def test_unsupported_multiple_obj(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('comp2', Paraboloid())
model.add_subsystem('con', ExecComp('c = - x + y'), promotes=['*'])
prob.set_solver_print(level=0)
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['gradient method'] = 'snopt_fd'
prob.driver.options['print_results'] = 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_objective('comp2.f_xy')
model.add_constraint('c', upper=-15.0)
expected = 'Multiple objectives have been added to pyOptSparseDriver' \
' but the selected optimizer (SLSQP) does not support' \
' multiple objectives.'
prob.setup(check=False)
with self.assertRaises(RuntimeError) as cm:
prob.final_setup()
self.assertEqual(str(cm.exception), expected)
def test_regression_bug_fix_issue_2062_sql_meta_file_running_parallel(self):
from openmdao.test_suite.components.paraboloid import Paraboloid
prob = om.Problem()
prob.model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])
prob.model.add_design_var('x', lower=0.0, upper=1.0)
prob.model.add_design_var('y', lower=0.0, upper=1.0)
prob.model.add_objective('f_xy')
prob.driver = om.DOEDriver(om.FullFactorialGenerator(levels=3))
prob.driver.options['run_parallel'] = True
prob.driver.options['procs_per_model'] = 1
prob.driver.add_recorder(om.SqliteRecorder("cases.sql"))
prob.setup()
prob.run_driver()
prob.cleanup()
# Run this again. Because of the bug fix for issue 2062, this code should NOT
# throw an exception
prob = om.Problem()
prob.model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])
prob.model.add_design_var('x', lower=0.0, upper=1.0)
prob.model.add_design_var('y', lower=0.0, upper=1.0)
prob.model.add_objective('f_xy')
prob.driver = om.DOEDriver(om.FullFactorialGenerator(levels=3))
prob.driver.options['run_parallel'] = True
prob.driver.options['procs_per_model'] = 1
prob.driver.add_recorder(om.SqliteRecorder("cases.sql"))
prob.setup()
if prob.comm.rank == 0:
expected_warnings = [
(UserWarning,
'The existing case recorder metadata file, cases.sql_meta, '
'is being overwritten.'),
(UserWarning,
'The existing case recorder file, cases.sql_0, is being '
'overwritten.'),
]
else:
expected_warnings = [
(UserWarning,
'The existing case recorder file, cases.sql_1, is being '
'overwritten.'),
]
with assert_warnings(expected_warnings):
prob.run_driver()
prob.cleanup()
def test_sql_meta_file_exists(self):
# Check that an existing sql_meta file will be deleted/overwritten
# if it already exists before a run. (see Issue #2062)
prob = om.Problem()
prob.model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])
prob.model.add_design_var('x', lower=0.0, upper=1.0)
prob.model.add_design_var('y', lower=0.0, upper=1.0)
prob.model.add_objective('f_xy')
prob.driver = om.DOEDriver(om.FullFactorialGenerator(levels=3))
prob.driver.options['run_parallel'] = True
prob.driver.options['procs_per_model'] = 1
prob.driver.add_recorder(om.SqliteRecorder("cases.sql"))
prob.setup()
prob.run_driver()
prob.cleanup()
# Run this again. It should NOT throw an exception.
prob = om.Problem()
prob.model.add_subsystem('comp', Paraboloid(), promotes=['x', 'y', 'f_xy'])
prob.model.add_design_var('x', lower=0.0, upper=1.0)
prob.model.add_design_var('y', lower=0.0, upper=1.0)
prob.model.add_objective('f_xy')
prob.driver = om.DOEDriver(om.FullFactorialGenerator(levels=3))
prob.driver.options['run_parallel'] = True
prob.driver.options['procs_per_model'] = 1
prob.driver.add_recorder(om.SqliteRecorder("cases.sql"))
prob.setup()
if prob.comm.rank == 0:
expected_warnings = [
(UserWarning,
'The existing case recorder metadata file, cases.sql_meta, '
'is being overwritten.'),
(UserWarning,
'The existing case recorder file, cases.sql_0, is being '
'overwritten.'),
]
else:
expected_warnings = [
(UserWarning,
'The existing case recorder file, cases.sql_1, is being '
'overwritten.'),
]
with assert_warnings(expected_warnings):
prob.run_driver()
prob.cleanup()
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_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_snopt_fd_option_error(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 = pyOptSparseDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['gradient method'] = 'snopt_fd'
prob.driver.options['print_results'] = 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', upper=-15.0)
prob.setup(check=False)
with self.assertRaises(Exception) as raises_cm:
prob.run_driver()
exception = raises_cm.exception
msg = "SNOPT's internal finite difference can only be used with SNOPT"
self.assertEqual(exception.args[0], msg)
def test_CMAESDriver_missing_objective(self):
prob = om.Problem()
prob.model.add_subsystem('x',
om.IndepVarComp('x', 2.0),
promotes=['*'])
prob.model.add_subsystem('f_x', Paraboloid(), promotes=['*'])
cma_es = CMAES()
cma_es.options['verbose'] = -9 # silence output
prob.driver = GenericDriver(algorithm=cma_es)
prob.model.add_design_var('x', lower=0)
prob.model.add_constraint('x', lower=0)
prob.setup()
with self.assertRaises(Exception) as raises_msg:
prob.run_driver()
exception = raises_msg.exception
msg = "Driver requires objective to be declared"
self.assertEqual(exception.args[0], msg)
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_inf_as_desvar_bounds(self):
# User may use np.inf as a bound. It is unneccessary, but the user
# may do it anyway, so make sure SLSQP doesn't blow up with it (bug
# reported by rfalck)
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 = pyOptSparseDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.opt_settings['ACC'] = 1e-9
prob.driver.options['print_results'] = False
model.add_design_var('x', lower=-np.inf, upper=np.inf)
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()
# 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_compute_totals_basic_return_array(self):
# Make sure 'array' 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'])
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, mode='fwd')
prob.set_solver_print(level=0)
prob.run_driver()
of = ['comp.f_xy']
wrt = ['p1.x', 'p2.y']
derivs = prob.driver._compute_totals(of=of, wrt=wrt, return_format='array')
assert_rel_error(self, derivs[0, 0], -6.0, 1e-6)
assert_rel_error(self, derivs[0, 1], 8.0, 1e-6)
prob.setup(check=False, mode='rev')
prob.run_model()
of = ['comp.f_xy']
wrt = ['p1.x', 'p2.y']
derivs = prob.driver._compute_totals(of=of, wrt=wrt, return_format='array')
assert_rel_error(self, derivs[0, 0], -6.0, 1e-6)
assert_rel_error(self, derivs[0, 1], 8.0, 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_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._subjacs
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, vector_class=vec_class, 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._subjacs
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_unconstrained(self):
from openmdao.test_suite.components.paraboloid import Paraboloid
# build the model
prob = om.Problem()
indeps = prob.model.add_subsystem('indeps', om.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 = om.ScipyOptimizeDriver(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', index=0)
prob.setup()
prob.run_driver()
# minimum value
assert_near_equal(prob['paraboloid.f_xy'], -27.33333, 1e-6)
# location of the minimum
assert_near_equal(prob['indeps.x'], 6.6667, 1e-4)
assert_near_equal(prob['indeps.y'], -7.33333, 1e-4)
# just make sure this doesn't raise an exception
prob.driver.scaling_report(show_browser=False)
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.db"))
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.db").driver_cases
self.assertEqual(cases.num_cases, 4)
for n in range(cases.num_cases):
self.assertEqual(cases.get_case(n).outputs['x'], expected[n]['x'])
self.assertEqual(cases.get_case(n).outputs['y'], expected[n]['y'])
self.assertEqual(cases.get_case(n).outputs['f_xy'], expected[n]['f_xy'])
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]['value']
y_val = vals[1]['value']
f_xy_val = vals[2]['value']
self.assertEqual(x_val, "N/A")
self.assertEqual(y_val, "N/A")
self.assertEqual(f_xy_val, np.array([27.]))
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_feature_basic(self):
from openmdao.api import Problem, Group, IndepVarComp, ScipyOptimizer
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 = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1e-9
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()
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(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_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_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):
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_upper(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 = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SLSQP':
prob.driver.opt_settings['ACC'] = 1e-9
prob.driver.options['print_results'] = 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', upper=-15.0)
prob.setup(check=False)
prob.run_driver()
# 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_snopt_fd_solution(self):
if OPTIMIZER is not 'SNOPT':
raise unittest.SkipTest()
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 = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
prob.driver.options['gradient method'] = 'snopt_fd'
prob.driver.options['print_results'] = 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', upper=-15.0)
prob.setup(check=False)
prob.run_driver()
# 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_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.db"))
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.db").driver_cases
self.assertEqual(cases.num_cases, 5)
for n in range(cases.num_cases):
assert_rel_error(self, cases.get_case(n).outputs['x'], expected[n]['x'], 1e-4)
assert_rel_error(self, cases.get_case(n).outputs['y'], expected[n]['y'], 1e-4)
def test_unconstrainted(self):
from openmdao.api import Problem, ScipyOptimizer, 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 = ScipyOptimizer()
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_uniform(self):
from openmdao.api import Problem, IndepVarComp
from openmdao.test_suite.components.paraboloid import Paraboloid
from openmdao.api import DOEDriver, UniformGenerator, SqliteRecorder, CaseReader
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))
prob.driver.add_recorder(SqliteRecorder("CASES.db"))
prob.setup()
prob.run_driver()
prob.cleanup()
cases = CaseReader("CASES.db").driver_cases
self.assertEqual(cases.num_cases, 5)
values = []
for n in range(cases.num_cases):
case = cases.get_case(n)
values.append((case.outputs['x'], case.outputs['y'], case.outputs['f_xy']))
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 = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SNOPT':
prob.driver.opt_settings['Verify level'] = 3
prob.driver.options['print_results'] = 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')
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_tol(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['tol'] = 1.0e-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')
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)
