.0, 4.0]
prob["mm.train:y1"] = [3.0, 2.0, 1.0]
prob["mm.train:y2"] = [1.0, 4.0, 7.0]
# run problem for provided data point and check prediction
prob["mm.x1"] = 2.0
prob["mm.x2"] = 3.0
self.assertTrue(mm.train) # training will occur before 1st run
prob.run()
assert_rel_error(self, prob["mm.y1"], 2.0, 0.00001)
assert_rel_error(self, prob["mm.y2"], 4.0, 0.00001)
# run problem for interpolated data point and check prediction
prob["mm.x1"] = 2.5
prob["mm.x2"] = 3.5
self.assertFalse(mm.train) # training will not occur before 2nd run
prob.run()
assert_rel_error(self, prob["mm.y1"], 1.5934, 0.001)
# change default surrogate, re-setup and check that metamodel re-trains
mm.default_surrogate = FloatKrigingSurrogate()
prob.setup(check=False)
surrogate = prob.root.unknowns.metadata("mm.y1").get("surrogate")
self.assertTrue(isinstance(surrogate, FloatKrigingSurrogate))
self.assertTrue(mm.train) # training will occur after re-setup
mm.warm_restart = True # use existing training data
prob["mm.x1"] = 2.5
prob["mm.x2"] = 3.5
prob.run()
assert_rel_error(self, prob["mm.y1"], 1.4609, 0.001)
