def test_single_input_parameter(self):
# Model
interp = om.MetaModelUnStructuredComp()
# Training Data
x_train = np.linspace(0, 10, 20)
y_train = np.linspace(0, 20, 20)
# Inputs
interp.add_input('simple_x', 0., training_data=x_train)
#Outputs
interp.add_output('cos_x', 0., training_data=.5 * np.cos(y_train))
# Surrogate Model
interp.options['default_surrogate'] = om.ResponseSurface()
prob = om.Problem()
prob.model.add_subsystem('interp', interp)
prob.setup()
with self.assertRaises(Exception) as context:
viz = MetaModelVisualization(interp)
msg = 'Must have more than one input value'
self.assertTrue(msg in str(context.exception))
def test_not_top_level_prob(self):
# Model
interp = om.MetaModelUnStructuredComp()
# Training Data
x_train1 = np.linspace(0, 10, 20)
x_train2 = np.linspace(0, 20, 20)
x_train3 = np.linspace(0, 30, 20)
x_train4 = np.linspace(0, 40, 20)
y_train = np.linspace(10, 20, 20)
# Inputs
interp.add_input('input_1', 0., training_data=x_train1)
interp.add_input('input_2', 0., training_data=x_train2)
interp.add_input('input_3', 0., training_data=x_train3)
interp.add_input('input_4', 0., training_data=x_train4)
# Outputs
interp.add_output('output_1', 0., training_data=.5 * np.cos(y_train))
interp.add_output('output_2', 0., training_data=.5 * np.sin(y_train))
# Surrogate Model
interp.options['default_surrogate'] = om.ResponseSurface()
prob = om.Problem(model=interp)
prob.setup()
prob.final_setup()
def setUp(self):
self.mm = mm = om.MetaModelUnStructuredComp()
filename = os.path.join(self.csv_dir, 'unstructured_data_points.csv')
# Training Data
x_train1 = np.genfromtxt(filename, delimiter=',', usecols=0)
x_train2 = np.genfromtxt(filename, delimiter=',', usecols=1)
x_train3 = np.genfromtxt(filename, delimiter=',', usecols=2)
y = np.sin(x_train1 * x_train2 * x_train3)
# Inputs
mm.add_input('input_1', 0., training_data=x_train1)
mm.add_input('input_2', 0., training_data=x_train2)
mm.add_input('input_3', 0., training_data=x_train3)
# Outputs
mm.add_output('output_1', 0., training_data=y)
# Surrogate Model
mm.options['default_surrogate'] = om.KrigingSurrogate()
prob = om.Problem()
prob.model.add_subsystem('mm', mm)
prob.setup()
prob.final_setup()
def test_make_predictions(self):
# Model
interp = om.MetaModelUnStructuredComp()
# Training Data
x_train = np.linspace(0, 10, 20)
y_train = np.linspace(10, 20, 20)
# Inputs
interp.add_input('simple_x', 0., training_data=x_train)
interp.add_input('sin_x', 0., training_data=x_train)
#Outputs
interp.add_output('cos_x', 0., training_data=.5 * np.cos(y_train))
# Surrogate Model
interp.options['default_surrogate'] = om.ResponseSurface()
prob = om.Problem()
prob.model.add_subsystem('interp', interp)
prob.setup()
viz = MetaModelVisualization(interp)
resolution = 50
data = dict({
'simple_x': np.array([np.random.rand(resolution**2, 1)]),
'sin_x': np.array([np.random.rand(resolution**2, 1)])
})
pred_array = viz._make_predictions(data)
self.assertTrue(pred_array.shape == (resolution**2, 1))
def test_training_point_array_for_nan_values(self):
# Model
interp = om.MetaModelUnStructuredComp()
# Training Data
x_train = np.linspace(0, 10, 20)
y_train = np.linspace(0, 20, 20)
# Inputs
interp.add_input('x', 0., training_data=x_train)
interp.add_input('y', 0., training_data=x_train)
#Outputs
interp.add_output('cos_x', 0., training_data=.5 * np.cos(y_train))
# Surrogate Model
interp.options['default_surrogate'] = om.ResponseSurface()
prob = om.Problem()
prob.model.add_subsystem('interp', interp)
prob.setup()
viz = MetaModelVisualization(interp)
training_points_output = viz._unstructured_training_points()
for i in range(0, 2):
self.assertFalse(np.any(np.isnan(training_points_output[:, i])))
def test_training_point_array_width(self):
# Model
interp = om.MetaModelUnStructuredComp()
# Training Data
x_train = np.linspace(0, 10, 20)
y_train = np.linspace(0, 20, 20)
# Inputs
interp.add_input('x', 0., training_data=x_train)
interp.add_input('y', 0., training_data=x_train)
#Outputs
interp.add_output('cos_x', 0., training_data=.5 * np.cos(y_train))
# Surrogate Model
interp.options['default_surrogate'] = om.ResponseSurface()
prob = om.Problem()
prob.model.add_subsystem('interp', interp)
prob.setup()
viz = MetaModelVisualization(interp)
training_points_output = viz._unstructured_training_points()
self.assertTrue(training_points_output.shape[1] == 2)
def test_missing_training_data_in_parameter(self):
# Model
interp = om.MetaModelUnStructuredComp()
# Training Data
x_train = np.linspace(0,10,20)
y_train = np.linspace(0,20,20)
# Inputs
interp.add_input('simple_x', 0., training_data=x_train)
interp.add_input('sin_x', 0.)
#Outputs
interp.add_output('cos_x', 0., training_data=.5*np.cos(y_train))
# Surrogate Model
interp.options['default_surrogate'] = om.ResponseSurface()
prob = om.Problem()
prob.model.add_subsystem('interp', interp)
prob.setup()
with self.assertRaises(Exception) as context:
viz = UnstructuredMetaModelVisualization(prob, interp)
msg = "No training data present for one or more parameters"
self.assertTrue(msg in str(context.exception))
def test_metamodel_feature_vector2d(self):
# similar to previous example, but processes 3 inputs/outputs at a time
import numpy as np
import openmdao.api as om
size = 3
# create a vectorized MetaModelUnStructuredComp for sine and cosine
trig = om.MetaModelUnStructuredComp(
vec_size=size, default_surrogate=om.KrigingSurrogate())
trig.add_input('x', np.zeros(size))
trig.add_output('y', np.zeros((size, 2)))
# add it to a Problem
prob = om.Problem()
prob.model.add_subsystem('trig', trig)
prob.setup()
# provide training data
trig.options['train:x'] = np.linspace(0, 10, 20)
trig.options['train:y'] = np.column_stack(
(.5 * np.sin(trig.options['train:x']),
.5 * np.cos(trig.options['train:x'])))
# train the surrogate and check predicted value
prob['trig.x'] = np.array([2.1, 3.2, 4.3])
prob.run_model()
assert_near_equal(
prob['trig.y'],
np.column_stack(
(.5 * np.sin(prob['trig.x']), .5 * np.cos(prob['trig.x']))),
1e-4)
def test_array_inputs(self):
mm = om.MetaModelUnStructuredComp()
mm.add_input('x', np.zeros((2, 2)))
mm.add_output('y1', 0.)
mm.add_output('y2', 0.)
mm.options['default_surrogate'] = om.KrigingSurrogate()
prob = om.Problem()
prob.model.add_subsystem('mm', mm)
prob.setup()
mm.options['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]]]
mm.options['train:y1'] = [3.0, 2.0, 1.0, 6.0, -2.0]
mm.options['train:y2'] = [1.0, 4.0, 7.0, -3.0, 3.0]
prob['mm.x'] = [[1.0, 2.0], [1.0, 1.0]]
prob.run_model()
assert_rel_error(self, prob['mm.y1'], 1.0, .00001)
assert_rel_error(self, prob['mm.y2'], 7.0, .00001)
def test_metamodel_feature_vector(self):
# Like simple sine example, but with input of length n instead of scalar
# The expected behavior is that the output is also of length n, with
# each one being an independent prediction.
# Its as if you stamped out n copies of metamodel, ran n scalars
# through its input, then muxed all those outputs into one contiguous
# array but you skip all the n-copies thing and do it all as an array
import numpy as np
import openmdao.api as om
size = 3
# create a vectorized MetaModelUnStructuredComp for sine
trig = om.MetaModelUnStructuredComp(
vec_size=size, default_surrogate=om.KrigingSurrogate())
trig.add_input('x', np.zeros(size))
trig.add_output('y', np.zeros(size))
# add it to a Problem
prob = om.Problem()
prob.model.add_subsystem('trig', trig)
prob.setup()
# provide training data
trig.options['train:x'] = np.linspace(0, 10, 20)
trig.options['train:y'] = .5 * np.sin(trig.options['train:x'])
# train the surrogate and check predicted value
prob['trig.x'] = np.array([2.1, 3.2, 4.3])
prob.run_model()
assert_near_equal(prob['trig.y'],
np.array(.5 * np.sin(prob['trig.x'])), 1e-4)
def test_nearest_neighbor(self):
import numpy as np
import openmdao.api as om
prob = om.Problem()
sin_mm = om.MetaModelUnStructuredComp()
sin_mm.add_input('x', 2.1)
sin_mm.add_output(
'f_x', 0., surrogate=om.NearestNeighbor(interpolant_type='linear'))
prob.model.add_subsystem('sin_mm', sin_mm)
prob.setup(check=True)
# train the surrogate and check predicted value
sin_mm.options['train:x'] = np.linspace(0, 10, 20)
sin_mm.options['train:f_x'] = .5 * np.sin(sin_mm.options['train:x'])
prob.set_val('sin_mm.x', 2.1)
prob.run_model()
assert_near_equal(prob.get_val('sin_mm.f_x'),
.5 * np.sin(prob.get_val('sin_mm.x')), 2e-3)
def test_surrogate_message_format(self):
prob = om.Problem()
prob.model.add_subsystem('p', om.IndepVarComp('x', 2.1))
sin_mm = om.MetaModelUnStructuredComp()
sin_mm.add_input('x', 0.)
sin_mm.add_output('f_x', 0., surrogate=om.KrigingSurrogate())
prob.model.add_subsystem('sin_mm', sin_mm)
prob.model.connect('p.x', 'sin_mm.x')
prob.setup(check=True)
# train the surrogate and check predicted value
sin_mm.options['train:x'] = np.linspace(0, 10, 1)
sin_mm.options['train:f_x'] = .5 * np.sin(sin_mm.options['train:x'])
prob['sin_mm.x'] = 2.1
with self.assertRaises(ValueError) as cm:
prob.run_model()
self.assertEqual(
str(cm.exception), 'sin_mm: KrigingSurrogate requires at least'
' 2 training points.')
def test_response_surface(self):
import numpy as np
import openmdao.api as om
prob = om.Problem()
prob.model.add_subsystem('p', om.IndepVarComp('x', 2.1))
sin_mm = om.MetaModelUnStructuredComp()
sin_mm.add_input('x', 0.)
sin_mm.add_output('f_x', 0., surrogate=om.ResponseSurface())
prob.model.add_subsystem('sin_mm', sin_mm)
prob.model.connect('p.x', 'sin_mm.x')
prob.setup(check=True)
# train the surrogate and check predicted value
sin_mm.options['train:x'] = np.linspace(0, 3.14, 20)
sin_mm.options['train:f_x'] = .5 * np.sin(sin_mm.options['train:x'])
prob['sin_mm.x'] = 2.1
prob.run_model()
assert_near_equal(prob['sin_mm.f_x'], .5 * np.sin(prob['sin_mm.x']),
2e-3)
def test_vectorized(self):
size = 3
# create a vectorized MetaModelUnStructuredComp for sine
trig = om.MetaModelUnStructuredComp(
vec_size=size, default_surrogate=om.KrigingSurrogate())
trig.add_input('x', np.zeros(size))
trig.add_output('y', np.zeros(size))
# add it to a Problem
prob = om.Problem()
prob.model.add_subsystem('trig', trig)
prob.setup()
# provide training data
trig.options['train_x'] = np.linspace(0, 10, 20)
trig.options['train_y'] = .5 * np.sin(trig.options['train_x'])
# train the surrogate and check predicted value
prob['trig.x'] = np.array([2.1, 3.2, 4.3])
prob.run_model()
assert_near_equal(prob['trig.y'],
np.array(.5 * np.sin(prob['trig.x'])), 1e-4)
data = prob.check_partials(out_stream=None)
assert_check_partials(data, atol=1e-6, rtol=1e-6)
def test_derivatives(self):
mm = om.MetaModelUnStructuredComp()
mm.add_input('x', 0.)
mm.add_output('f', 0.)
mm.options['default_surrogate'] = om.KrigingSurrogate()
prob = om.Problem()
prob.model.add_subsystem('p',
om.IndepVarComp('x', 0.),
promotes_outputs=['x'])
prob.model.add_subsystem('mm', mm, promotes_inputs=['x'])
prob.setup()
mm.options['train:x'] = [0., .25, .5, .75, 1.]
mm.options['train:f'] = [1., .75, .5, .25, 0.]
prob['x'] = 0.125
prob.run_model()
data = prob.check_partials(out_stream=None)
Jf = data['mm'][('f', 'x')]['J_fwd']
assert_near_equal(Jf[0][0], -1., 1.e-3)
assert_check_partials(data, atol=1e-6, rtol=1e-6)
# Complex step
prob.setup(force_alloc_complex=True)
prob.model.mm.set_check_partial_options(wrt='*', method='cs')
data = prob.check_partials(out_stream=None)
assert_check_partials(data, atol=1e-11, rtol=1e-11)
def test_two_vector_inputs(self):
mm = om.MetaModelUnStructuredComp()
mm.add_input('x1', np.zeros(4))
mm.add_input('x2', np.zeros(4))
mm.add_output('y1', 0.)
mm.add_output('y2', 0.)
mm.options['default_surrogate'] = om.KrigingSurrogate()
prob = om.Problem()
prob.model.add_subsystem('mm', mm)
prob.setup()
mm.options['train_x1'] = [[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]]
mm.options['train_x2'] = [[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]]
mm.options['train_y1'] = [3.0, 2.0, 1.0, 6.0, -2.0]
mm.options['train_y2'] = [1.0, 4.0, 7.0, -3.0, 3.0]
prob['mm.x1'] = [1.0, 2.0, 1.0, 1.0]
prob['mm.x2'] = [1.0, 2.0, 1.0, 1.0]
prob.run_model()
assert_near_equal(prob['mm.y1'], 1.0, .00001)
assert_near_equal(prob['mm.y2'], 7.0, .00001)
def test_nearest_neighbor_rbf_options(self):
prob = om.Problem()
sin_mm = om.MetaModelUnStructuredComp()
sin_mm.add_input('x', 2.1)
sin_mm.add_output('f_x',
0.,
surrogate=om.NearestNeighbor(interpolant_type='rbf',
num_neighbors=3))
prob.model.add_subsystem('sin_mm', sin_mm)
prob.setup(check=True)
# train the surrogate and check predicted value
sin_mm.options['train_x'] = np.linspace(0, 10, 20)
sin_mm.options['train_f_x'] = .5 * np.sin(sin_mm.options['train_x'])
prob.set_val('sin_mm.x', 2.1)
prob.run_model()
assert_near_equal(prob.get_val('sin_mm.f_x'),
.5 * np.sin(prob.get_val('sin_mm.x')), 5e-3)
def test_kriging_options_eval_rmse(self):
prob = om.Problem()
sin_mm = om.MetaModelUnStructuredComp()
sin_mm.add_input('x', 2.1)
sin_mm.add_output('f_x',
0.,
surrogate=om.KrigingSurrogate(eval_rmse=True))
prob.model.add_subsystem('sin_mm', sin_mm)
prob.setup(check=True)
# train the surrogate and check predicted value
sin_mm.options['train_x'] = np.linspace(0, 10, 20)
sin_mm.options['train_f_x'] = .5 * np.sin(sin_mm.options['train_x'])
prob.set_val('sin_mm.x', 2.1)
prob.run_model()
print("mean")
assert_near_equal(prob.get_val('sin_mm.f_x'),
.5 * np.sin(prob.get_val('sin_mm.x')), 1e-4)
print("std")
assert_near_equal(sin_mm._metadata('f_x')['rmse'][0, 0], 0.0, 1e-4)
def test_sin_metamodel_rmse(self):
# create MetaModelUnStructuredComp with Kriging, using the rmse option
sin_mm = om.MetaModelUnStructuredComp()
sin_mm.add_input('x', 0.)
sin_mm.add_output('f_x', 0.)
sin_mm.options['default_surrogate'] = om.KrigingSurrogate(
eval_rmse=True)
# add it to a Problem
prob = om.Problem()
prob.model.add_subsystem('sin_mm', sin_mm)
prob.setup()
# train the surrogate and check predicted value
sin_mm.options['train:x'] = np.linspace(0, 10, 20)
sin_mm.options['train:f_x'] = np.sin(sin_mm.options['train:x'])
prob['sin_mm.x'] = 2.1
prob.run_model()
assert_near_equal(prob['sin_mm.f_x'], np.sin(2.1), 1e-4) # mean
self.assertTrue(
self,
sin_mm._metadata('f_x')['rmse'] < 1e-5) # std deviation
def test_kriging_options_eval_rmse(self):
import numpy as np
import openmdao.api as om
prob = om.Problem()
prob.model.add_subsystem('p', om.IndepVarComp('x', 2.1))
sin_mm = om.MetaModelUnStructuredComp()
sin_mm.add_input('x', 0.)
sin_mm.add_output('f_x',
0.,
surrogate=om.KrigingSurrogate(eval_rmse=True))
prob.model.add_subsystem('sin_mm', sin_mm)
prob.model.connect('p.x', 'sin_mm.x')
prob.setup(check=True)
# train the surrogate and check predicted value
sin_mm.options['train:x'] = np.linspace(0, 10, 20)
sin_mm.options['train:f_x'] = .5 * np.sin(sin_mm.options['train:x'])
prob['sin_mm.x'] = 2.1
prob.run_model()
print("mean")
assert_near_equal(prob['sin_mm.f_x'], .5 * np.sin(prob['sin_mm.x']),
1e-4)
print("std")
assert_near_equal(sin_mm._metadata('f_x')['rmse'][0, 0], 0.0, 1e-4)
def test_vectorized_kriging(self):
# Test for coverage (handling the rmse)
size = 3
# create a vectorized MetaModelUnStructuredComp for sine
trig = om.MetaModelUnStructuredComp(
vec_size=size,
default_surrogate=om.KrigingSurrogate(eval_rmse=True))
trig.add_input('x', np.zeros(size))
trig.add_output('y', np.zeros(size))
# add it to a Problem
prob = om.Problem()
prob.model.add_subsystem('trig', trig)
prob.setup()
# provide training data
trig.options['train:x'] = np.linspace(0, 10, 20)
trig.options['train:y'] = .5 * np.sin(trig.options['train:x'])
# train the surrogate and check predicted value
prob['trig.x'] = np.array([2.1, 3.2, 4.3])
prob.run_model()
assert_near_equal(prob['trig.y'],
np.array(.5 * np.sin(prob['trig.x'])), 1e-4)
self.assertEqual(len(prob.model.trig._metadata('y')['rmse']), 3)
def test_vectorized(self):
size = 3
# create a vectorized MetaModelUnStructuredComp for sine
trig = om.MetaModelUnStructuredComp(
vec_size=size, default_surrogate=om.KrigingSurrogate())
trig.add_input('x', np.zeros(size))
trig.add_output('y', np.zeros(size))
# add it to a Problem
prob = om.Problem()
prob.model.add_subsystem('trig', trig)
prob.setup()
# provide training data
trig.options['train:x'] = np.linspace(0, 10, 20)
trig.options['train:y'] = .5 * np.sin(trig.options['train:x'])
# train the surrogate and check predicted value
prob['trig.x'] = np.array([2.1, 3.2, 4.3])
prob.run_model()
assert_near_equal(prob['trig.y'],
np.array(.5 * np.sin(prob['trig.x'])), 1e-4)
data = prob.check_partials(out_stream=None)
abs_errors = data['trig'][('y', 'x')]['abs error']
self.assertTrue(len(abs_errors) > 0)
for match in abs_errors:
abs_error = float(match)
self.assertTrue(abs_error < 1.e-6)
def test_nearest_neighbor_rbf_options(self):
import numpy as np
import openmdao.api as om
prob = om.Problem()
prob.model.add_subsystem('p', om.IndepVarComp('x', 2.1))
sin_mm = om.MetaModelUnStructuredComp()
sin_mm.add_input('x', 0.)
sin_mm.add_output('f_x',
0.,
surrogate=om.NearestNeighbor(interpolant_type='rbf',
num_neighbors=3))
prob.model.add_subsystem('sin_mm', sin_mm)
prob.model.connect('p.x', 'sin_mm.x')
prob.setup(check=True)
# train the surrogate and check predicted value
sin_mm.options['train:x'] = np.linspace(0, 10, 20)
sin_mm.options['train:f_x'] = .5 * np.sin(sin_mm.options['train:x'])
prob['sin_mm.x'] = 2.1
prob.run_model()
assert_near_equal(prob['sin_mm.f_x'], .5 * np.sin(prob['sin_mm.x']),
5e-3)
def test_metamodel_feature2d(self):
# similar to previous example, but output is 2d
import numpy as np
import openmdao.api as om
# create a MetaModelUnStructuredComp that predicts sine and cosine as an array
trig = om.MetaModelUnStructuredComp(
default_surrogate=om.KrigingSurrogate())
trig.add_input('x', 0)
trig.add_output('y', np.zeros(2))
# add it to a Problem
prob = om.Problem()
prob.model.add_subsystem('trig', trig)
prob.setup()
# provide training data
trig.options['train:x'] = np.linspace(0, 10, 20)
trig.options['train:y'] = np.column_stack(
(.5 * np.sin(trig.options['train:x']),
.5 * np.cos(trig.options['train:x'])))
# train the surrogate and check predicted value
prob['trig.x'] = 2.1
prob.run_model()
assert_near_equal(
prob['trig.y'],
np.append(.5 * np.sin(prob['trig.x']),
.5 * np.cos(prob['trig.x'])), 1e-4)
def test_metamodel_feature(self):
# create a MetaModelUnStructuredComp, specifying surrogates for the outputs
import numpy as np
import openmdao.api as om
trig = om.MetaModelUnStructuredComp()
x_train = np.linspace(0, 10, 20)
trig.add_input('x', 0., training_data=x_train)
trig.add_output('sin_x',
0.,
training_data=.5 * np.sin(x_train),
surrogate=om.KrigingSurrogate())
trig.add_output('cos_x', 0., training_data=.5 * np.cos(x_train))
trig.options['default_surrogate'] = om.KrigingSurrogate()
# add it to a Problem, run and check the predicted values
prob = om.Problem()
prob.model.add_subsystem('trig', trig)
prob.setup()
prob['trig.x'] = 2.1
prob.run_model()
assert_near_equal(prob['trig.sin_x'], .5 * np.sin(prob['trig.x']),
1e-4)
assert_near_equal(prob['trig.cos_x'], .5 * np.cos(prob['trig.x']),
1e-4)
def test_unequal_training_outputs(self):
mm = om.MetaModelUnStructuredComp()
mm.add_input('x', 0.)
mm.add_input('y', 0.)
mm.add_output('f', 0.)
mm.options['default_surrogate'] = om.KrigingSurrogate()
prob = om.Problem()
prob.model.add_subsystem('mm', mm)
prob.setup()
mm.options['train:x'] = [1.0, 1.0, 1.0, 1.0]
mm.options['train:y'] = [1.0, 2.0, 3.0, 4.0]
mm.options['train:f'] = [1.0, 1.0]
prob['mm.x'] = 1.0
prob['mm.y'] = 1.0
with self.assertRaises(RuntimeError) as cm:
prob.run_model()
expected = (
"MetaModelUnStructuredComp (mm): 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_2darray_outputs(self):
mm = om.MetaModelUnStructuredComp()
mm.add_input('x', np.zeros((2, 2)))
mm.add_output('y', np.zeros((2, 2)))
mm.options['default_surrogate'] = om.KrigingSurrogate()
prob = om.Problem()
prob.model.add_subsystem('mm', mm)
prob.setup()
mm.options['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]]]
mm.options['train:y'] = [[[3.0, 1.0], [3.0, 1.0]],
[[2.0, 4.0], [2.0, 4.0]],
[[1.0, 7.0], [1.0, 7.0]],
[[6.0, -3.0], [6.0, -3.0]],
[[-2.0, 3.0], [-2.0, 3.0]]]
prob['mm.x'] = [[1.0, 2.0], [1.0, 1.0]]
prob.run_model()
assert_near_equal(prob['mm.y'], np.array([[1.0, 7.0], [1.0, 7.0]]),
.00001)
def test_basics(self):
# create a metamodel component
mm = om.MetaModelUnStructuredComp()
mm.add_input('x1', 0.)
mm.add_input('x2', 0.)
mm.add_output('y1', 0.)
mm.add_output('y2', 0., surrogate=om.KrigingSurrogate())
mm.options['default_surrogate'] = om.ResponseSurface()
# add metamodel to a problem
prob = om.Problem()
prob.model.add_subsystem('mm', mm)
prob.setup()
# check that surrogates were properly assigned
surrogate = mm._metadata('y1').get('surrogate')
self.assertTrue(isinstance(surrogate, om.ResponseSurface))
surrogate = mm._metadata('y2').get('surrogate')
self.assertTrue(isinstance(surrogate, om.KrigingSurrogate))
# populate training data
mm.options['train:x1'] = [1.0, 2.0, 3.0]
mm.options['train:x2'] = [1.0, 3.0, 4.0]
mm.options['train:y1'] = [3.0, 2.0, 1.0]
mm.options['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_model()
assert_near_equal(prob['mm.y1'], 2.0, .00001)
assert_near_equal(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_model()
assert_near_equal(prob['mm.y1'], 1.5934, .001)
# change default surrogate, re-setup and check that metamodel re-trains
mm.options['default_surrogate'] = om.KrigingSurrogate()
prob.setup()
surrogate = mm._metadata('y1').get('surrogate')
self.assertTrue(isinstance(surrogate, om.KrigingSurrogate))
self.assertTrue(mm.train) # training will occur after re-setup
def test_derivatives_vectorized_multiD(self):
vec_size = 5
mm = om.MetaModelUnStructuredComp(vec_size=vec_size)
mm.add_input('x', np.zeros((vec_size, 2, 3)))
mm.add_input('xx', np.zeros((vec_size, 1)))
mm.add_output('y', np.zeros((vec_size, 4, 2)))
mm.options['default_surrogate'] = om.KrigingSurrogate()
prob = om.Problem()
prob.model.add_subsystem('mm', mm)
prob.setup()
mm.options['train_x'] = [[[1.0, 2.0, 1.0], [1.0, 2.0, 1.0]],
[[2.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
[[1.0, 1.0, 2.0], [1.0, 2.0, 1.0]],
[[1.0, 1.0, 1.0], [2.0, 1.0, 1.0]],
[[1.0, 2.0, 1.0], [1.0, 2.0, 2.0]]]
mm.options['train_xx'] = [1.0, 2.0, 1.0, 1.0, 2.0]
mm.options['train_y'] = [[[30.0, 10.0], [30.0, 25.0], [50.0, 10.7],
[15.0, 25.7]],
[[20.0, 40.0], [20.0, 40.0], [80.0, 30.3],
[12.0, 20.7]],
[[10.0, 70.0], [10.0, 70.0], [20.0, 10.9],
[13.0, 15.7]],
[[60.0, -30.0], [60.0, -30.0], [50.0, 50.5],
[14.0, 10.7]],
[[-20.0, 30.0], [-20.0, 30.0], [20.2, 10.0],
[15.0, 60.7]]]
prob['mm.x'] = [[[1.3, 1.3, 1.3], [1.5, 1.5, 1.5]],
[[1.4, 1.4, 1.4], [1.5, 1.5, 1.5]],
[[1.5, 1.5, 1.5], [1.5, 1.5, 1.5]],
[[1.5, 1.5, 1.5], [1.4, 1.4, 1.4]],
[[1.5, 1.5, 1.5], [1.3, 1.3, 1.3]]]
prob['mm.xx'] = [[1.4], [1.5], [1.6], [1.5], [1.4]]
prob.run_model()
data = prob.check_partials(out_stream=None)
assert_check_partials(data, atol=1e-5, rtol=1e-5)
# Complex step
prob.setup(force_alloc_complex=True)
prob.model.mm.set_check_partial_options(wrt='*', method='cs')
data = prob.check_partials(out_stream=None)
assert_check_partials(data, atol=1e-11, rtol=1e-11)
def test_metamodel_vector_errors(self):
# first dimension of all inputs/outputs must be 3
mm = om.MetaModelUnStructuredComp(vec_size=3)
with self.assertRaises(RuntimeError) as cm:
mm.add_input('x', np.zeros(2))
self.assertEqual(str(cm.exception),
"<class MetaModelUnStructuredComp>: First dimension of input 'x' must be 3")
with self.assertRaises(RuntimeError) as cm:
mm.add_output('y', np.zeros(4))
self.assertEqual(str(cm.exception),
"<class MetaModelUnStructuredComp>: First dimension of output 'y' must be 3")