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test_doe_driver.py
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test_doe_driver.py
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"""
Test DOE Driver and Generators.
"""
from io import StringIO
import unittest
import os
import os.path
import glob
import csv
import numpy as np
import openmdao.api as om
from openmdao.test_suite.components.paraboloid import Paraboloid
from openmdao.test_suite.components.paraboloid_distributed import DistParab
from openmdao.test_suite.groups.parallel_groups import FanInGrouped
from openmdao.utils.assert_utils import assert_near_equal
from openmdao.utils.general_utils import run_driver
from openmdao.utils.testing_utils import use_tempdirs
from openmdao.utils.mpi import MPI
try:
from openmdao.vectors.petsc_vector import PETScVector
except ImportError:
PETScVector = None
try:
import pyDOE3
except ImportError:
pyDOE3 = None
class ParaboloidArray(om.ExplicitComponent):
"""
Evaluates the equation f(x,y) = (x-3)^2 + x*y + (y+4)^2 - 3.
Where x and y are xy[0] and xy[1] respectively.
"""
def setup(self):
self.add_input('xy', val=np.array([0., 0.]))
self.add_output('f_xy', val=0.0)
def compute(self, inputs, outputs):
"""
f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3
"""
x = inputs['xy'][0]
y = inputs['xy'][1]
outputs['f_xy'] = (x - 3.0)**2 + x * y + (y + 4.0)**2 - 3.0
class ParaboloidDiscrete(om.ExplicitComponent):
def setup(self):
self.add_discrete_input('x', val=10, tags='xx')
self.add_discrete_input('y', val=0, tags='yy')
self.add_discrete_output('f_xy', val=0, tags='ff')
def compute(self, inputs, outputs, discrete_inputs, discrete_outputs):
x = discrete_inputs['x']
y = discrete_inputs['y']
f_xy = (x - 3.0)**2 + x * y + (y + 4.0)**2 - 3.0
discrete_outputs['f_xy'] = int(f_xy)
class ParaboloidDiscreteArray(om.ExplicitComponent):
def setup(self):
self.add_discrete_input('x', val=np.ones((2, )), tags='xx')
self.add_discrete_input('y', val=np.ones((2, )), tags='yy')
self.add_discrete_output('f_xy', val=np.ones((2, )), tags='ff')
def compute(self, inputs, outputs, discrete_inputs, discrete_outputs):
x = discrete_inputs['x']
y = discrete_inputs['y']
f_xy = (x - 3.0)**2 + x * y + (y + 4.0)**2 - 3.0
discrete_outputs['f_xy'] = f_xy.astype(int)
class TestErrors(unittest.TestCase):
def test_generator_check(self):
prob = om.Problem()
with self.assertRaises(TypeError) as err:
prob.driver = om.DOEDriver(om.FullFactorialGenerator)
self.assertEqual(str(err.exception),
"DOEDriver requires an instance of DOEGenerator, "
"but a class object was found: FullFactorialGenerator")
with self.assertRaises(TypeError) as err:
prob.driver = om.DOEDriver(om.Problem())
self.assertEqual(str(err.exception),
"DOEDriver requires an instance of DOEGenerator, "
"but an instance of Problem was found.")
@unittest.skipIf(pyDOE3, "only runs if 'pyDOE3' is not installed")
def test_no_pyDOE3(self):
with self.assertRaises(RuntimeError) as err:
om.FullFactorialGenerator(levels=3)
self.assertEqual(str(err.exception),
"FullFactorialGenerator requires the 'pyDOE3' package, "
"which can be installed with one of the following commands:\n"
" pip install openmdao[doe]\n"
" pip install pyDOE3")
with self.assertRaises(RuntimeError) as err:
om.DOEDriver(generator=om.FullFactorialGenerator(levels=3))
self.assertEqual(str(err.exception),
"FullFactorialGenerator requires the 'pyDOE3' package, "
"which can be installed with one of the following commands:\n"
" pip install openmdao[doe]\n"
" pip install pyDOE3")
@unittest.skipUnless(pyDOE3, "requires 'pyDOE3', pip install openmdao[doe]")
def test_lhc_criterion(self):
with self.assertRaises(ValueError) as err:
om.LatinHypercubeGenerator(criterion='foo')
self.assertEqual(str(err.exception),
"Invalid criterion 'foo' specified for LatinHypercubeGenerator. "
"Must be one of ['center', 'c', 'maximin', 'm', 'centermaximin', "
"'cm', 'correlation', 'corr', None].")
@use_tempdirs
class TestDOEDriver(unittest.TestCase):
def setUp(self):
self.fullfact3 = [
[('x', 0.), ('y', 0.)],
[('x', .5), ('y', 0.)],
[('x', 1.), ('y', 0.)],
[('x', 0.), ('y', .5)],
[('x', .5), ('y', .5)],
[('x', 1.), ('y', .5)],
[('x', 0.), ('y', 1.)],
[('x', .5), ('y', 1.)],
[('x', 1.), ('y', 1.)],
]
self.expected_fullfact3 = [
{'x': np.array([0.]), 'y': np.array([0.]), 'f_xy': np.array([22.00])},
{'x': np.array([.5]), 'y': np.array([0.]), 'f_xy': np.array([19.25])},
{'x': np.array([1.]), 'y': np.array([0.]), 'f_xy': np.array([17.00])},
{'x': np.array([0.]), 'y': np.array([.5]), 'f_xy': np.array([26.25])},
{'x': np.array([.5]), 'y': np.array([.5]), 'f_xy': np.array([23.75])},
{'x': np.array([1.]), 'y': np.array([.5]), 'f_xy': np.array([21.75])},
{'x': np.array([0.]), 'y': np.array([1.]), 'f_xy': np.array([31.00])},
{'x': np.array([.5]), 'y': np.array([1.]), 'f_xy': np.array([28.75])},
{'x': np.array([1.]), 'y': np.array([1.]), 'f_xy': np.array([27.00])},
]
self.expected_fullfact3_derivs = [
{('f_xy', 'x'): np.array(-6.), ('f_xy', 'y'): np.array(8.)},
{('f_xy', 'x'): np.array(-5.), ('f_xy', 'y'): np.array(8.5)},
{('f_xy', 'x'): np.array(-4.), ('f_xy', 'y'): np.array(9.)},
{('f_xy', 'x'): np.array(-5.5), ('f_xy', 'y'): np.array(9.)},
{('f_xy', 'x'): np.array(-4.5), ('f_xy', 'y'): np.array(9.5)},
{('f_xy', 'x'): np.array(-3.5), ('f_xy', 'y'): np.array(10.)},
{('f_xy', 'x'): np.array(-5.), ('f_xy', 'y'): np.array(10.)},
{('f_xy', 'x'): np.array(-4.), ('f_xy', 'y'): np.array(10.5)},
{('f_xy', 'x'): np.array(-3.), ('f_xy', 'y'): np.array(11.)},
]
def test_no_generator(self):
prob = om.Problem()
model = prob.model
model.add_subsystem('p1', om.IndepVarComp('x', 0.), promotes=['*'])
model.add_subsystem('p2', om.IndepVarComp('y', 0.), promotes=['*'])
model.add_subsystem('comp', Paraboloid(), promotes=['*'])
model.add_design_var('x', lower=-10, upper=10)
model.add_design_var('y', lower=-10, upper=10)
model.add_objective('f_xy')
prob.driver = om.DOEDriver()
prob.driver.add_recorder(om.SqliteRecorder("cases.sql"))
prob.setup()
prob.run_driver()
prob.cleanup()
cr = om.CaseReader("cases.sql")
cases = cr.list_cases('driver', out_stream=None)
self.assertEqual(len(cases), 0)
def test_list(self):
prob = om.Problem()
model = prob.model
model.add_subsystem('p1', om.IndepVarComp('x', 0.0), promotes=['x'])
model.add_subsystem('p2', om.IndepVarComp('y', 0.0), promotes=['y'])
model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])
model.add_design_var('x', lower=0.0, upper=1.0)
model.add_design_var('y', lower=0.0, upper=1.0)
model.add_objective('f_xy')
prob.setup()
# create DOEDriver using provided list of cases
prob.driver = om.DOEDriver(self.fullfact3)
prob.driver.add_recorder(om.SqliteRecorder("cases.sql"))
prob.run_driver()
prob.cleanup()
expected = self.expected_fullfact3
cr = om.CaseReader("cases.sql")
cases = cr.list_cases('driver', out_stream=None)
self.assertEqual(len(cases), 9)
for case, expected_case in zip(cases, expected):
outputs = cr.get_case(case).outputs
for name in ('x', 'y', 'f_xy'):
self.assertEqual(outputs[name], expected_case[name])
def test_list_errors(self):
prob = om.Problem()
model = prob.model
model.add_subsystem('p1', om.IndepVarComp('x', 0.0), promotes=['x'])
model.add_subsystem('p2', om.IndepVarComp('y', 0.0), promotes=['y'])
model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])
model.add_design_var('x', lower=0.0, upper=1.0)
model.add_design_var('y', lower=0.0, upper=1.0)
model.add_objective('f_xy')
prob.setup()
# data does not contain a list
cases = {'desvar': 1.0}
with self.assertRaises(RuntimeError) as err:
prob.driver = om.DOEDriver(generator=om.ListGenerator(cases))
self.assertEqual(str(err.exception), "Invalid DOE case data, "
"expected a list but got a dict.")
# data contains a list of non-list
cases = [{'desvar': 1.0}]
prob.driver = om.DOEDriver(generator=om.ListGenerator(cases))
with self.assertRaises(RuntimeError) as err:
prob.run_driver()
self.assertEqual(str(err.exception), "Invalid DOE case found, "
"expecting a list of name/value pairs:\n{'desvar': 1.0}")
# data contains a list of list, but one has the wrong length
cases = [
[['x', 0.], ['y', 0.]],
[['x', 1.], ['y', 1., 'foo']]
]
prob.driver = om.DOEDriver(generator=om.ListGenerator(cases))
with self.assertRaises(RuntimeError) as err:
prob.run_driver()
self.assertEqual(str(err.exception), "Invalid DOE case found, "
"expecting a list of name/value pairs:\n"
"[['x', 1.0], ['y', 1.0, 'foo']]")
# data contains a list of list, but one case has an invalid design var
cases = [
[['x', 0.], ['y', 0.]],
[['x', 1.], ['z', 1.]]
]
prob.driver = om.DOEDriver(generator=om.ListGenerator(cases))
with self.assertRaises(RuntimeError) as err:
prob.run_driver()
self.assertEqual(str(err.exception), "Invalid DOE case found, "
"'z' is not a valid design variable:\n"
"[['x', 1.0], ['z', 1.0]]")
# data contains a list of list, but one case has multiple invalid design vars
cases = [
[['x', 0.], ['y', 0.]],
[['p1.y', 1.], ['p2.z', 1.]]
]
prob.driver = om.DOEDriver(generator=om.ListGenerator(cases))
with self.assertRaises(RuntimeError) as err:
prob.run_driver()
self.assertEqual(str(err.exception), "Invalid DOE case found, "
"['p1.y', 'p2.z'] are not valid design variables:\n"
"[['p1.y', 1.0], ['p2.z', 1.0]]")
def test_csv(self):
prob = om.Problem()
model = prob.model
model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])
model.set_input_defaults('x', 0.0)
model.set_input_defaults('y', 0.0)
model.add_design_var('x', lower=0.0, upper=1.0)
model.add_design_var('y', lower=0.0, upper=1.0)
model.add_objective('f_xy')
prob.setup()
# generate CSV file with cases
cases = self.fullfact3
header = [var for (var, val) in cases[0]]
with open('cases.csv', 'w') as f:
writer = csv.writer(f)
writer.writerow(header)
for case in cases:
writer.writerow([val for _, val in case])
# create DOEDriver using generated CSV file
prob.driver = om.DOEDriver(om.CSVGenerator('cases.csv'))
prob.driver.add_recorder(om.SqliteRecorder("cases.sql"))
prob.run_driver()
prob.cleanup()
expected = self.expected_fullfact3
cr = om.CaseReader("cases.sql")
cases = cr.list_cases('driver', out_stream=None)
self.assertEqual(len(cases), 9)
for case, expected_case in zip(cases, expected):
outputs = cr.get_case(case).outputs
for name in ('x', 'y', 'f_xy'):
self.assertEqual(outputs[name], expected_case[name])
@unittest.skipUnless(pyDOE3, "requires 'pyDOE3', pip install openmdao[doe]")
def test_csv_array(self):
prob = om.Problem()
model = prob.model
model.add_subsystem('p1', om.IndepVarComp('x', [0., 1.]))
model.add_subsystem('p2', om.IndepVarComp('y', [0., 1.]))
model.add_subsystem('comp1', Paraboloid())
model.add_subsystem('comp2', Paraboloid())
model.connect('p1.x', 'comp1.x', src_indices=[0])
model.connect('p2.y', 'comp1.y', src_indices=[0])
model.connect('p1.x', 'comp2.x', src_indices=[1])
model.connect('p2.y', 'comp2.y', src_indices=[1])
model.add_design_var('p1.x', lower=0.0, upper=1.0)
model.add_design_var('p2.y', lower=0.0, upper=1.0)
prob.setup()
# create a list of DOE cases
case_gen = om.FullFactorialGenerator(levels=2)
cases = list(case_gen(model.get_design_vars(recurse=True)))
# generate CSV file with cases
header = [var for var, _ in cases[0]]
with open('cases.csv', 'w') as f:
writer = csv.writer(f)
writer.writerow(header)
for case in cases:
writer.writerow([val for _, val in case])
# create DOEDriver using generated CSV file
prob.driver = om.DOEDriver(om.CSVGenerator('cases.csv'))
prob.driver.add_recorder(om.SqliteRecorder("cases.sql"))
prob.run_driver()
prob.cleanup()
expected = [
{'p1.x': np.array([0., 0.]), 'p2.y': np.array([0., 0.])},
{'p1.x': np.array([1., 0.]), 'p2.y': np.array([0., 0.])},
{'p1.x': np.array([0., 1.]), 'p2.y': np.array([0., 0.])},
{'p1.x': np.array([1., 1.]), 'p2.y': np.array([0., 0.])},
{'p1.x': np.array([0., 0.]), 'p2.y': np.array([1., 0.])},
{'p1.x': np.array([1., 0.]), 'p2.y': np.array([1., 0.])},
{'p1.x': np.array([0., 1.]), 'p2.y': np.array([1., 0.])},
{'p1.x': np.array([1., 1.]), 'p2.y': np.array([1., 0.])},
{'p1.x': np.array([0., 0.]), 'p2.y': np.array([0., 1.])},
{'p1.x': np.array([1., 0.]), 'p2.y': np.array([0., 1.])},
{'p1.x': np.array([0., 1.]), 'p2.y': np.array([0., 1.])},
{'p1.x': np.array([1., 1.]), 'p2.y': np.array([0., 1.])},
{'p1.x': np.array([0., 0.]), 'p2.y': np.array([1., 1.])},
{'p1.x': np.array([1., 0.]), 'p2.y': np.array([1., 1.])},
{'p1.x': np.array([0., 1.]), 'p2.y': np.array([1., 1.])},
{'p1.x': np.array([1., 1.]), 'p2.y': np.array([1., 1.])},
]
cr = om.CaseReader("cases.sql")
cases = cr.list_cases('driver', out_stream=None)
self.assertEqual(len(cases), 16)
for case, expected_case in zip(cases, expected):
outputs = cr.get_case(case).outputs
self.assertEqual(outputs['p1.x'][0], expected_case['p1.x'][0])
self.assertEqual(outputs['p2.y'][0], expected_case['p2.y'][0])
self.assertEqual(outputs['p1.x'][1], expected_case['p1.x'][1])
self.assertEqual(outputs['p2.y'][1], expected_case['p2.y'][1])
def test_csv_errors(self):
# test invalid file name
with self.assertRaises(RuntimeError) as err:
om.CSVGenerator(1.23)
self.assertEqual(str(err.exception),
"'1.23' is not a valid file name.")
# test file not found
with self.assertRaises(RuntimeError) as err:
om.CSVGenerator('nocases.csv')
self.assertEqual(str(err.exception),
"File not found: nocases.csv")
# create problem and a list of DOE cases
prob = om.Problem()
model = prob.model
model.add_subsystem('p1', om.IndepVarComp('x', 0.0), promotes=['x'])
model.add_subsystem('p2', om.IndepVarComp('y', 0.0), promotes=['y'])
model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])
model.add_design_var('x', lower=0.0, upper=1.0)
model.add_design_var('y', lower=0.0, upper=1.0)
model.add_objective('f_xy')
prob.setup()
# test CSV file with an invalid design var
cases = self.fullfact3
header = [var for var, _ in cases[0]]
header[-1] = 'foobar'
with open('cases.csv', 'w') as f:
writer = csv.writer(f)
writer.writerow(header)
for case in cases:
writer.writerow([val for _, val in case])
prob.driver = om.DOEDriver(om.CSVGenerator('cases.csv'))
with self.assertRaises(RuntimeError) as err:
prob.run_driver()
self.assertEqual(str(err.exception), "Invalid DOE case file, "
"'foobar' is not a valid design variable.")
# test CSV file with invalid design vars
header = [var + '_bad' for var, _ in cases[0]]
with open('cases.csv', 'w') as f:
writer = csv.writer(f)
writer.writerow(header)
for case in cases:
writer.writerow([val for _, val in case])
with self.assertRaises(RuntimeError) as err:
prob.run_driver()
self.assertEqual(str(err.exception), "Invalid DOE case file, "
"%s are not valid design variables." %
str(header))
# test CSV file with invalid values
header = [var for var, _ in cases[0]]
with open('cases.csv', 'w') as f:
writer = csv.writer(f)
writer.writerow(header)
for case in cases:
writer.writerow([np.ones((2, 2)) * val for _, val in case])
with self.assertRaises(ValueError) as err:
prob.run_driver()
self.assertEqual(str(err.exception),
"Error assigning x = [0. 0. 0. 0.]: could not broadcast "
"input array from shape (4,) into shape (1,)")
def test_uniform(self):
prob = om.Problem()
model = prob.model
model.add_subsystem('comp', Paraboloid(), promotes=['*'])
model.set_input_defaults('x', 0.0)
model.set_input_defaults('y', 0.0)
model.add_design_var('x', lower=-10, upper=10)
model.add_design_var('y', lower=-10, upper=10)
model.add_objective('f_xy')
prob.driver = om.DOEDriver(om.UniformGenerator(num_samples=5, seed=0))
prob.driver.add_recorder(om.SqliteRecorder("cases.sql"))
prob.setup()
prob.run_driver()
prob.cleanup()
# all values should be between -10 and 10, check expected values for seed = 0
expected = [
{'x': np.array([0.97627008]), 'y': np.array([4.30378733])},
{'x': np.array([2.05526752]), 'y': np.array([0.89766366])},
{'x': np.array([-1.52690401]), 'y': np.array([2.91788226])},
{'x': np.array([-1.24825577]), 'y': np.array([7.83546002])},
{'x': np.array([9.27325521]), 'y': np.array([-2.33116962])},
]
cr = om.CaseReader("cases.sql")
cases = cr.list_cases('driver', out_stream=None)
self.assertEqual(len(cases), 5)
for case, expected_case in zip(cases, expected):
outputs = cr.get_case(case).outputs
for name in ('x', 'y'):
assert_near_equal(outputs[name], expected_case[name], 1e-4)
@unittest.skipUnless(pyDOE3, "requires 'pyDOE3', pip install openmdao[doe]")
def test_full_factorial(self):
prob = om.Problem()
model = prob.model
model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])
model.set_input_defaults('x', 0.0)
model.set_input_defaults('y', 0.0)
model.add_design_var('x', lower=0.0, upper=1.0)
model.add_design_var('y', lower=0.0, upper=1.0)
model.add_objective('f_xy')
prob.driver = om.DOEDriver(generator=om.FullFactorialGenerator(levels=3))
prob.driver.add_recorder(om.SqliteRecorder("cases.sql"))
prob.setup()
prob.run_driver()
prob.cleanup()
expected = self.expected_fullfact3
cr = om.CaseReader("cases.sql")
cases = cr.list_cases('driver', out_stream=None)
self.assertEqual(len(cases), 9)
for case, expected_case in zip(cases, expected):
outputs = cr.get_case(case).outputs
for name in ('x', 'y', 'f_xy'):
self.assertEqual(outputs[name], expected_case[name])
@unittest.skipUnless(pyDOE3, "requires 'pyDOE3', pip install openmdao[doe]")
def test_full_factorial_factoring(self):
class Digits2Num(om.ExplicitComponent):
"""
Makes from two vectors with 2 elements a 4 digit number.
For singe digit integers always gives a unique output number.
"""
def setup(self):
self.add_input('x', val=np.array([0., 0.]))
self.add_input('y', val=np.array([0., 0.]))
self.add_output('f', val=0.0)
def compute(self, inputs, outputs):
x = inputs['x']
y = inputs['y']
outputs['f'] = x[0] * 1000 + x[1] * 100 + y[0] * 10 + y[1]
prob = om.Problem()
model = prob.model
model.set_input_defaults('x', np.array([0.0, 0.0]))
model.set_input_defaults('y', np.array([0.0, 0.0]))
model.add_subsystem('comp', Digits2Num(), promotes=['*'])
model.add_design_var('x', lower=0.0, upper=np.array([1.0, 2.0]))
model.add_design_var('y', lower=0.0, upper=np.array([3.0, 4.0]))
model.add_objective('f')
prob.driver = om.DOEDriver(generator=om.FullFactorialGenerator(levels=2))
prob.driver.add_recorder(om.SqliteRecorder("cases.sql"))
prob.setup()
prob.run_driver()
prob.cleanup()
cr = om.CaseReader("cases.sql")
cases = cr.list_cases('driver', out_stream=None)
objs = [cr.get_case(case).outputs['f'].item() for case in cases]
self.assertEqual(len(objs), 16)
# Testing uniqueness. If all elements are unique, it should be the same length as the
# number of cases
self.assertEqual(len(set(objs)), 16)
@unittest.skipUnless(pyDOE3, "requires 'pyDOE3', pip install openmdao[doe]")
def test_full_factorial_array(self):
prob = om.Problem()
model = prob.model
model.set_input_defaults('xy', np.array([0., 0.]))
model.add_subsystem('comp', ParaboloidArray(), promotes=['*'])
model.add_design_var('xy', lower=np.array([-10., -50.]), upper=np.array([10., 50.]))
model.add_objective('f_xy')
prob.driver = om.DOEDriver(om.FullFactorialGenerator(levels=3))
prob.driver.add_recorder(om.SqliteRecorder("cases.sql"))
prob.setup()
prob.run_driver()
prob.cleanup()
expected = [
{'xy': np.array([-10., -50.])},
{'xy': np.array([0., -50.])},
{'xy': np.array([10., -50.])},
{'xy': np.array([-10., 0.])},
{'xy': np.array([0., 0.])},
{'xy': np.array([10., 0.])},
{'xy': np.array([-10., 50.])},
{'xy': np.array([0., 50.])},
{'xy': np.array([10., 50.])},
]
cr = om.CaseReader("cases.sql")
cases = cr.list_cases('driver', out_stream=None)
self.assertEqual(len(cases), 9)
for case, expected_case in zip(cases, expected):
outputs = cr.get_case(case).outputs
self.assertEqual(outputs['xy'][0], expected_case['xy'][0])
self.assertEqual(outputs['xy'][1], expected_case['xy'][1])
@unittest.skipUnless(pyDOE3, "requires 'pyDOE3', pip install openmdao[doe]")
def test_full_fact_dict_levels(self):
# Specifying levels only for one DV, the other is defaulted
prob = om.Problem()
model = prob.model
expected = [
{'x': np.array([0.]), 'y': np.array([0.]), 'f_xy': np.array([22.00])},
{'x': np.array([1.]), 'y': np.array([0.]), 'f_xy': np.array([17.00])},
{'x': np.array([0.]), 'y': np.array([.5]), 'f_xy': np.array([26.25])},
{'x': np.array([1.]), 'y': np.array([.5]), 'f_xy': np.array([21.75])},
{'x': np.array([0.]), 'y': np.array([1.]), 'f_xy': np.array([31.00])},
{'x': np.array([1.]), 'y': np.array([1.]), 'f_xy': np.array([27.00])},
]
model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])
model.set_input_defaults('x', 0.0)
model.set_input_defaults('y', 0.0)
model.add_design_var('x', lower=0.0, upper=1.0)
model.add_design_var('y', lower=0.0, upper=1.0)
model.add_objective('f_xy')
prob.driver = om.DOEDriver(generator=om.FullFactorialGenerator(levels={"y": 3}))
prob.driver.add_recorder(om.SqliteRecorder("cases.sql"))
prob.setup()
prob.run_driver()
prob.cleanup()
cr = om.CaseReader("cases.sql")
cases = cr.list_cases('driver', out_stream=None)
self.assertEqual(len(cases), 6)
for case, expected_case in zip(cases, expected):
outputs = cr.get_case(case).outputs
self.assertEqual(outputs['x'], expected_case['x'])
self.assertEqual(outputs['y'], expected_case['y'])
self.assertEqual(outputs['f_xy'], expected_case['f_xy'])
@unittest.skipUnless(pyDOE3, "requires 'pyDOE3', pip install openmdao[doe]")
def test_generalized_subset(self):
# All DVs have the same number of levels
prob = om.Problem()
model = prob.model
model.set_input_defaults('x', 0.0)
model.set_input_defaults('y', 0.0)
model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])
model.add_design_var('x', lower=0.0, upper=1.0)
model.add_design_var('y', lower=0.0, upper=1.0)
model.add_objective('f_xy')
prob.driver = om.DOEDriver(generator=om.GeneralizedSubsetGenerator(levels=2, reduction=2))
prob.driver.add_recorder(om.SqliteRecorder("cases.sql"))
prob.setup()
prob.run_driver()
prob.cleanup()
expected = [
{'x': np.array([0.0]), 'y': np.array([0.0]), 'f_xy': np.array([22.0])},
{'x': np.array([1.0]), 'y': np.array([1.0]), 'f_xy': np.array([27.0])},
]
cr = om.CaseReader("cases.sql")
cases = cr.list_cases('driver')
self.assertEqual(len(cases), 2)
for case, expected_case in zip(cases, expected):
outputs = cr.get_case(case).outputs
for name in ('x', 'y', 'f_xy'):
self.assertEqual(outputs[name], expected_case[name])
@unittest.skipUnless(pyDOE3, "requires 'pyDOE3', pip install openmdao[doe]")
def test_generalized_subset_dict_levels(self):
# Number of variables specified individually for all DVs (scalars).
prob = om.Problem()
model = prob.model
model.set_input_defaults('x', 0.0)
model.set_input_defaults('y', 0.0)
model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])
model.add_design_var('x', lower=0.0, upper=1.0)
model.add_design_var('y', lower=0.0, upper=1.0)
model.add_objective('f_xy')
prob.driver = om.DOEDriver(generator=om.GeneralizedSubsetGenerator(levels={'x': 3, 'y': 6}, reduction=2))
prob.driver.add_recorder(om.SqliteRecorder("cases.sql"))
prob.setup()
prob.run_driver()
prob.cleanup()
expected = [
{'x': np.array([0.]), 'y': np.array([0.]), 'f_xy': np.array([22.])},
{'x': np.array([0.]), 'y': np.array([0.4]), 'f_xy': np.array([25.36])},
{'x': np.array([0.]), 'y': np.array([0.8]), 'f_xy': np.array([29.04])},
{'x': np.array([1.]), 'y': np.array([0.]), 'f_xy': np.array([17.])},
{'x': np.array([1.]), 'y': np.array([0.4]), 'f_xy': np.array([20.76])},
{'x': np.array([1.]), 'y': np.array([0.8]), 'f_xy': np.array([24.84])},
{'x': np.array([0.5]), 'y': np.array([0.2]), 'f_xy': np.array([20.99])},
{'x': np.array([0.5]), 'y': np.array([0.6]), 'f_xy': np.array([24.71])},
{'x': np.array([0.5]), 'y': np.array([1.]), 'f_xy': np.array([28.75])},
]
cr = om.CaseReader("cases.sql")
cases = cr.list_cases('driver')
self.assertEqual(len(cases), 9)
for case, expected_case in zip(cases, expected):
outputs = cr.get_case(case).outputs
for name in ('x', 'y', 'f_xy'):
self.assertAlmostEqual(outputs[name][0], expected_case[name][0])
@unittest.skipUnless(pyDOE3, "requires 'pyDOE3', pip install openmdao[doe]")
def test_generalized_subset_array(self):
# Number of levels specified individually for all DVs (arrays).
class Digits2Num(om.ExplicitComponent):
"""
Makes from two vectors with 2 elements a 4 digit number.
For singe digit integers always gives a unique output number.
"""
def setup(self):
self.add_input('x', val=np.array([0., 0.]))
self.add_input('y', val=np.array([0., 0.]))
self.add_output('f', val=0.0)
def compute(self, inputs, outputs):
x = inputs['x']
y = inputs['y']
outputs['f'] = x[0] * 1000 + x[1] * 100 + y[0] * 10 + y[1]
prob = om.Problem()
model = prob.model
model.set_input_defaults('x', np.array([0.0, 0.0]))
model.set_input_defaults('y', np.array([0.0, 0.0]))
model.add_subsystem('comp', Digits2Num(), promotes=['*'])
model.add_design_var('x', lower=0.0, upper=np.array([1.0, 2.0]))
model.add_design_var('y', lower=0.0, upper=np.array([3.0, 4.0]))
model.add_objective('f')
prob.driver = om.DOEDriver(generator=om.GeneralizedSubsetGenerator(levels={'x': 5, 'y': 8}, reduction=14))
prob.driver.add_recorder(om.SqliteRecorder("cases.sql"))
prob.setup()
prob.run_driver()
prob.cleanup()
cr = om.CaseReader("cases.sql")
cases = cr.list_cases('driver', out_stream=None)
objs = [cr.get_case(case).outputs['f'].item() for case in cases]
self.assertEqual(len(objs), 104) # The number can be verified with standalone pyDOE3
# Testing uniqueness. If all elements are unique, it should be the same length as the number of cases
self.assertEqual(len(set(objs)), 104)
@unittest.skipUnless(pyDOE3, "requires 'pyDOE3', pip install openmdao[doe]")
def test_plackett_burman(self):
prob = om.Problem()
model = prob.model
model.add_subsystem('p1', om.IndepVarComp('x', 0.0), promotes=['x'])
model.add_subsystem('p2', om.IndepVarComp('y', 0.0), promotes=['y'])
model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])
model.add_design_var('x', lower=0.0, upper=1.0)
model.add_design_var('y', lower=0.0, upper=1.0)
model.add_objective('f_xy')
prob.driver = om.DOEDriver(om.PlackettBurmanGenerator())
prob.driver.add_recorder(om.SqliteRecorder("cases.sql"))
prob.setup()
prob.run_driver()
prob.cleanup()
expected = [
{'x': np.array([0.]), 'y': np.array([0.]), 'f_xy': np.array([22.00])},
{'x': np.array([1.]), 'y': np.array([0.]), 'f_xy': np.array([17.00])},
{'x': np.array([0.]), 'y': np.array([1.]), 'f_xy': np.array([31.00])},
{'x': np.array([1.]), 'y': np.array([1.]), 'f_xy': np.array([27.00])},
]
cr = om.CaseReader("cases.sql")
cases = cr.list_cases('driver', out_stream=None)
self.assertEqual(len(cases), 4)
for case, expected_case in zip(cases, expected):
outputs = cr.get_case(case).outputs
for name in ('x', 'y', 'f_xy'):
self.assertEqual(outputs[name], expected_case[name])
@unittest.skipUnless(pyDOE3, "requires 'pyDOE3', pip install openmdao[doe]")
def test_box_behnken(self):
upper = 10.
center = 1
prob = om.Problem()
model = prob.model
indep = model.add_subsystem('indep', om.IndepVarComp(), promotes=['*'])
indep.add_output('x', 0.0)
indep.add_output('y', 0.0)
indep.add_output('z', 0.0)
model.add_subsystem('comp', om.ExecComp('a = x**2 + y - z'), promotes=['*'])
model.add_design_var('x', lower=0., upper=upper)
model.add_design_var('y', lower=0., upper=upper)
model.add_design_var('z', lower=0., upper=upper)
model.add_objective('a')
prob.driver = om.DOEDriver(om.BoxBehnkenGenerator(center=center))
prob.driver.add_recorder(om.SqliteRecorder("cases.sql"))
prob.setup()
prob.run_driver()
prob.cleanup()
cr = om.CaseReader("cases.sql")
cases = cr.list_cases('driver', out_stream=None)
# The Box-Behnken design for 3 factors involves three blocks, in each of
# which 2 factors are varied thru the 4 possible combinations of high & low.
# It also includes centre points (all factors at their central values).
# ref: https://en.wikipedia.org/wiki/Box-Behnken_design
self.assertEqual(len(cases), (3*4)+center)
expected = [
{'x': np.array([0.]), 'y': np.array([0.]), 'z': np.array([5.])},
{'x': np.array([10.]), 'y': np.array([0.]), 'z': np.array([5.])},
{'x': np.array([0.]), 'y': np.array([10.]), 'z': np.array([5.])},
{'x': np.array([10.]), 'y': np.array([10.]), 'z': np.array([5.])},
{'x': np.array([0.]), 'y': np.array([5.]), 'z': np.array([0.])},
{'x': np.array([10.]), 'y': np.array([5.]), 'z': np.array([0.])},
{'x': np.array([0.]), 'y': np.array([5.]), 'z': np.array([10.])},
{'x': np.array([10.]), 'y': np.array([5.]), 'z': np.array([10.])},
{'x': np.array([5.]), 'y': np.array([0.]), 'z': np.array([0.])},
{'x': np.array([5.]), 'y': np.array([10.]), 'z': np.array([0.])},
{'x': np.array([5.]), 'y': np.array([0.]), 'z': np.array([10.])},
{'x': np.array([5.]), 'y': np.array([10.]), 'z': np.array([10.])},
{'x': np.array([5.]), 'y': np.array([5.]), 'z': np.array([5.])},
]
for case, expected_case in zip(cases, expected):
outputs = cr.get_case(case).outputs
for name in ('x', 'y', 'z'):
self.assertEqual(outputs[name], expected_case[name])
@unittest.skipUnless(pyDOE3, "requires 'pyDOE3', pip install openmdao[doe]")
def test_latin_hypercube(self):
samples = 4
bounds = np.array([
[-1, -10], # lower bounds for x and y
[1, 10] # upper bounds for x and y
])
xlb, xub = bounds[0][0], bounds[1][0]
ylb, yub = bounds[0][1], bounds[1][1]
prob = om.Problem()
model = prob.model
model.add_subsystem('p1', om.IndepVarComp('x', 0.0), promotes=['x'])
model.add_subsystem('p2', om.IndepVarComp('y', 0.0), promotes=['y'])
model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])
model.add_design_var('x', lower=xlb, upper=xub)
model.add_design_var('y', lower=ylb, upper=yub)
model.add_objective('f_xy')
prob.driver = om.DOEDriver()
prob.driver.options['generator'] = om.LatinHypercubeGenerator(samples=4, seed=0)
prob.driver.add_recorder(om.SqliteRecorder("cases.sql"))
prob.setup()
prob.run_driver()
prob.cleanup()
# the sample space for each variable should be divided into equal
# size buckets and each variable should have a value in each bucket
all_buckets = set(range(samples))
x_offset = - xlb
x_bucket_size = xub - xlb
x_buckets_filled = set()
y_offset = - ylb
y_bucket_size = yub - ylb
y_buckets_filled = set()
# expected values for seed = 0
expected = [
{'x': np.array([-0.19861831]), 'y': np.array([-6.42405317])},
{'x': np.array([0.2118274]), 'y': np.array([9.458865])},
{'x': np.array([0.71879361]), 'y': np.array([3.22947057])},
{'x': np.array([-0.72559325]), 'y': np.array([-2.27558409])},
]
cr = om.CaseReader("cases.sql")
cases = cr.list_cases('driver', out_stream=None)
self.assertEqual(len(cases), 4)
for case, expected_case in zip(cases, expected):
outputs = cr.get_case(case).outputs
x = outputs['x'].item()
y = outputs['y'].item()
bucket = int((x + x_offset) / (x_bucket_size / samples))
x_buckets_filled.add(bucket)
bucket = int((y + y_offset) / (y_bucket_size / samples))
y_buckets_filled.add(bucket)
assert_near_equal(x, expected_case['x'], 1e-4)
assert_near_equal(y, expected_case['y'], 1e-4)
self.assertEqual(x_buckets_filled, all_buckets)
self.assertEqual(y_buckets_filled, all_buckets)
@unittest.skipUnless(pyDOE3, "requires 'pyDOE3', pip install openmdao[doe]")
def test_latin_hypercube_array(self):
samples = 4
bounds = np.array([
[-10, -50], # lower bounds for x and y
[10, 50] # upper bounds for x and y
])
prob = om.Problem()