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test_calc_gradient.py
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test_calc_gradient.py
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""" Unit tests for the calc_gradient method on Problem. """
from __future__ import print_function
import unittest
import numpy as np
from numpy.testing import assert_almost_equal
from six import text_type, PY3
from openmdao.api import Problem, Group, IndepVarComp, ExecComp
from openmdao.test.simple_comps import RosenSuzuki, FanIn
if PY3:
def py3fix(s):
return s.replace('<type', '<class')
else:
def py3fix(s):
return s
#
# expected jacobian
#
expectedJ = {
'comp.f': {
'parm.x': np.array([
[ -3., -3., -17., 9.]
])
},
'comp.g': {
'parm.x': np.array([
[ 3., 1., 3., 1.],
[ 1., 4., 2., 3.],
[ 6., 1., 2., -1.],
])
}
}
expectedJ_array = np.concatenate((
expectedJ['comp.f']['parm.x'],
expectedJ['comp.g']['parm.x']
))
class TestCalcGradient(unittest.TestCase):
def test_calc_gradient_interface_errors(self):
root = Group()
prob = Problem(root=root)
root.add('comp', ExecComp('y=x*2.0'))
try:
prob.calc_gradient(['comp.x'], ['comp.y'], mode='junk')
except Exception as error:
msg = "mode must be 'auto', 'fwd', 'rev', or 'fd'"
self.assertEqual(text_type(error), msg)
else:
self.fail("Error expected")
try:
prob.calc_gradient(['comp.x'], ['comp.y'], return_format='junk')
except Exception as error:
msg = "return_format must be 'array' or 'dict'"
self.assertEqual(text_type(error), msg)
else:
self.fail("Error expected")
def test_calc_gradient(self):
root = Group()
root.add('parm', IndepVarComp('x', np.array([1., 1., 1., 1.])))
root.add('comp', RosenSuzuki())
root.connect('parm.x', 'comp.x')
prob = Problem(root)
prob.driver.add_desvar('parm.x', lower=-10, upper=99)
prob.driver.add_objective('comp.f')
prob.driver.add_constraint('comp.g', upper=0.)
prob.setup(check=False)
prob.run()
indep_list = ['parm.x']
unknown_list = ['comp.f', 'comp.g']
# check that calc_gradient returns proper dict value when mode is 'fwd'
J = prob.calc_gradient(indep_list, unknown_list, mode='fwd', return_format='dict')
assert_almost_equal(J['comp.f']['parm.x'], expectedJ['comp.f']['parm.x'])
assert_almost_equal(J['comp.g']['parm.x'], expectedJ['comp.g']['parm.x'])
# check that calc_gradient returns proper array value when mode is 'fwd'
J = prob.calc_gradient(indep_list, unknown_list, mode='fwd', return_format='array')
assert_almost_equal(J, expectedJ_array)
# check that calc_gradient returns proper dict value when mode is 'rev'
J = prob.calc_gradient(indep_list, unknown_list, mode='rev', return_format='dict')
assert_almost_equal(J['comp.f']['parm.x'], expectedJ['comp.f']['parm.x'])
assert_almost_equal(J['comp.g']['parm.x'], expectedJ['comp.g']['parm.x'])
# check that calc_gradient returns proper array value when mode is 'rev'
J = prob.calc_gradient(indep_list, unknown_list, mode='rev', return_format='array')
assert_almost_equal(J, expectedJ_array)
# check that calc_gradient returns proper dict value when mode is 'fd'
J = prob.calc_gradient(indep_list, unknown_list, mode='fd', return_format='dict')
assert_almost_equal(J['comp.f']['parm.x'], expectedJ['comp.f']['parm.x'], decimal=5)
assert_almost_equal(J['comp.g']['parm.x'], expectedJ['comp.g']['parm.x'], decimal=5)
# check that calc_gradient returns proper array value when mode is 'fd'
J = prob.calc_gradient(indep_list, unknown_list, mode='fd', return_format='array')
assert_almost_equal(J, expectedJ_array, decimal=5)
def test_calc_gradient_with_poi_indices(self):
p_idxs = [0, 1, 2, 4]
root = Group()
root.add('parm', IndepVarComp('x', np.array([1., 1., 1., 2., 1.])))
root.add('comp', RosenSuzuki())
root.connect('parm.x', 'comp.x', src_indices=p_idxs)
prob = Problem(root)
prob.driver.add_desvar('parm.x', indices=p_idxs, lower=-10, upper=99)
prob.driver.add_objective('comp.f')
prob.driver.add_constraint('comp.g', upper=0.)
prob.setup(check=False)
prob.run()
indep_list = ['parm.x']
unknown_list = ['comp.f', 'comp.g']
# check that calc_gradient returns proper dict value when mode is 'fwd'
J = prob.calc_gradient(indep_list, unknown_list, mode='fwd', return_format='dict')
assert_almost_equal(J['comp.f']['parm.x'], expectedJ['comp.f']['parm.x'])
assert_almost_equal(J['comp.g']['parm.x'], expectedJ['comp.g']['parm.x'])
# check that calc_gradient returns proper array value when mode is 'fwd'
J = prob.calc_gradient(indep_list, unknown_list, mode='fwd', return_format='array')
assert_almost_equal(J, expectedJ_array)
# check that calc_gradient returns proper dict value when mode is 'rev'
J = prob.calc_gradient(indep_list, unknown_list, mode='rev', return_format='dict')
assert_almost_equal(J['comp.f']['parm.x'], expectedJ['comp.f']['parm.x'])
assert_almost_equal(J['comp.g']['parm.x'], expectedJ['comp.g']['parm.x'])
# check that calc_gradient returns proper array value when mode is 'rev'
J = prob.calc_gradient(indep_list, unknown_list, mode='rev', return_format='array')
assert_almost_equal(J, expectedJ_array)
# check that calc_gradient returns proper dict value when mode is 'fd'
J = prob.calc_gradient(indep_list, unknown_list, mode='fd', return_format='dict')
assert_almost_equal(J['comp.f']['parm.x'], expectedJ['comp.f']['parm.x'], decimal=5)
assert_almost_equal(J['comp.g']['parm.x'], expectedJ['comp.g']['parm.x'], decimal=5)
# check that calc_gradient returns proper array value when mode is 'fd'
J = prob.calc_gradient(indep_list, unknown_list, mode='fd', return_format='array')
assert_almost_equal(J, expectedJ_array, decimal=5)
def test_calc_gradient_with_qoi_indices(self):
q_idxs = [0, 2]
root = Group()
root.add('parm', IndepVarComp('x', np.array([1., 1., 1., 1.])))
root.add('comp', RosenSuzuki())
root.connect('parm.x', 'comp.x')
prob = Problem(root)
prob.driver.add_desvar('parm.x', lower=-10, upper=99)
prob.driver.add_objective('comp.f')
prob.driver.add_constraint('comp.g', upper=0., indices=q_idxs)
prob.setup(check=False)
prob.run()
indep_list = ['parm.x']
unknown_list = ['comp.f', 'comp.g']
# override expected array value to reflect qoi indices
expectedJ_array = np.concatenate((
expectedJ['comp.f']['parm.x'],
expectedJ['comp.g']['parm.x'][q_idxs, :]
))
# check that calc_gradient returns proper dict value when mode is 'fwd'
J = prob.calc_gradient(indep_list, unknown_list, mode='fwd', return_format='dict')
assert_almost_equal(J['comp.f']['parm.x'], expectedJ['comp.f']['parm.x'])
assert_almost_equal(J['comp.g']['parm.x'], expectedJ['comp.g']['parm.x'][q_idxs, :])
# check that calc_gradient returns proper array value when mode is 'fwd'
J = prob.calc_gradient(indep_list, unknown_list, mode='fwd', return_format='array')
assert_almost_equal(J, expectedJ_array)
# check that calc_gradient returns proper dict value when mode is 'rev'
J = prob.calc_gradient(indep_list, unknown_list, mode='rev', return_format='dict')
assert_almost_equal(J['comp.f']['parm.x'], expectedJ['comp.f']['parm.x'])
assert_almost_equal(J['comp.g']['parm.x'], expectedJ['comp.g']['parm.x'][q_idxs, :])
# check that calc_gradient returns proper array value when mode is 'rev'
J = prob.calc_gradient(indep_list, unknown_list, mode='rev', return_format='array')
assert_almost_equal(J, expectedJ_array)
# check that calc_gradient returns proper dict value when mode is 'fd'
J = prob.calc_gradient(indep_list, unknown_list, mode='fd', return_format='dict')
assert_almost_equal(J['comp.f']['parm.x'], expectedJ['comp.f']['parm.x'], decimal=5)
assert_almost_equal(J['comp.g']['parm.x'], expectedJ['comp.g']['parm.x'][q_idxs, :], decimal=5)
# check that calc_gradient returns proper array value when mode is 'fd'
J = prob.calc_gradient(indep_list, unknown_list, mode='fd', return_format='array')
assert_almost_equal(J, expectedJ_array, decimal=5)
def test_calc_gradient_multiple_params(self):
prob = Problem()
prob.root = FanIn()
prob.setup(check=False)
prob.run()
indep_list = ['p1.x1', 'p2.x2']
unknown_list = ['comp3.y']
# check that calc_gradient returns proper dict value when mode is 'fwd'
J = prob.calc_gradient(indep_list, unknown_list, mode='fwd', return_format='dict')
assert_almost_equal(J['comp3.y']['p2.x2'], np.array([[35.]]))
assert_almost_equal(J['comp3.y']['p1.x1'], np.array([[-6.]]))
# check that calc_gradient returns proper array value when mode is 'fwd'
J = prob.calc_gradient(indep_list, unknown_list, mode='fwd', return_format='array')
assert_almost_equal(J, np.array([[-6., 35.]]))
# check that calc_gradient returns proper dict value when mode is 'rev'
J = prob.calc_gradient(indep_list, unknown_list, mode='rev', return_format='dict')
assert_almost_equal(J['comp3.y']['p2.x2'], np.array([[35.]]))
assert_almost_equal(J['comp3.y']['p1.x1'], np.array([[-6.]]))
# check that calc_gradient returns proper array value when mode is 'rev'
J = prob.calc_gradient(indep_list, unknown_list, mode='rev', return_format='array')
assert_almost_equal(J, np.array([[-6., 35.]]))
# check that calc_gradient returns proper dict value when mode is 'fd'
J = prob.calc_gradient(indep_list, unknown_list, mode='fd', return_format='dict')
assert_almost_equal(J['comp3.y']['p2.x2'], np.array([[35.]]))
assert_almost_equal(J['comp3.y']['p1.x1'], np.array([[-6.]]))
# check that calc_gradient returns proper array value when mode is 'fd'
J = prob.calc_gradient(indep_list, unknown_list, mode='fd', return_format='array')
assert_almost_equal(J, np.array([[-6., 35.]]))
if __name__ == "__main__":
unittest.main()