def test_one_dim_bi_fidelity_training(self):
mm = MultiFiMetaModel(nfi=2)
mm.add_param('x', 0.)
surr = MockSurrogate()
mm.add_output('y', 0., surrogate = surr)
prob = Problem(Group())
prob.root.add('mm', mm)
prob.setup(check=False)
prob['mm.train:x']= [0.0, 0.4, 1.0]
prob['mm.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]
prob['mm.train:y'] = [3.02720998, 0.11477697, 15.82973195]
prob['mm.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()
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(self):
mm = MultiFiMetaModel()
mm.add_param('x', 0.)
surr = MockSurrogate()
mm.add_output('y', 0., surrogate = surr)
prob = Problem(Group())
prob.root.add('mm', mm)
prob.setup(check=False)
prob['mm.train:x'] = [0.0, 0.4, 1.0]
prob['mm.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()
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()
np.testing.assert_array_equal(surr.xpredict, expected_xpredict)
def test_one_dim_one_fidelity_training(self):
mm = MultiFiMetaModel()
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.metadata['train:x'] = [0.0, 0.4, 1.0]
mm.metadata['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)
def __init__(self, surrogate, nfi):
super(Simulation, self).__init__()
self.surrogate = surrogate
mm = self.add("mm", MultiFiMetaModel(nfi=nfi))
mm.add_param('x', val=0.)
mm.add_output('f_x', val=(0., 0.), surrogate=surrogate)
def test_inputs_wrt_nfidelity(self):
mm = MultiFiMetaModel(nfi=3)
mm.add_param('x', 0.)
mm.add_output('y', 0.)
prob = Problem(Group())
prob.root.add('mm', mm)
prob.setup(check=False)
self.assertEqual(prob['mm.train:x'], [])
self.assertEqual(prob['mm.train:x_fi2'], [])
self.assertEqual(prob['mm.train:x_fi3'], [])
self.assertEqual(prob['mm.train:y'], [])
self.assertEqual(prob['mm.train:y_fi2'], [])
self.assertEqual(prob['mm.train:y_fi3'], [])
def test_two_dim_bi_fidelity_training(self):
mm = MultiFiMetaModel(nfi=2)
mm.add_param('x1', 0.)
mm.add_param('x2', 0.)
surr_y1 = MockSurrogate()
surr_y2 = MockSurrogate()
mm.add_output('y1', 0., surrogate = surr_y1)
mm.add_output('y2', 0., surrogate = surr_y2)
prob = Problem(Group())
prob.root.add('mm', mm)
prob.setup(check=False)
prob['mm.train:x1'] = [1.0, 2.0, 3.0]
prob['mm.train:x1_fi2'] = [1.1, 2.1, 3.1, 1.0, 2.0, 3.0]
prob['mm.train:x2'] = [1.0, 2.0, 3.0]
prob['mm.train:x2_fi2'] = [2.1, 2.2, 2.3, 1.0, 2.0, 3.0]
prob['mm.train:y1'] = [0.0, 0.1, 0.2]
prob['mm.train:y1_fi2'] = [3.0, 3.1, 3.3, 3.4, 3.5 ,3.6]
prob['mm.train:y2'] = [4.0, 4.0, 4.0]
prob['mm.train:y2_fi2'] = [4.0, 4.1, 4.3, 4.4, 4.5 ,4.6]
prob.run()
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_one_dim_bi_fidelity_training(self):
mm = MultiFiMetaModel(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.metadata['train:x'] = [0.0, 0.4, 1.0]
mm.metadata['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.metadata['train:y'] = [3.02720998, 0.11477697, 15.82973195]
mm.metadata['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_inputs_wrt_nfidelity(self):
mm = MultiFiMetaModel(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.metadata['train:x'], None)
self.assertEqual(mm.metadata['train:x_fi2'], None)
self.assertEqual(mm.metadata['train:x_fi3'], None)
self.assertEqual(mm.metadata['train:y'], None)
self.assertEqual(mm.metadata['train:y_fi2'], None)
self.assertEqual(mm.metadata['train:y_fi3'], None)
def test_inputs_wrt_nfidelity(self):
mm = MultiFiMetaModel(nfi=3)
mm.add_param('x', 0.)
mm.add_output('y', 0.)
prob = Problem(Group())
prob.root.add('mm', mm)
prob.setup(check=False)
self.assertEqual(prob['mm.train:x'], [])
self.assertEqual(prob['mm.train:x_fi2'], [])
self.assertEqual(prob['mm.train:x_fi3'], [])
self.assertEqual(prob['mm.train:y'], [])
self.assertEqual(prob['mm.train:y_fi2'], [])
self.assertEqual(prob['mm.train:y_fi3'], [])
def test_two_dim_bi_fidelity_training(self):
mm = MultiFiMetaModel(nfi=2)
mm.add_param('x1', 0.)
mm.add_param('x2', 0.)
surr_y1 = MockSurrogate()
surr_y2 = MockSurrogate()
mm.add_output('y1', 0., surrogate=surr_y1)
mm.add_output('y2', 0., surrogate=surr_y2)
prob = Problem(Group())
prob.root.add('mm', mm)
prob.setup(check=False)
prob['mm.train:x1'] = [1.0, 2.0, 3.0]
prob['mm.train:x1_fi2'] = [1.1, 2.1, 3.1, 1.0, 2.0, 3.0]
prob['mm.train:x2'] = [1.0, 2.0, 3.0]
prob['mm.train:x2_fi2'] = [2.1, 2.2, 2.3, 1.0, 2.0, 3.0]
prob['mm.train:y1'] = [0.0, 0.1, 0.2]
prob['mm.train:y1_fi2'] = [3.0, 3.1, 3.3, 3.4, 3.5, 3.6]
prob['mm.train:y2'] = [4.0, 4.0, 4.0]
prob['mm.train:y2_fi2'] = [4.0, 4.1, 4.3, 4.4, 4.5, 4.6]
prob.run()
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_multifidelity_warm_start(self):
mm = MultiFiMetaModel(nfi=2)
surr = MockSurrogate()
mm.add_input('x', 0.)
mm.add_output('y', 0., surrogate=surr)
mm.warm_restart = True
prob = Problem(Group())
prob.model.add_subsystem('mm', mm)
prob.setup(check=False)
mm.metadata['train:x'] = [0.0, 0.4, 1.0]
mm.metadata['train:x_fi2'] = [0.1, 0.2, 0.3, 0.5, 0.6]
mm.metadata['train:y'] = [1.0, 1.4, 2.0]
mm.metadata['train:y_fi2'] = [1.1, 1.2, 1.3, 1.5, 1.6]
prob.run_model()
expected_xtrain = [
np.array([[0.0], [0.4], [1.0]]),
np.array([[0.1], [0.2], [0.3], [0.5], [0.6]])
]
expected_ytrain = [
np.array([[1.0], [1.4], [2.0]]),
np.array([[1.1], [1.2], [1.3], [1.5], [1.6]])
]
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])
# Test adding only one lowest fidelity sample
mm.metadata['train:x'] = []
mm.metadata['train:y'] = []
mm.metadata['train:x_fi2'] = [2.0]
mm.metadata['train:y_fi2'] = [1.0]
mm.train = True
prob.run_model()
expected_xtrain = [
np.array([[0.0], [0.4], [1.0]]),
np.array([[0.1], [0.2], [0.3], [0.5], [0.6], [2.0]])
]
expected_ytrain = [
np.array([[1.0], [1.4], [2.0]]),
np.array([[1.1], [1.2], [1.3], [1.5], [1.6], [1.0]])
]
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])
# Test adding high and low fidelity points
mm.metadata['train:x'] = [3.0]
mm.metadata['train:x_fi2'] = [3.0]
mm.metadata['train:y'] = [4.0]
mm.metadata['train:y_fi2'] = [4.0]
mm.train = True
prob.run_model()
expected_xtrain = [
np.array([[0.0], [0.4], [1.0], [3.0]]),
np.array([[0.1], [0.2], [0.3], [0.5], [0.6], [2.0], [3.0]])
]
expected_ytrain = [
np.array([[1.0], [1.4], [2.0], [4.0]]),
np.array([[1.1], [1.2], [1.3], [1.5], [1.6], [1.0], [4.0]])
]
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_multifidelity_warm_start(self):
mm = MultiFiMetaModel(nfi=2)
mm.add_param('x', 0.)
surr = MockSurrogate()
mm.add_output('y', 0., surrogate = surr)
mm.warm_restart=True
prob = Problem(Group())
prob.root.add('mm', mm)
prob.setup(check=False)
prob['mm.train:x'] = [0.0, 0.4, 1.0]
prob['mm.train:x_fi2'] = [0.1, 0.2, 0.3, 0.5, 0.6]
prob['mm.train:y'] = [1.0, 1.4, 2.0]
prob['mm.train:y_fi2'] = [1.1, 1.2, 1.3, 1.5, 1.6]
prob.run()
expected_xtrain=[np.array([[0.0], [0.4], [1.0]]),
np.array([[0.1], [0.2], [0.3], [0.5], [0.6]])]
expected_ytrain=[np.array([[1.0], [1.4], [2.0]]),
np.array([[1.1], [1.2], [1.3], [1.5], [1.6]])]
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])
# Test adding only one lowest fidelity sample
prob['mm.train:x'] = []
prob['mm.train:y'] = []
prob['mm.train:x_fi2'] = [2.0]
prob['mm.train:y_fi2'] = [1.0]
mm.train=True
prob.run()
expected_xtrain=[np.array([[0.0], [0.4], [1.0]]),
np.array([[0.1], [0.2], [0.3], [0.5], [0.6], [2.0]])]
expected_ytrain=[np.array([[1.0], [1.4], [2.0]]),
np.array([[1.1], [1.2], [1.3], [1.5], [1.6], [1.0]])]
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])
# Test adding high and low fidelity points
prob['mm.train:x'] = [3.0]
prob['mm.train:x_fi2'] = [3.0]
prob['mm.train:y'] = [4.0]
prob['mm.train:y_fi2'] = [4.0]
mm.train = True
prob.run()
expected_xtrain=[np.array([[0.0], [0.4], [1.0], [3.0]]),
np.array([[0.1], [0.2], [0.3], [0.5], [0.6], [2.0], [3.0]])]
expected_ytrain=[np.array([[1.0], [1.4], [2.0], [4.0]]),
np.array([[1.1], [1.2], [1.3], [1.5], [1.6], [1.0], [4.0]])]
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])