Problem Recording

You might also want to attach a recorder to the Problem itself. This allows you to record an arbitrary case at a point of your choosing. This feature can be useful if you only record a limited number of variables during the run but would like to see a more complete list of values after the run.

The options are a subset of those for driver recording.

Option	Default	Acceptable Values	Acceptable Types	Description
excludes	[]	N/A	['list']	Patterns for vars to exclude in recording (processed post-includes). Uses fnmatch wildcards
includes	['*']	N/A	['list']	Patterns for variables to include in recording. Uses fnmatch wildcards
record_abs_error	True	[True, False]	['bool']	Set to True to record absolute error of model nonlinear solver
record_constraints	True	[True, False]	['bool']	Set to True to record constraints at the problem level
record_derivatives	False	[True, False]	['bool']	Set to True to record derivatives for the problem level
record_desvars	True	[True, False]	['bool']	Set to True to record design variables at the problem level
record_inputs	False	[True, False]	['bool']	Set True to record inputs at the problem level.
record_objectives	True	[True, False]	['bool']	Set to True to record objectives at the problem level
record_outputs	True	[True, False]	['bool']	Set True to record outputs at the problem level.
record_rel_error	True	[True, False]	['bool']	Set to True to record relative error of model nonlinear solver
record_residuals	False	[True, False]	['bool']	Set True to record residuals at the problem level.
record_responses	False	[True, False]	['bool']	Set True to record constraints and objectives at the problem level.

Note

Note that the excludes option takes precedence over the includes option.

Problem Recording Example

SellarDerivatives class definition

class SellarDerivatives(om.Group):
    """
    Group containing the Sellar MDA. This version uses the disciplines with derivatives.
    """

    def setup(self):
        self.add_subsystem('d1', SellarDis1withDerivatives(), promotes=['x', 'z', 'y1', 'y2'])
        self.add_subsystem('d2', SellarDis2withDerivatives(), promotes=['z', 'y1', 'y2'])

        self.add_subsystem('obj_cmp', om.ExecComp('obj = x**2 + z[1] + y1 + exp(-y2)', obj=0.0,
                                                  x=0.0, z=np.array([0.0, 0.0]), y1=0.0, y2=0.0),
                           promotes=['obj', 'x', 'z', 'y1', 'y2'])

        self.add_subsystem('con_cmp1', om.ExecComp('con1 = 3.16 - y1', con1=0.0, y1=0.0),
                           promotes=['con1', 'y1'])
        self.add_subsystem('con_cmp2', om.ExecComp('con2 = y2 - 24.0', con2=0.0, y2=0.0),
                           promotes=['con2', 'y2'])

        self.set_input_defaults('x', 1.0)
        self.set_input_defaults('z', np.array([5.0, 2.0]))

import openmdao.api as om
from openmdao.test_suite.components.sellar_feature import SellarDerivatives

import numpy as np

model = SellarDerivatives()

model.nonlinear_solver = om.NonlinearBlockGS()
model.linear_solver = om.ScipyKrylov()


model.add_design_var('z', lower=np.array([-10.0, 0.0]),
                          upper=np.array([10.0, 10.0]))
model.add_design_var('x', lower=0.0, upper=10.0)
model.add_objective('obj')
model.add_constraint('con1', upper=0.0)
model.add_constraint('con2', upper=0.0)

driver = om.ScipyOptimizeDriver(optimizer='SLSQP', tol=1e-9)

prob = om.Problem(model, driver)

prob.add_recorder(om.SqliteRecorder("cases.sql"))

prob.recording_options['includes'] = []
prob.recording_options['record_objectives'] = True
prob.recording_options['record_constraints'] = True
prob.recording_options['record_desvars'] = True

prob.setup()
prob.run_driver()

NL: NLBGS Converged in 8 iterations

NL: NLBGS Converged in 1 iterations

NL: NLBGS Converged in 9 iterations

NL: NLBGS Converged in 10 iterations

NL: NLBGS Converged in 10 iterations

NL: NLBGS Converged in 9 iterations

NL: NLBGS Converged in 6 iterations

Optimization terminated successfully    (Exit mode 0)
            Current function value: 3.183393951728078
            Iterations: 6
            Function evaluations: 6
            Gradient evaluations: 6
Optimization Complete
-----------------------------------

Problem: problem2
Driver:  ScipyOptimizeDriver
  success     : True
  iterations  : 7
  runtime     : 3.5201E-02 s
  model_evals : 7
  model_time  : 9.2148E-03 s
  deriv_evals : 6
  deriv_time  : 2.0545E-02 s
  exit_status : SUCCESS

prob.record('final')
prob.cleanup()

cr = om.CaseReader("cases.sql")

# get list of cases recorded on problem
problem_cases = cr.list_cases('problem')

problem
final

# get list of output variables recorded on problem
cr.list_source_vars('problem')

inputs	outputs	residuals
	z
	x
	con1
	con2
	obj

{'inputs': [], 'outputs': ['z', 'x', 'con1', 'con2', 'obj'], 'residuals': []}

# get the recorded case
case = cr.get_case('final')

case.get_objectives()

{'obj': array([3.18339395])}

case.get_design_vars()

{'z': array([1.97763888, 0.        ]), 'x': array([3.56410563e-15])}

case.get_constraints()

{'con1': array([-8.86162255e-11]), 'con2': array([-20.24472223])}