def test_surrogate_message_format(self):
mm = om.MultiFiMetaModelUnStructuredComp(nfi=2)
mm.add_input('x', np.zeros((1, 2)))
mm.add_output('y', np.zeros((1, )))
mm.options['default_surrogate'] = om.MultiFiCoKrigingSurrogate(
normalize=False)
prob = om.Problem()
prob.model.add_subsystem('mm', mm)
prob.setup()
x = [
[
[0.13073587, 0.24909577], # expensive (hifi) doe
[0.13073587, 0.24909577],
[0.40806563, 0.91465314]
],
[
[0.91430235, 0.17029894], # cheap (lowfi) doe
[0.40806563, 0.91465314]
]
]
mm.options['train:x'] = x[0]
mm.options['train:y'] = np.array([1, 2, 3])
mm.options['train:x_fi2'] = x[1]
mm.options['train:y_fi2'] = np.array([1, 2])
prob['mm.x'] = np.array([[2. / 3., 1. / 3.]])
with self.assertRaises(ValueError) as cm:
prob.run_model()
self.assertEqual(
str(cm.exception), "MultiFiMetaModelUnStructuredComp (mm): "
"Error calling compute(), Multiple input features cannot have the same value."
)
def test_one_dim_bi_fidelity_training(self):
mm = om.MultiFiMetaModelUnStructuredComp(nfi=2)
surr = MockSurrogate()
mm.add_input('x', 0.)
mm.add_output('y', 0., surrogate=surr)
prob = om.Problem()
prob.model.add_subsystem('mm', mm)
prob.setup()
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_2_input_2_fidelity(self):
import numpy as np
import openmdao.api as om
mm = om.MultiFiMetaModelUnStructuredComp(nfi=2)
mm.add_input('x', np.zeros((1, 2)))
mm.add_output('y', np.zeros((1, )))
# Surrrogate model that implements the multifidelity cokriging method.
mm.options['default_surrogate'] = om.MultiFiCoKrigingSurrogate(
normalize=False)
prob = om.Problem()
prob.model.add_subsystem('mm', mm)
prob.setup()
x_hifi = np.array([
[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]
])
y_hifi = np.array([
69.22687251161716, 28.427292491743817, 20.36175030334259,
7.840766670948326, 23.950783505007422, 16.0326610719367,
77.32264403894713, 26.625242780670835, 135.85615334210993,
101.43980212355875
])
x_lofi = np.array([
[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_lofi = list([
18.204898470295255, 107.66640600958577, 46.11717344625053,
186.002239934648, 135.12480249921992, 65.3605467926758,
51.72316385370553, 15.541873662737451, 72.77648156410065,
100.33324800434931, 86.69974561161716, 52.63307549174382,
34.358261803342586, 28.218996970948325, 57.280532805007425,
41.9510060719367, 107.05618533894713, 39.580998480670836,
171.46115394210995, 138.87939632355875
])
mm.options['train:x'] = x_hifi
mm.options['train:y'] = y_hifi
mm.options['train:x_fi2'] = x_lofi
mm.options['train:y_fi2'] = y_lofi
prob.set_val('mm.x', np.array([[2. / 3., 1. / 3.]]))
prob.run_model()
assert_near_equal(prob.get_val('mm.y'), 26.26, tolerance=0.02)
def test_error_messages(self):
mm = om.MultiFiMetaModelUnStructuredComp(nfi=3)
mm.add_input('x', 0.)
mm.add_output('y', 0.)
prob = om.Problem()
prob.model.add_subsystem('mm', mm)
prob.setup()
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 = (
"MultiFiMetaModelUnStructuredComp (mm): No surrogate specified for output 'y'"
)
self.assertEqual(str(cm.exception), msg)
# Wrong number of input samples
mm = om.MultiFiMetaModelUnStructuredComp(nfi=3)
mm.add_input('x', 0.)
mm.add_input('x2', 0.)
mm.add_output('y', 0.)
prob = om.Problem()
prob.model.add_subsystem('mm', mm)
prob.setup()
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 = (
"MultiFiMetaModelUnStructuredComp (mm): 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 = om.MultiFiMetaModelUnStructuredComp(nfi=3)
mm.add_input('x', 0.)
mm.add_output('y', 0.)
mm.add_output('y2', 0.)
prob = om.Problem()
prob.model.add_subsystem('mm', mm)
prob.setup()
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 = (
"MultiFiMetaModelUnStructuredComp (mm): 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_near_equal(prob['mm.y'], 26.26, tolerance=0.02)
prob['mm.x'] = np.array([[1. / 3., 2. / 3.]])
prob.run_model()
assert_near_equal(prob['mm.y'], 36.1031735, tolerance=0.02)
# Now, vectorized model with both points predicted together.
mm = om.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'] = om.MultiFiCoKrigingSurrogate(
normalize=False)
prob = om.Problem()
prob.model.add_subsystem('mm', mm)
prob.setup()
mm.options['train:x'] = x[0]
mm.options['train:y'] = y[0]
import numpy as np
from time import time
import matplotlib.pyplot as plt
import openmdao.api as om
from testbed_components import simple_1D_low, simple_1D_high
np.random.seed(314)
mm = om.MultiFiMetaModelUnStructuredComp(nfi=2)
mm.add_input("x", np.zeros((1, )))
mm.add_output("y", np.zeros((1, )))
# Surrrogate model that implements the multifidelity cokriging method.
mm.options["default_surrogate"] = om.MultiFiCoKrigingSurrogate(normalize=False)
prob = om.Problem()
prob.model.add_subsystem("mm", mm, promotes=["*"])
prob.driver = om.pyOptSparseDriver() # om.ScipyOptimizeDriver() #
prob.driver.options["optimizer"] = "SNOPT"
# prob.driver.opt_settings['Verify level'] = -1
# --- Objective ---
prob.model.add_objective("y")
prob.model.add_design_var("x", lower=0.0, upper=1.0)
prob.setup()
prob["x"] = 0.8
