def test_one_dim_bi_fidelity_training(self):
mm = MultiFiMetaModelUnStructuredComp(nfi=2)
surr = MockSurrogate()
mm.add_input('x', 0.)
mm.add_output('y', 0., surrogate=surr)
prob = Problem(Group())
prob.model.add_subsystem('mm', mm)
prob.setup(check=False)
mm.options['train:x']= [0.0, 0.4, 1.0]
mm.options['train:x_fi2'] = [0.1, 0.2, 0.3, 0.5, 0.6,
0.7, 0.8, 0.9, 0.0, 0.4, 1.0]
mm.options['train:y'] = [3.02720998, 0.11477697, 15.82973195]
mm.options['train:y_fi2'] = [-9.32828839, -8.31986355, -7.00778837,
-4.54535129, -4.0747189 , -5.30287702,
-4.47456522, 1.85597517, -8.48639501,
-5.94261151, 7.91486597]
expected_xtrain=[np.array([[0.0], [0.4], [1.0]]),
np.array([[0.1], [0.2], [0.3], [0.5], [0.6], [0.7],
[0.8], [0.9], [0.0], [0.4], [1.0]])]
expected_ytrain=[np.array([[ 3.02720998], [0.11477697], [15.82973195]]),
np.array([[-9.32828839], [-8.31986355], [-7.00778837], [-4.54535129],
[-4.0747189], [-5.30287702], [-4.47456522], [1.85597517],
[-8.48639501], [-5.94261151], [7.91486597]])]
prob.run_model()
np.testing.assert_array_equal(surr.xtrain[0], expected_xtrain[0])
np.testing.assert_array_equal(surr.xtrain[1], expected_xtrain[1])
np.testing.assert_array_equal(surr.ytrain[0], expected_ytrain[0])
np.testing.assert_array_equal(surr.ytrain[1], expected_ytrain[1])
def test_one_dim_one_fidelity_training_run_setup_twice(self):
mm = MultiFiMetaModelUnStructuredComp()
surr = MockSurrogate()
mm.add_input('x', 0.)
mm.add_output('y', 0., surrogate=surr)
prob = Problem(Group())
prob.model.add_subsystem('mm', mm)
prob.setup(check=False)
mm.options['train:x'] = [0.0, 0.4, 1.0]
mm.options['train:y'] = [3.02720998, 0.11477697, 15.82973195]
expected_xtrain=[np.array([[0.0], [0.4], [1.0]])]
expected_ytrain=[np.array([[3.02720998], [0.11477697], [15.82973195]])]
prob.run_model()
np.testing.assert_array_equal(surr.xtrain, expected_xtrain)
np.testing.assert_array_equal(surr.ytrain, expected_ytrain)
expected_xpredict=0.5
prob['mm.x'] = expected_xpredict
prob.run_model()
np.testing.assert_array_equal(surr.xpredict, expected_xpredict)
# Setup and run second time
prob.setup(check=False)
expected_xpredict=0.5
prob['mm.x'] = expected_xpredict
prob.run_model()
np.testing.assert_array_equal(surr.xpredict, expected_xpredict)
def test_array_multi_vectorize(self):
def branin(x):
x1 = 15 * x[0] - 5
x2 = 15 * x[1]
return (x2 - (5.1 / (4. * np.pi**2.)) * x1**2. + 5. * x1 / np.pi -
6.)**2. + 10. * (1. - 1. / (8. * np.pi)) * np.cos(x1) + 10.
# Add a linear error
def branin_low_fidelity(x):
return branin(x) + 30. * x[1] + 10.
mm = MultiFiMetaModelUnStructuredComp(nfi=2)
mm.add_input('x', np.zeros((1, 2)))
mm.add_output('y', np.zeros((1, )))
mm.options['default_surrogate'] = MultiFiCoKrigingSurrogate(
normalize=False)
prob = Problem()
prob.model.add_subsystem('mm', mm)
prob.setup()
x = [
[
[0.13073587, 0.24909577], # expensive (hifi) doe
[0.91915571, 0.4735261],
[0.75830543, 0.13321705],
[0.51760477, 0.34594101],
[0.03531219, 0.77765831],
[0.27249206, 0.5306115],
[0.62762489, 0.65778471],
[0.3914706, 0.09852519],
[0.86565585, 0.85350002],
[0.40806563, 0.91465314]
],
[
[0.91430235, 0.17029894], # cheap (lowfi) doe
[0.99329651, 0.76431519],
[0.2012252, 0.35006032],
[0.61707854, 0.90210676],
[0.15113004, 0.0133355],
[0.07108082, 0.55344447],
[0.4483159, 0.52182902],
[0.5926638, 0.06595122],
[0.66305449, 0.48579608],
[0.47965045, 0.7407793],
[0.13073587, 0.24909577], # notice hifi doe inclusion
[0.91915571, 0.4735261],
[0.75830543, 0.13321705],
[0.51760477, 0.34594101],
[0.03531219, 0.77765831],
[0.27249206, 0.5306115],
[0.62762489, 0.65778471],
[0.3914706, 0.09852519],
[0.86565585, 0.85350002],
[0.40806563, 0.91465314]
]
]
y = np.array([[branin(case) for case in x[0]],
[branin_low_fidelity(case) for case in x[1]]])
def test_inputs_wrt_nfidelity(self):
mm = MultiFiMetaModelUnStructuredComp(nfi=3)
mm.add_input('x', 0.)
mm.add_output('y', 0.)
prob = Problem(Group())
prob.model.add_subsystem('mm', mm)
prob.setup(check=False)
self.assertEqual(mm.options['train:x'], None)
self.assertEqual(mm.options['train:x_fi2'], None)
self.assertEqual(mm.options['train:x_fi3'], None)
self.assertEqual(mm.options['train:y'], None)
self.assertEqual(mm.options['train:y_fi2'], None)
self.assertEqual(mm.options['train:y_fi3'], None)
def test_two_dim_bi_fidelity_training(self):
mm = MultiFiMetaModelUnStructuredComp(nfi=2)
surr_y1 = MockSurrogate()
surr_y2 = MockSurrogate()
mm.add_input('x1', 0.)
mm.add_input('x2', 0.)
mm.add_output('y1', 0., surrogate=surr_y1)
mm.add_output('y2', 0., surrogate=surr_y2)
prob = Problem(Group())
prob.model.add_subsystem('mm', mm)
prob.setup()
mm.options['train:x1'] = [1.0, 2.0, 3.0]
mm.options['train:x1_fi2'] = [1.1, 2.1, 3.1, 1.0, 2.0, 3.0]
mm.options['train:x2'] = [1.0, 2.0, 3.0]
mm.options['train:x2_fi2'] = [2.1, 2.2, 2.3, 1.0, 2.0, 3.0]
mm.options['train:y1'] = [0.0, 0.1, 0.2]
mm.options['train:y1_fi2'] = [3.0, 3.1, 3.3, 3.4, 3.5, 3.6]
mm.options['train:y2'] = [4.0, 4.0, 4.0]
mm.options['train:y2_fi2'] = [4.0, 4.1, 4.3, 4.4, 4.5, 4.6]
prob.run_model()
expected_xtrain = [
np.array([[1.0, 1.0], [2.0, 2.0], [3.0, 3.0]]),
np.array([[1.1, 2.1], [2.1, 2.2], [3.1, 2.3], [1.0, 1.0],
[2.0, 2.0], [3.0, 3.0]])
]
expected_y1train = [
np.array([[0.0], [0.1], [0.2]]),
np.array([[3.0], [3.1], [3.3], [3.4], [3.5], [3.6]])
]
expected_y2train = [
np.array([[4.0], [4.0], [4.0]]),
np.array([[4.0], [4.1], [4.3], [4.4], [4.5], [4.6]])
]
np.testing.assert_array_equal(surr_y1.ytrain[0], expected_y1train[0])
np.testing.assert_array_equal(surr_y1.ytrain[1], expected_y1train[1])
np.testing.assert_array_equal(surr_y2.ytrain[0], expected_y2train[0])
np.testing.assert_array_equal(surr_y2.ytrain[1], expected_y2train[1])
np.testing.assert_array_equal(surr_y1.ytrain[0], expected_y1train[0])
np.testing.assert_array_equal(surr_y1.ytrain[1], expected_y1train[1])
np.testing.assert_array_equal(surr_y2.ytrain[0], expected_y2train[0])
np.testing.assert_array_equal(surr_y2.ytrain[1], expected_y2train[1])
def test_error_messages(self):
mm = MultiFiMetaModelUnStructuredComp(nfi=3)
mm.add_input('x', 0.)
mm.add_output('y', 0.)
prob = Problem(Group())
prob.model.add_subsystem('mm', mm)
prob.setup(check=False)
mm.options['train:x'] = [0.0, 0.4, 1.0]
mm.options['train:y'] = [3.02720998, 0.11477697, 15.82973195]
mm.options['train:x_fi2'] = [0.0, 0.4, 1.0]
mm.options['train:y_fi2'] = [3.02720998, 0.11477697, 15.82973195]
mm.options['train:x_fi3'] = [0.0, 0.4, 1.0]
mm.options['train:y_fi3'] = [3.02720998, 0.11477697, 15.82973195]
with self.assertRaises(RuntimeError) as cm:
prob.run_model()
msg = ("MultiFiMetaModelUnStructured 'mm': No surrogate specified for output 'y'")
self.assertEqual(str(cm.exception), msg)
# Wrong number of input samples
mm = MultiFiMetaModelUnStructuredComp(nfi=3)
mm.add_input('x', 0.)
mm.add_input('x2', 0.)
mm.add_output('y', 0.)
prob = Problem(Group())
prob.model.add_subsystem('mm', mm)
prob.setup(check=False)
mm.options['train:x'] = [0.0, 0.4, 1.0]
mm.options['train:x2'] = [0.0, 0.4, 1.0, 999.0]
mm.options['train:y'] = [3.02720998, 0.11477697, 15.82973195]
mm.options['train:x_fi2'] = [0.0, 0.4, 1.0]
mm.options['train:x2_fi2'] = [0.0, 0.4, 1.0]
mm.options['train:y_fi2'] = [3.02720998, 0.11477697, 15.82973195]
mm.options['train:x_fi3'] = [0.0, 0.4, 1.0]
mm.options['train:x2_fi3'] = [0.0, 0.4, 1.0]
mm.options['train:y_fi3'] = [3.02720998, 0.11477697, 15.82973195]
with self.assertRaises(RuntimeError) as cm:
prob.run_model()
msg = ("MultiFiMetaModelUnStructured: Each variable must have the same number of training points. Expected 3 but found 4 points for 'x2'.")
self.assertEqual(str(cm.exception), msg)
# Wrong number of output samples
mm = MultiFiMetaModelUnStructuredComp(nfi=3)
mm.add_input('x', 0.)
mm.add_output('y', 0.)
mm.add_output('y2', 0.)
prob = Problem(Group())
prob.model.add_subsystem('mm', mm)
prob.setup(check=False)
mm.options['train:x'] = [0.0, 0.4, 1.0]
mm.options['train:y'] = [3.02720998, 0.11477697, 15.82973195]
mm.options['train:y'] = [3.02720998, 0.11477697, 15.82973195, 3.14159]
mm.options['train:x_fi2'] = [0.0, 0.4, 1.0]
mm.options['train:y_fi2'] = [3.02720998, 0.11477697, 15.82973195]
mm.options['train:y_fi2'] = [3.02720998, 0.11477697, 15.82973195]
mm.options['train:x_fi3'] = [0.0, 0.4, 1.0]
mm.options['train:y_fi3'] = [3.02720998, 0.11477697, 15.82973195]
mm.options['train:y_fi3'] = [3.02720998, 0.11477697, 15.82973195]
with self.assertRaises(RuntimeError) as cm:
prob.run_model()
msg = ("MultiFiMetaModelUnStructured: Each variable must have the same number of training points. Expected 3 but found 4 points for 'y'.")
self.assertEqual(str(cm.exception), msg)
mm.options['train:x_fi2'] = x[1]
mm.options['train:y_fi2'] = y[1]
prob['mm.x'] = np.array([[2./3., 1./3.]])
prob.run_model()
assert_rel_error(self, prob['mm.y'], 26.26, tolerance=0.02)
prob['mm.x'] = np.array([[1./3., 2./3.]])
prob.run_model()
assert_rel_error(self, prob['mm.y'], 36.1031735, tolerance=0.02)
# Now, vectorized model with both points predicted together.
mm = MultiFiMetaModelUnStructuredComp(nfi=2, vec_size=2)
mm.add_input('x', np.zeros((2, 1, 2)))
mm.add_output('y', np.zeros((2, 1, )))
mm.options['default_surrogate'] = MultiFiCoKrigingSurrogate()
prob = Problem()
prob.model.add_subsystem('mm', mm)
prob.setup(check=False)
mm.options['train:x'] = x[0]
mm.options['train:y'] = y[0]
mm.options['train:x_fi2'] = x[1]
mm.options['train:y_fi2'] = y[1]
prob['mm.x'] = np.array([[[2./3., 1./3.]], [[1./3., 2./3.]]])