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])
