def test_compute_totals_basic(self):
# Basic test for the method using default solvers on simple model.
prob = Problem()
model = prob.model = Group()
model.add_subsystem('p1', IndepVarComp('x', 0.0), promotes=['x'])
model.add_subsystem('p2', IndepVarComp('y', 0.0), promotes=['y'])
model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])
prob.setup(check=False, mode='fwd')
prob.set_solver_print(level=0)
prob.run_model()
of = ['f_xy']
wrt = ['x', 'y']
derivs = prob.compute_totals(of=of, wrt=wrt)
assert_rel_error(self, derivs['f_xy', 'x'], [[-6.0]], 1e-6)
assert_rel_error(self, derivs['f_xy', 'y'], [[8.0]], 1e-6)
prob.setup(check=False, mode='rev')
prob.run_model()
of = ['f_xy']
wrt = ['x', 'y']
derivs = prob.compute_totals(of=of, wrt=wrt)
assert_rel_error(self, derivs['f_xy', 'x'], [[-6.0]], 1e-6)
assert_rel_error(self, derivs['f_xy', 'y'], [[8.0]], 1e-6)
def test_compute_totals_basic_return_dict(self):
# Make sure 'dict' return_format works.
prob = Problem()
model = prob.model = Group()
model.add_subsystem('p1', IndepVarComp('x', 0.0), promotes=['x'])
model.add_subsystem('p2', IndepVarComp('y', 0.0), promotes=['y'])
model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])
prob.setup(check=False, mode='fwd')
prob.set_solver_print(level=0)
prob.run_model()
of = ['f_xy']
wrt = ['x', 'y']
derivs = prob.compute_totals(of=of, wrt=wrt, return_format='dict')
assert_rel_error(self, derivs['f_xy']['x'], [[-6.0]], 1e-6)
assert_rel_error(self, derivs['f_xy']['y'], [[8.0]], 1e-6)
prob.setup(check=False, mode='rev')
prob.run_model()
of = ['f_xy']
wrt = ['x', 'y']
derivs = prob.compute_totals(of=of, wrt=wrt, return_format='dict')
assert_rel_error(self, derivs['f_xy']['x'], [[-6.0]], 1e-6)
assert_rel_error(self, derivs['f_xy']['y'], [[8.0]], 1e-6)
def test_paraboloid_subbed(self):
prob = Problem()
model = prob.model = Group()
model.add_subsystem('p1', IndepVarComp('x', 0.0), promotes=['x'])
model.add_subsystem('p2', IndepVarComp('y', 0.0), promotes=['y'])
sub = model.add_subsystem('sub', Group(), promotes=['x', 'y', 'f_xy'])
sub.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])
model.linear_solver = ScipyKrylov()
sub.approx_totals()
prob.setup(check=False, mode='fwd')
prob.set_solver_print(level=0)
prob.run_model()
of = ['f_xy']
wrt = ['x', 'y']
derivs = prob.compute_totals(of=of, wrt=wrt)
assert_rel_error(self, derivs['f_xy', 'x'], [[-6.0]], 1e-6)
assert_rel_error(self, derivs['f_xy', 'y'], [[8.0]], 1e-6)
Jfd = sub._jacobian
assert_rel_error(self, Jfd['sub.comp.f_xy', 'sub.comp.x'], [[-6.0]],
1e-6)
assert_rel_error(self, Jfd['sub.comp.f_xy', 'sub.comp.y'], [[8.0]],
1e-6)
# 1 output x 2 inputs
self.assertEqual(len(sub._approx_schemes['fd']._exec_list), 2)
def test_paraboloid_subbed(self, vec_class):
if not vec_class:
raise unittest.SkipTest("PETSc is not installed")
prob = self.prob
model = prob.model = Group()
model.add_subsystem('p1', IndepVarComp('x', 0.0), promotes=['x'])
model.add_subsystem('p2', IndepVarComp('y', 0.0), promotes=['y'])
sub = model.add_subsystem('sub', Group(), promotes=['x', 'y', 'f_xy'])
sub.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])
model.linear_solver = ScipyKrylov()
sub.approx_totals(method='cs')
prob.setup(check=False, mode='fwd', local_vector_class=vec_class)
prob.set_solver_print(level=0)
prob.run_model()
of = ['f_xy']
wrt = ['x', 'y']
derivs = prob.compute_totals(of=of, wrt=wrt)
assert_rel_error(self, derivs['f_xy', 'x'], [[-6.0]], 1e-6)
assert_rel_error(self, derivs['f_xy', 'y'], [[8.0]], 1e-6)
Jfd = sub._jacobian
assert_rel_error(self, Jfd['sub.comp.f_xy', 'sub.comp.x'], [[-6.0]],
1e-6)
assert_rel_error(self, Jfd['sub.comp.f_xy', 'sub.comp.y'], [[8.0]],
1e-6)
# 1 output x 2 inputs
self.assertEqual(len(sub._approx_schemes['cs']._exec_list), 2)
def test_unconstrainted(self):
from openmdao.api import Problem, ScipyOptimizeDriver, IndepVarComp
# We'll use the component that was defined in the last tutorial
from openmdao.test_suite.components.paraboloid import Paraboloid
# build the model
prob = Problem()
indeps = prob.model.add_subsystem('indeps', IndepVarComp())
indeps.add_output('x', 3.0)
indeps.add_output('y', -4.0)
prob.model.add_subsystem('paraboloid', Paraboloid())
prob.model.connect('indeps.x', 'paraboloid.x')
prob.model.connect('indeps.y', 'paraboloid.y')
# setup the optimization
prob.driver = ScipyOptimizeDriver()
prob.driver.options['optimizer'] = 'COBYLA'
prob.model.add_design_var('indeps.x', lower=-50, upper=50)
prob.model.add_design_var('indeps.y', lower=-50, upper=50)
prob.model.add_objective('paraboloid.f_xy')
prob.setup()
prob.run_driver()
# minimum value
assert_rel_error(self, prob['paraboloid.f_xy'], -27.33333, 1e-6)
# location of the minimum
assert_rel_error(self, prob['indeps.x'], 6.6667, 1e-4)
assert_rel_error(self, prob['indeps.y'], -7.33333, 1e-4)
def test_debug_print_option_totals(self):
prob = Problem()
model = prob.model = Group()
model.add_subsystem('p1', IndepVarComp('x', 50.0), promotes=['*'])
model.add_subsystem('p2', IndepVarComp('y', 50.0), promotes=['*'])
model.add_subsystem('comp', Paraboloid(), promotes=['*'])
model.add_subsystem('con', ExecComp('c = - x + y'), promotes=['*'])
prob.set_solver_print(level=0)
prob.driver = ScipyOptimizeDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1e-9
prob.driver.options['disp'] = False
prob.driver.options['debug_print'] = ['totals']
model.add_design_var('x', lower=-50.0, upper=50.0)
model.add_design_var('y', lower=-50.0, upper=50.0)
model.add_objective('f_xy')
model.add_constraint('c', upper=-15.0)
prob.setup(check=False)
failed, output = run_driver(prob)
self.assertFalse(failed, "Optimization failed.")
self.assertTrue('Solving variable: comp.f_xy' in output)
self.assertTrue('Solving variable: con.c' in output)
def test_index_number(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(self.recorder)
prob.setup()
prob.run_driver()
prob.cleanup()
data_dict = _get_viewer_data(self.filename, case_id=3)
vals = data_dict['tree']['children'][2]['children']
x_val = vals[0]['val']
y_val = vals[1]['val']
f_xy_val = vals[2]['val']
self.assertEqual(x_val, "N/A")
self.assertEqual(y_val, "N/A")
self.assertEqual(f_xy_val, np.array([27.]))
def setup_and_run_simple_problem(self,
driver=None,
reports=_UNDEFINED,
reports_dir=_UNDEFINED):
prob = om.Problem(reports=reports, reports_dir=reports_dir)
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')
if driver:
prob.driver = driver
else:
prob.driver = om.ScipyOptimizeDriver()
prob.setup(False)
prob.run_driver()
prob.cleanup()
return prob
def test_simple_paraboloid_scaled_objective_fwd(self):
prob = Problem()
model = prob.model = Group()
prob.set_solver_print(level=0)
model.add_subsystem('p1', IndepVarComp('x', 50.0), promotes=['*'])
model.add_subsystem('p2', IndepVarComp('y', 50.0), promotes=['*'])
model.add_subsystem('comp', Paraboloid(), promotes=['*'])
model.add_subsystem('con', ExecComp('c = x - y'), promotes=['*'])
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1e-9
prob.driver.options['disp'] = False
model.add_design_var('x', lower=-50.0, upper=50.0)
model.add_design_var('y', lower=-50.0, upper=50.0)
model.add_objective('f_xy', ref=10.)
model.add_constraint('c', lower=10.0, upper=11.0)
prob.setup(check=False, mode='fwd')
prob.run_driver()
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['x'] - prob['y'], 11.0, 1e-6)
def test_feature_numpyvec_setup(self):
from openmdao.api import Problem, Group, IndepVarComp
from openmdao.test_suite.components.paraboloid import Paraboloid
prob = Problem()
model = prob.model = Group()
model.add_subsystem('p1', IndepVarComp('x', 0.0), promotes=['x'])
model.add_subsystem('p2', IndepVarComp('y', 0.0), promotes=['y'])
model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])
prob.setup()
prob['x'] = 2.
prob['y'] = 10.
prob.run_model()
assert_rel_error(self, prob['f_xy'], 214.0, 1e-6)
prob['x'] = 0.
prob['y'] = 0.
prob.run_model()
assert_rel_error(self, prob['f_xy'], 22.0, 1e-6)
# skip the setup error checking
prob.setup(check=False)
prob['x'] = 4
prob['y'] = 8.
prob.run_model()
assert_rel_error(self, prob['f_xy'], 174.0, 1e-6)
def test_simple_paraboloid_unconstrained_COBYLA(self):
prob = Problem()
model = prob.model = Group()
model.add_subsystem('p1', IndepVarComp('x', 50.0), promotes=['*'])
model.add_subsystem('p2', IndepVarComp('y', 50.0), promotes=['*'])
model.add_subsystem('comp', Paraboloid(), promotes=['*'])
prob.set_solver_print(level=0)
prob.driver = ScipyOptimizeDriver()
prob.driver.options['optimizer'] = 'COBYLA'
prob.driver.options['tol'] = 1e-9
prob.driver.options['disp'] = False
model.add_design_var('x', lower=-50.0, upper=50.0)
model.add_design_var('y', lower=-50.0, upper=50.0)
model.add_objective('f_xy')
prob.setup(check=False)
failed = prob.run_driver()
self.assertFalse(
failed,
"Optimization failed, result =\n" + str(prob.driver.result))
assert_rel_error(self, prob['x'], 6.66666667, 1e-6)
assert_rel_error(self, prob['y'], -7.3333333, 1e-6)
def test_feature_simple_run_once_compute_totals(self):
from openmdao.api import Problem, Group, IndepVarComp
from openmdao.test_suite.components.paraboloid import Paraboloid
prob = Problem()
model = prob.model = Group()
model.add_subsystem('p1', IndepVarComp('x', 3.0))
model.add_subsystem('p2', IndepVarComp('y', -4.0))
model.add_subsystem('comp', Paraboloid())
model.connect('p1.x', 'comp.x')
model.connect('p2.y', 'comp.y')
prob.setup()
prob.run_model()
totals = prob.compute_totals(of=['comp.f_xy'], wrt=['p1.x', 'p2.y'])
assert_rel_error(self, totals[('comp.f_xy', 'p1.x')][0][0], -4.0)
assert_rel_error(self, totals[('comp.f_xy', 'p2.y')][0][0], 3.0)
totals = prob.compute_totals(of=['comp.f_xy'],
wrt=['p1.x', 'p2.y'],
return_format='dict')
assert_rel_error(self, totals['comp.f_xy']['p1.x'][0][0], -4.0)
assert_rel_error(self, totals['comp.f_xy']['p2.y'][0][0], 3.0)
def test_feature_optimizer(self):
from openmdao.api import Problem, Group, IndepVarComp, ScipyOptimizeDriver
from openmdao.test_suite.components.paraboloid import Paraboloid
prob = Problem()
model = prob.model = Group()
model.add_subsystem('p1', IndepVarComp('x', 50.0), promotes=['*'])
model.add_subsystem('p2', IndepVarComp('y', 50.0), promotes=['*'])
model.add_subsystem('comp', Paraboloid(), promotes=['*'])
prob.driver = ScipyOptimizeDriver()
prob.driver.options['optimizer'] = 'COBYLA'
prob.driver.options['disp'] = True
model.add_design_var('x', lower=-50.0, upper=50.0)
model.add_design_var('y', lower=-50.0, upper=50.0)
model.add_objective('f_xy')
prob.setup(check=False)
prob.run_driver()
assert_rel_error(self, prob['x'], 6.66666667, 1e-6)
assert_rel_error(self, prob['y'], -7.3333333, 1e-6)
def test_simple_paraboloid_scaled_objective_rev(self):
prob = Problem()
model = prob.model = Group()
prob.set_solver_print(level=0)
model.add_subsystem('p1', IndepVarComp('x', 50.0), promotes=['*'])
model.add_subsystem('p2', IndepVarComp('y', 50.0), promotes=['*'])
model.add_subsystem('comp', Paraboloid(), promotes=['*'])
model.add_subsystem('con', ExecComp('c = x - y'), promotes=['*'])
prob.driver = ScipyOptimizeDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1e-9
prob.driver.options['disp'] = False
model.add_design_var('x', lower=-50.0, upper=50.0)
model.add_design_var('y', lower=-50.0, upper=50.0)
model.add_objective('f_xy', ref=10.)
model.add_constraint('c', lower=10.0, upper=11.0)
prob.setup(check=False, mode='rev')
failed = prob.run_driver()
self.assertFalse(
failed,
"Optimization failed, result =\n" + str(prob.driver.result))
assert_rel_error(self, prob['x'] - prob['y'], 11.0, 1e-6)
def test_simple_paraboloid_equality_linear(self):
prob = Problem()
model = prob.model = Group()
model.add_subsystem('p1', IndepVarComp('x', 50.0), promotes=['*'])
model.add_subsystem('p2', IndepVarComp('y', 50.0), promotes=['*'])
model.add_subsystem('comp', Paraboloid(), promotes=['*'])
model.add_subsystem('con', ExecComp('c = - x + y'), promotes=['*'])
prob.set_solver_print(level=0)
prob.driver = ScipyOptimizeDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1e-9
prob.driver.options['disp'] = False
model.add_design_var('x', lower=-50.0, upper=50.0)
model.add_design_var('y', lower=-50.0, upper=50.0)
model.add_objective('f_xy')
model.add_constraint('c', equals=-15.0, linear=True)
prob.setup(check=False)
failed = prob.run_driver()
self.assertFalse(
failed,
"Optimization failed, result =\n" + str(prob.driver.result))
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['x'], 7.16667, 1e-6)
assert_rel_error(self, prob['y'], -7.833334, 1e-6)
def test_unsupported_equality(self):
prob = Problem()
model = prob.model = Group()
model.add_subsystem('p1', IndepVarComp('x', 50.0), promotes=['*'])
model.add_subsystem('p2', IndepVarComp('y', 50.0), promotes=['*'])
model.add_subsystem('comp', Paraboloid(), promotes=['*'])
model.add_subsystem('con', ExecComp('c = - x + y'), promotes=['*'])
prob.set_solver_print(level=0)
prob.driver = ScipyOptimizeDriver()
prob.driver.options['optimizer'] = 'COBYLA'
prob.driver.options['tol'] = 1e-9
prob.driver.options['disp'] = False
model.add_design_var('x', lower=-50.0, upper=50.0)
model.add_design_var('y', lower=-50.0, upper=50.0)
model.add_objective('f_xy')
model.add_constraint('c', equals=-15.0)
prob.setup(check=False)
with self.assertRaises(Exception) as raises_cm:
prob.run_driver()
exception = raises_cm.exception
msg = "Constraints of type 'eq' not handled by COBYLA."
self.assertEqual(exception.args[0], msg)
def test_simple_paraboloid_equality(self):
prob = Problem()
model = prob.model = Group()
model.add_subsystem('p1', IndepVarComp('x', 50.0), promotes=['*'])
model.add_subsystem('p2', IndepVarComp('y', 50.0), promotes=['*'])
model.add_subsystem('comp', Paraboloid(), promotes=['*'])
model.add_subsystem('con', ExecComp('c = - x + y'), promotes=['*'])
prob.set_solver_print(level=0)
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1e-9
prob.driver.options['disp'] = False
model.add_design_var('x', lower=-50.0, upper=50.0)
model.add_design_var('y', lower=-50.0, upper=50.0)
model.add_objective('f_xy')
model.add_constraint('c', equals=-15.0)
prob.setup(check=False)
prob.run_driver()
# Minimum should be at (7.166667, -7.833334)
# (Note, loose tol because of appveyor py3.4 machine.)
assert_rel_error(self, prob['x'], 7.16667, 1e-4)
assert_rel_error(self, prob['y'], -7.833334, 1e-4)
def test_debug_print_option(self):
from openmdao.api import Problem, Group, IndepVarComp, ScipyOptimizeDriver, ExecComp
from openmdao.test_suite.components.paraboloid import Paraboloid
prob = Problem()
model = prob.model = Group()
model.add_subsystem('p1', IndepVarComp('x', 50.0), promotes=['*'])
model.add_subsystem('p2', IndepVarComp('y', 50.0), promotes=['*'])
model.add_subsystem('comp', Paraboloid(), promotes=['*'])
model.add_subsystem('con', ExecComp('c = - x + y'), promotes=['*'])
prob.set_solver_print(level=0)
prob.driver = ScipyOptimizeDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1e-9
prob.driver.options['disp'] = False
prob.driver.options['debug_print'] = [
'desvars', 'ln_cons', 'nl_cons', 'objs'
]
model.add_design_var('x', lower=-50.0, upper=50.0)
model.add_design_var('y', lower=-50.0, upper=50.0)
model.add_objective('f_xy')
model.add_constraint('c', upper=-15.0)
prob.setup(check=False)
prob.run_driver()
def test_simple_driver_recording(self, m):
self.setup_endpoints(m)
recorder = WebRecorder(self._accepted_token, suppress_output=True)
prob = Problem()
model = prob.model = Group()
model.add_subsystem('p1', IndepVarComp('x', 50.0), promotes=['*'])
model.add_subsystem('p2', IndepVarComp('y', 50.0), promotes=['*'])
model.add_subsystem('comp', Paraboloid(), promotes=['*'])
model.add_subsystem('con', ExecComp('c = - x + y'), promotes=['*'])
model.suppress_solver_output = True
prob.driver = pyOptSparseDriver()
prob.driver.add_recorder(recorder)
prob.driver.recording_options['record_desvars'] = True
prob.driver.recording_options['record_responses'] = True
prob.driver.recording_options['record_objectives'] = True
prob.driver.recording_options['record_constraints'] = True
prob.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SLSQP':
prob.driver.opt_settings['ACC'] = 1e-9
model.add_design_var('x', lower=-50.0, upper=50.0)
model.add_design_var('y', lower=-50.0, upper=50.0)
model.add_objective('f_xy')
model.add_constraint('c', upper=-15.0)
prob.setup(check=False)
t0, t1 = run_driver(prob)
prob.cleanup()
driver_iteration_data = json.loads(self.driver_iteration_data)
expected_desvars = [
{'name': 'p1.x', 'values': [7.1666666]},
{'name': 'p2.y', 'values': [-7.8333333]}
]
expected_objectives = [
{'name': 'comp.f_xy', 'values': [-27.083333]}
]
expected_constraints = [
{'name': 'con.c', 'values': [-15.0]}
]
for d in expected_desvars:
self.assert_array_close(d, driver_iteration_data['desvars'])
for o in expected_objectives:
self.assert_array_close(o, driver_iteration_data['objectives'])
for c in expected_constraints:
self.assert_array_close(c, driver_iteration_data['constraints'])
def test_full_factorial(self):
prob = Problem()
model = prob.model
model.add_subsystem('p1', IndepVarComp('x', 0.0), promotes=['x'])
model.add_subsystem('p2', 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 = DOEDriver(FullFactorialGenerator(levels=3), parallel=True)
prob.driver.add_recorder(SqliteRecorder("CASES.db"))
prob.setup()
failed, output = run_driver(prob)
self.assertFalse(failed)
prob.cleanup()
expected = {
0: {'x': np.array([0.]), 'y': np.array([0.]), 'f_xy': np.array([22.00])},
1: {'x': np.array([.5]), 'y': np.array([0.]), 'f_xy': np.array([19.25])},
2: {'x': np.array([1.]), 'y': np.array([0.]), 'f_xy': np.array([17.00])},
3: {'x': np.array([0.]), 'y': np.array([.5]), 'f_xy': np.array([26.25])},
4: {'x': np.array([.5]), 'y': np.array([.5]), 'f_xy': np.array([23.75])},
5: {'x': np.array([1.]), 'y': np.array([.5]), 'f_xy': np.array([21.75])},
6: {'x': np.array([0.]), 'y': np.array([1.]), 'f_xy': np.array([31.00])},
7: {'x': np.array([.5]), 'y': np.array([1.]), 'f_xy': np.array([28.75])},
8: {'x': np.array([1.]), 'y': np.array([1.]), 'f_xy': np.array([27.00])},
}
size = prob.comm.size
rank = prob.comm.rank
# cases will be split across files for each proc
filename = "CASES.db_%d" % rank
expect_msg = "Cases from rank %d are being written to %s." % (rank, filename)
self.assertTrue(expect_msg in output)
cases = CaseReader(filename).driver_cases
# cases recorded on this proc
num_cases = cases.num_cases
self.assertEqual(num_cases, len(expected)//size+(rank<len(expected)%size))
for n in range(num_cases):
case = cases.get_case(n)
idx = n * size + rank # index of expected case
self.assertEqual(cases.get_case(n).outputs['x'], expected[idx]['x'])
self.assertEqual(cases.get_case(n).outputs['y'], expected[idx]['y'])
self.assertEqual(cases.get_case(n).outputs['f_xy'], expected[idx]['f_xy'])
def test_debug_print_option(self):
prob = Problem()
model = prob.model = Group()
model.add_subsystem('p1', IndepVarComp('x', 50.0), promotes=['*'])
model.add_subsystem('p2', IndepVarComp('y', 50.0), promotes=['*'])
model.add_subsystem('comp', Paraboloid(), promotes=['*'])
model.add_subsystem('con', ExecComp('c = - x + y'), promotes=['*'])
prob.set_solver_print(level=0)
prob.driver = ScipyOptimizeDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1e-9
prob.driver.options['disp'] = False
prob.driver.options['debug_print'] = [
'desvars', 'ln_cons', 'nl_cons', 'objs'
]
model.add_design_var('x', lower=-50.0, upper=50.0)
model.add_design_var('y', lower=-50.0, upper=50.0)
model.add_objective('f_xy')
model.add_constraint('c', upper=-15.0)
prob.setup(check=False)
failed, output = run_driver(prob)
self.assertFalse(failed, "Optimization failed.")
output = output.split('\n')
self.assertTrue(
output.count("Design Vars") > 1,
"Should be more than one design vars header printed")
self.assertTrue(
output.count("Nonlinear constraints") > 1,
"Should be more than one nonlinear constraint header printed")
self.assertTrue(
output.count("Linear constraints") > 1,
"Should be more than one linear constraint header printed")
self.assertTrue(
output.count("Objectives") > 1,
"Should be more than one objective header printed")
self.assertTrue(
len([s for s in output if s.startswith("{'p1.x")]) > 1,
"Should be more than one p1.x printed")
self.assertTrue(
len([s for s in output if "'p2.y'" in s]) > 1,
"Should be more than one p2.y printed")
self.assertTrue(
len([s for s in output if s.startswith("{'con.c")]) > 1,
"Should be more than one con.c printed")
self.assertTrue(
len([s for s in output if s.startswith("{'comp.f_xy")]) > 1,
"Should be more than one comp.f_xy printed")
def test_latin_hypercube_center(self):
samples = 4
upper = 10.
prob = Problem()
model = prob.model
indep = model.add_subsystem('indep', IndepVarComp())
indep.add_output('x', 0.0)
indep.add_output('y', 0.0)
model.add_subsystem('comp', Paraboloid())
model.connect('indep.x', 'comp.x')
model.connect('indep.y', 'comp.y')
model.add_design_var('indep.x', lower=0., upper=upper)
model.add_design_var('indep.y', lower=0., upper=upper)
model.add_objective('comp.f_xy')
prob.driver = DOEDriver(LatinHypercubeGenerator(samples=samples, criterion='c'))
prob.driver.add_recorder(SqliteRecorder("CASES.db"))
prob.setup(check=False)
prob.run_driver()
prob.cleanup()
cases = CaseReader("CASES.db").driver_cases
self.assertEqual(cases.num_cases, samples)
# the sample space for each variable (0 to upper) should be divided into
# equal size buckets and each variable should have a value in each bucket
bucket_size = upper/samples
all_buckets = set(range(samples))
x_buckets_filled = set()
y_buckets_filled = set()
# with criterion of 'center', each value should be in the center of it's bucket
valid_values = [round(bucket_size*(bucket + 1/2), 3) for bucket in all_buckets]
for n in range(cases.num_cases):
x = float(cases.get_case(n).outputs['indep.x'])
y = float(cases.get_case(n).outputs['indep.y'])
x_buckets_filled.add(int(x/bucket_size))
y_buckets_filled.add(int(y/bucket_size))
self.assertTrue(round(x, 3) in valid_values, '%f not in %s' % (x, valid_values))
self.assertTrue(round(y, 3) in valid_values, '%f not in %s' % (y, valid_values))
self.assertEqual(x_buckets_filled, all_buckets)
self.assertEqual(y_buckets_filled, all_buckets)
def test_feature_simple_run_once_input_input(self):
from openmdao.api import Problem, Group, IndepVarComp
from openmdao.test_suite.components.paraboloid import Paraboloid
prob = Problem()
model = prob.model = Group()
model.add_subsystem('p1', IndepVarComp('x', 3.0))
#promote the two inputs to the same name
model.add_subsystem('comp1', Paraboloid(), promotes_inputs=['x'])
model.add_subsystem('comp2', Paraboloid(), promotes_inputs=['x'])
#connect the source to the common name
model.connect('p1.x', 'x')
prob.setup()
prob.run_model()
assert_rel_error(self, prob['comp1.f_xy'], 13.0)
assert_rel_error(self, prob['comp2.f_xy'], 13.0)
def __init__(self, **kwargs):
super().__init__(**kwargs)
model = self.model
model.add_subsystem('p1', om.IndepVarComp('x', 50.0), promotes=['*'])
model.add_subsystem('p2', om.IndepVarComp('y', 50.0), promotes=['*'])
model.add_subsystem('comp', Paraboloid(), promotes=['*'])
model.add_subsystem('con', om.ExecComp('c = - x + y'), promotes=['*'])
model.add_design_var('x', lower=-50.0, upper=50.0)
model.add_design_var('y', lower=-50.0, upper=50.0)
model.add_objective('f_xy')
model.add_constraint('c', upper=-15.0)
def test_plackett_burman(self):
prob = Problem()
model = prob.model
model.add_subsystem('p1', IndepVarComp('x', 0.0), promotes=['x'])
model.add_subsystem('p2', 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 = DOEDriver(PlackettBurmanGenerator())
prob.driver.add_recorder(SqliteRecorder("cases.sql"))
prob.setup(check=False)
prob.run_driver()
prob.cleanup()
expected = {
0: {
'x': np.array([0.]),
'y': np.array([0.]),
'f_xy': np.array([22.00])
},
1: {
'x': np.array([1.]),
'y': np.array([0.]),
'f_xy': np.array([17.00])
},
2: {
'x': np.array([0.]),
'y': np.array([1.]),
'f_xy': np.array([31.00])
},
3: {
'x': np.array([1.]),
'y': np.array([1.]),
'f_xy': np.array([27.00])
},
}
cases = CaseReader("cases.sql").driver_cases
self.assertEqual(cases.num_cases, 4)
for n in range(cases.num_cases):
outputs = cases.get_case(n).outputs
self.assertEqual(outputs['x'], expected[n]['x'])
self.assertEqual(outputs['y'], expected[n]['y'])
self.assertEqual(outputs['f_xy'], expected[n]['f_xy'])
def test_uniform(self):
prob = Problem()
model = prob.model
model.add_subsystem('p1', IndepVarComp('x', 0.), promotes=['*'])
model.add_subsystem('p2', 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 = DOEDriver(UniformGenerator(num_samples=5, seed=0))
prob.driver.add_recorder(SqliteRecorder("cases.sql"))
prob.setup(check=False)
prob.run_driver()
prob.cleanup()
# all values should be between -10 and 10, check expected values for seed = 0
expected = {
0: {
'x': np.array([0.97627008]),
'y': np.array([4.30378733])
},
1: {
'x': np.array([2.05526752]),
'y': np.array([0.89766366])
},
2: {
'x': np.array([-1.52690401]),
'y': np.array([2.91788226])
},
3: {
'x': np.array([-1.24825577]),
'y': np.array([7.83546002])
},
4: {
'x': np.array([9.27325521]),
'y': np.array([-2.33116962])
},
}
cases = CaseReader("cases.sql").driver_cases
self.assertEqual(cases.num_cases, 5)
for n in range(cases.num_cases):
outputs = cases.get_case(n).outputs
assert_rel_error(self, outputs['x'], expected[n]['x'], 1e-4)
assert_rel_error(self, outputs['y'], expected[n]['y'], 1e-4)
def test_ListRecorder():
from openmdao.api import Problem, IndepVarComp
from openmdao.test_suite.components.paraboloid import Paraboloid
prob = Problem()
model = prob.model
model.add_subsystem('p1', IndepVarComp('x', 0.), promotes=['*'])
model.add_subsystem('p2', 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')
xyf = [[0.98, 4.30, 74.1844],
[2.06, 0.90, 23.7476],
[-1.53, 2.92, 60.9397],
[-1.25, 7.84, 145.4481]]
prob.driver = DOEDriver(ListGenerator([[('x', xy[0]), ('y', xy[1])] for xy in xyf]))
recorder = TopFarmListRecorder()
recorder._initialize_database()
recorder._cleanup_abs2meta()
recorder.record_iteration_problem(None, None, None)
recorder.record_iteration_system(None, None, None)
recorder.record_iteration_solver(None, None, None)
recorder.record_viewer_data(None)
recorder.record_metadata_solver(None)
recorder.record_derivatives_driver(None, None, None)
recorder.shutdown()
prob.driver.add_recorder(recorder)
prob.driver.recording_options['record_desvars'] = True
prob.driver.recording_options['includes'] = ['*']
prob.driver.recording_options['record_inputs'] = True
prob.setup()
prob.run_driver()
prob.cleanup()
assert recorder.num_cases == 4
npt.assert_array_equal(recorder.get('counter'), range(1, 5))
npt.assert_array_equal(recorder['counter'], range(1, 5))
npt.assert_array_almost_equal(recorder.get(['x', 'y', 'f_xy']), xyf, 4)
for xyf, k in zip(xyf[0], ['x', 'y', 'f_xy']):
npt.assert_allclose(recorder[k][0], xyf)
with pytest.raises(KeyError, match="missing"):
recorder.get('missing')
def test_feature_set_indeps(self):
prob = Problem()
model = prob.model = Group()
model.add_subsystem('p1', IndepVarComp('x', 0.0), promotes=['x'])
model.add_subsystem('p2', IndepVarComp('y', 0.0), promotes=['y'])
model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])
prob.setup()
prob['x'] = 2.
prob['y'] = 10.
prob.run_model()
assert_rel_error(self, prob['f_xy'], 214.0, 1e-6)
def test_full_factorial(self):
from openmdao.api import Problem, IndepVarComp
from openmdao.test_suite.components.paraboloid import Paraboloid
from openmdao.api import DOEDriver, FullFactorialGenerator
from openmdao.api import SqliteRecorder, CaseReader
from mpi4py import MPI
prob = Problem()
model = prob.model
model.add_subsystem('p1', IndepVarComp('x', 0.0), promotes=['x'])
model.add_subsystem('p2', 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 = DOEDriver(FullFactorialGenerator(levels=3))
prob.driver.options['parallel'] = True
prob.driver.add_recorder(SqliteRecorder("cases.sql"))
prob.setup()
prob.run_driver()
prob.cleanup()
self.assertEqual(MPI.COMM_WORLD.size, 2)
# check recorded cases from each case file
rank = MPI.COMM_WORLD.rank
filename = "cases.sql_%d" % rank
self.assertEqual(filename, "cases.sql_%d" % rank)
cases = CaseReader(filename).driver_cases
self.assertEqual(cases.num_cases, 5 if rank == 0 else 4)
values = []
for n in range(cases.num_cases):
outputs = cases.get_case(n).outputs
values.append((outputs['x'], outputs['y'], outputs['f_xy']))
self.assertEqual(
"\n" + "\n".join([
"x: %5.2f, y: %5.2f, f_xy: %6.2f" % (x, y, f_xy)
for x, y, f_xy in values
]), self.expect_text)
def test_feature_petsc_setup(self):
prob = Problem()
model = prob.model = Group()
model.add_subsystem('p1', IndepVarComp('x', 0.0), promotes=['x'])
model.add_subsystem('p2', IndepVarComp('y', 0.0), promotes=['y'])
model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])
# use PETScVector when using any PETSc linear solvers or running under MPI
prob.setup(vector_class=PETScVector)
prob['x'] = 2.
prob['y'] = 10.
prob.run_model()
assert_rel_error(self, prob['f_xy'], 214.0, 1e-6)
