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_report_generation_basic_doedriver(self):
# Test a driver that does not generate scaling report
setup_default_reports()
prob = self.setup_and_run_simple_problem(
driver=om.DOEDriver(om.PlackettBurmanGenerator()))
problem_reports_dir = pathlib.Path(_reports_dir).joinpath(prob._name)
path = pathlib.Path(problem_reports_dir).joinpath(self.n2_filename)
self.assertTrue(path.is_file(),
f'The N2 report file, {str(path)} was not found')
# DOEDriver won't cause the creation of a scaling report
path = pathlib.Path(problem_reports_dir).joinpath(
self.scaling_filename)
self.assertFalse(
path.is_file(),
f'The scaling report file, {str(path)}, was found but should not have'
)
def set_driver(self, wt_opt):
folder_output = self.opt["general"]["folder_output"]
if self.opt["driver"]["optimization"]["flag"]:
step_size = self._get_step_size()
# Solver has specific meaning in OpenMDAO
wt_opt.model.approx_totals(method="fd", step=step_size, form=self.opt["driver"]["optimization"]["form"])
# Set optimization solver and options. First, Scipy's SLSQP
if self.opt["driver"]["optimization"]["solver"] == "SLSQP":
wt_opt.driver = om.ScipyOptimizeDriver()
wt_opt.driver.options["optimizer"] = self.opt["driver"]["optimization"]["solver"]
wt_opt.driver.options["tol"] = self.opt["driver"]["optimization"]["tol"]
wt_opt.driver.options["maxiter"] = self.opt["driver"]["optimization"]["max_iter"]
# The next two optimization methods require pyOptSparse.
elif self.opt["driver"]["optimization"]["solver"] == "CONMIN":
try:
from openmdao.api import pyOptSparseDriver
except:
raise ImportError(
"You requested the optimization solver CONMIN, but you have not installed the pyOptSparseDriver. Please do so and rerun."
)
wt_opt.driver = pyOptSparseDriver()
wt_opt.driver.options["optimizer"] = self.opt["driver"]["optimization"]["solver"]
wt_opt.driver.opt_settings["ITMAX"] = self.opt["driver"]["optimization"]["max_iter"]
elif self.opt["driver"]["optimization"]["solver"] == "SNOPT":
try:
from openmdao.api import pyOptSparseDriver
except:
raise ImportError(
"You requested the optimization solver SNOPT, but you have not installed the pyOptSparseDriver. Please do so and rerun."
)
wt_opt.driver = pyOptSparseDriver()
try:
wt_opt.driver.options["optimizer"] = self.opt["driver"]["optimization"]["solver"]
except:
raise ImportError(
"You requested the optimization solver SNOPT, but you have not installed it within the pyOptSparseDriver. Please do so and rerun."
)
wt_opt.driver.opt_settings["Major optimality tolerance"] = float(
self.opt["driver"]["optimization"]["tol"]
)
wt_opt.driver.opt_settings["Major iterations limit"] = int(
self.opt["driver"]["optimization"]["max_major_iter"]
)
wt_opt.driver.opt_settings["Iterations limit"] = int(
self.opt["driver"]["optimization"]["max_minor_iter"]
)
wt_opt.driver.opt_settings["Major feasibility tolerance"] = float(
self.opt["driver"]["optimization"]["tol"]
)
wt_opt.driver.opt_settings["Summary file"] = os.path.join(folder_output, "SNOPT_Summary_file.txt")
wt_opt.driver.opt_settings["Print file"] = os.path.join(folder_output, "SNOPT_Print_file.txt")
if "hist_file_name" in self.opt["driver"]["optimization"]:
wt_opt.driver.hist_file = self.opt["driver"]["optimization"]["hist_file_name"]
if "verify_level" in self.opt["driver"]["optimization"]:
wt_opt.driver.opt_settings["Verify level"] = self.opt["driver"]["optimization"]["verify_level"]
else:
wt_opt.driver.opt_settings["Verify level"] = -1
# wt_opt.driver.declare_coloring()
if "hotstart_file" in self.opt["driver"]["optimization"]:
wt_opt.driver.hotstart_file = self.opt["driver"]["optimization"]["hotstart_file"]
else:
raise ValueError(
"The optimizer " + self.opt["driver"]["optimization"]["solver"] + "is not yet supported!"
)
elif self.opt["driver"]["design_of_experiments"]["flag"]:
if self.opt["driver"]["design_of_experiments"]["generator"].lower() == "uniform":
generator = om.UniformGenerator(
num_samples=self.opt["driver"]["design_of_experiments"]["num_samples"],
seed=self.opt["driver"]["design_of_experiments"]["seed"],
)
elif self.opt["driver"]["design_of_experiments"]["generator"].lower() == "fullfact":
generator = om.FullFactorialGenerator(levels=self.opt["driver"]["design_of_experiments"]["num_samples"])
elif self.opt["driver"]["design_of_experiments"]["generator"].lower() == "plackettburman":
generator = om.PlackettBurmanGenerator()
elif self.opt["driver"]["design_of_experiments"]["generator"].lower() == "boxbehnken":
generator = om.BoxBehnkenGenerator()
elif self.opt["driver"]["design_of_experiments"]["generator"].lower() == "latinhypercube":
generator = om.LatinHypercubeGenerator(
samples=self.opt["driver"]["design_of_experiments"]["num_samples"],
criterion=self.opt["driver"]["design_of_experiments"]["criterion"],
seed=self.opt["driver"]["design_of_experiments"]["seed"],
)
else:
raise Exception(
"The generator type {} is unsupported.".format(
self.opt["driver"]["design_of_experiments"]["generator"]
)
)
# Initialize driver
wt_opt.driver = om.DOEDriver(generator)
# options
wt_opt.driver.options["run_parallel"] = self.opt["driver"]["design_of_experiments"]["run_parallel"]
else:
raise Exception(
"Design variables are set to be optimized or studied, but no driver is selected. Please enable a driver."
)
return wt_opt