# Constraints:
for fc in flowCases:
opt_prob.addCon('cl_con_'+fc, type='i', lower=0.0, upper=0.0,
scale=10, wrt=['geo','alpha_'+fc])
# Geometric Constraints
DVCon.addConstraintsPyOpt(opt_prob)
# Add Objective
opt_prob.addObj('cd')
# Check opt problem:
if MPI.COMM_WORLD.rank == 0:
print(opt_prob)
opt_prob.printSparsity()
# The MP object needs the 'obj' and 'sens' function for each proc set,
# the optimization problem and what the objcon function is:
MP.setProcSetObjFunc('cruise', cruiseObj)
MP.setProcSetSensFunc('cruise',cruiseSens)
MP.setOptProb(opt_prob)
MP.setObjCon(objCon)
# Make Instance of Optimizer
snopt = pySNOPT.SNOPT(options=optOptions)
# Run Optimization
hist_file = output_directory + '/opt_hist'
snopt(opt_prob, MP.sens, store_hst=hist_file)
ap.addVariablesPyOpt(optProb)
# Add DVGeo variables
DVGeo.addVariablesPyOpt(optProb)
# Add constraints
DVCon.addConstraintsPyOpt(optProb)
optProb.addCon("cl_con_" + ap.name, lower=0.0, upper=0.0, scale=1.0)
# The MP object needs the 'obj' and 'sens' function for each proc set,
# the optimization problem and what the objcon function is:
MP.setProcSetObjFunc("cruise", cruiseFuncs)
MP.setProcSetSensFunc("cruise", cruiseFuncsSens)
MP.setObjCon(objCon)
MP.setOptProb(optProb)
optProb.printSparsity()
# rst optprob (end)
# rst optimizer
# Set up optimizer
if args.opt == "SLSQP":
optOptions = {"IFILE": os.path.join(args.output, "SLSQP.out")}
elif args.opt == "SNOPT":
optOptions = {
"Major feasibility tolerance": 1e-4,
"Major optimality tolerance": 1e-4,
"Difference interval": 1e-3,
"Hessian full memory": None,
"Function precision": 1e-8,
"Print file": os.path.join(args.output, "SNOPT_print.out"),
"Summary file": os.path.join(args.output, "SNOPT_summary.out"),
}
class TestOptProb(unittest.TestCase):
def objfunc(self, xdict):
"""
This is a simple quadratic test function with linear constraints.
The actual problem doesn't really matter, since we are not testing optimization,
but just optProb. However, we need to initialize and run an optimization
in order to have optimizer-specific fields in optProb populated, such as
jacIndices.
This problem is probably not feasible, but that's okay.
"""
funcs = {}
funcs["obj_0"] = 0
for x in xdict.keys():
funcs["obj_0"] += np.sum(np.power(xdict[x], 2))
for iCon, nc in enumerate(self.nCon):
conName = "con_{}".format(iCon)
funcs[conName] = np.zeros(nc)
for x in xdict.keys():
for j in range(nc):
funcs[conName][j] = (iCon + 1) * np.sum(xdict[x])
return funcs, False
def setup_optProb(self,
nObj=1,
nDV=[4],
nCon=[2],
xScale=[1.0],
objScale=[1.0],
conScale=[1.0],
offset=[0.0]):
"""
This function sets up a general optimization problem, with arbitrary
DVs, constraints and objectives.
Arbitrary scaling for the various parameters can also be specified.
"""
self.nObj = nObj
self.nDV = nDV
self.nCon = nCon
self.xScale = xScale
self.objScale = objScale
self.conScale = conScale
self.offset = offset
# Optimization Object
self.optProb = Optimization("Configurable Test Problem", self.objfunc)
self.x0 = {}
# Design Variables
for iDV in range(len(nDV)):
n = nDV[iDV]
lower = np.random.uniform(-5, 2, n)
upper = np.random.uniform(5, 20, n)
x0 = np.random.uniform(lower, upper)
dvName = "x{}".format(iDV)
self.x0[dvName] = x0
self.optProb.addVarGroup(
dvName,
n,
lower=lower,
upper=upper,
value=x0,
scale=xScale[iDV],
offset=offset[iDV],
)
# Constraints
for iCon in range(len(nCon)):
nc = nCon[iCon]
lower = np.random.uniform(-5, 2, nc)
upper = np.random.uniform(5, 6, nc)
self.optProb.addConGroup(
"con_{}".format(iCon),
nc,
lower=lower,
upper=upper,
scale=conScale[iCon],
)
# Objective
for iObj in range(nObj):
self.optProb.addObj("obj_{}".format(iObj), scale=objScale[iObj])
# Finalize
self.optProb.printSparsity()
# run optimization
# we don't care about outputs, but this performs optimizer-specific re-ordering
# of constraints so we need this to test mappings
opt = OPT("slsqp", options={"IFILE": "optProb_SLSQP.out"})
opt(self.optProb, "FD")
def test_setDV_getDV(self):
"""
We just test that setDV and getDV work, even with scaling
"""
self.setup_optProb(
nObj=1,
nDV=[4, 8],
nCon=[2, 3],
xScale=[4, 1],
objScale=[0.3],
conScale=[0.1, 8],
offset=[3, 7],
)
# test getDV first
x0 = self.optProb.getDVs()
self.assert_dict_allclose(x0, self.x0)
# now set, get, and compare
newDV = {"x0": np.arange(4), "x1": np.arange(8)}
self.optProb.setDVs(newDV)
outDV = self.optProb.getDVs()
self.assert_dict_allclose(newDV, outDV)
def test_setDV_VarGroup(self):
"""
Test that setDV works with a subset of VarGroups
"""
self.setup_optProb(
nObj=1,
nDV=[4, 8],
nCon=[2, 3],
xScale=[4, 1],
objScale=[0.3],
conScale=[0.1, 8],
offset=[3, 7],
)
oldDV = self.optProb.getDVs()
# set values for only one VarGroup
newDV = {"x0": np.arange(4)}
self.optProb.setDVs(newDV)
outDV = self.optProb.getDVs()
# check x0 changed
assert_allclose(newDV["x0"], outDV["x0"])
# check x1 is the same
assert_allclose(oldDV["x1"], outDV["x1"])
def test_mappings(self):
"""
This test checks the various mapping and process helper functions
in pyOpt_optimization. In this function we just set up an optimization problem,
and the actual test is done in `map_check_value`.
"""
nDV = [4, 8, 1]
nCon = [2, 3, 1, 1]
self.setup_optProb(
nObj=1,
nDV=nDV,
nCon=nCon,
xScale=[np.random.rand(i) for i in nDV],
objScale=[0.3],
conScale=[np.random.rand(i) for i in nCon],
offset=[np.random.rand(i) * np.arange(i) for i in nDV],
)
# first test X
x = self.optProb.getDVs()
self.map_check_value("X", x)
# next we check the objective
funcs, _ = self.objfunc(x)
obj_funcs = {}
for key in funcs.keys():
if "obj" in key:
obj_funcs[key] = funcs[key]
self.map_check_value("Obj", obj_funcs)
# lastly we check the constraints
funcs, _ = self.objfunc(x)
con_funcs = {}
for key in funcs.keys():
if "con" in key:
con_funcs[key] = funcs[key]
self.map_check_value("Con", con_funcs)
def map_check_value(self, key, val):
"""
This function checks all the mapping and process functions
in both directions, for a given key = {'X', 'Con', 'Obj'}
and val in dictionary format.
"""
# dictionary of function handles to test
map_funcs = {
"X": [self.optProb._mapXtoOpt, self.optProb._mapXtoUser],
"X_Dict":
[self.optProb._mapXtoOpt_Dict, self.optProb._mapXtoUser_Dict],
"Con": [self.optProb._mapContoOpt, self.optProb._mapContoUser],
"Con_Dict":
[self.optProb._mapContoOpt_Dict, self.optProb._mapContoUser_Dict],
"Obj": [self.optProb._mapObjtoOpt, self.optProb._mapObjtoUser],
"Obj_Dict":
[self.optProb._mapObjtoOpt_Dict, self.optProb._mapObjtoUser_Dict],
}
process_funcs = {
"X": {
"vec": self.optProb.processXtoVec,
"dict": self.optProb.processXtoDict
},
"Con": {
"vec": self.optProb.processContoVec,
"dict": self.optProb.processContoDict
},
"Obj": {
"vec": self.optProb.processObjtoVec,
"dict": self.optProb.processObjtoDict
},
}
def processValue(key, val, output):
"""helper function since some functions have optional arguments that are needed"""
if key == "Con":
return process_funcs[key][output](val,
scaled=False,
natural=True)
elif key == "Obj":
return process_funcs[key][output](val, scaled=False)
else:
return process_funcs[key][output](val)
# test dict to vec mappings
vec = processValue(key, val, "vec")
dictionary = processValue(key, vec, "dict")
self.assert_dict_allclose(val, dictionary)
# test mappings using dictionaries
val_opt = map_funcs[key + "_Dict"][0](val)
val_user = map_funcs[key + "_Dict"][1](val_opt)
self.assert_dict_allclose(val_user, val)
self.assert_dict_not_allclose(val_user, val_opt)
# test mappings using vectors
val = processValue(key, val, "vec")
val_opt = map_funcs[key][0](val)
val_user = map_funcs[key][1](val_opt)
assert_allclose(val_user, val, atol=tol, rtol=tol)
self.assert_not_allclose(val_user, val_opt)
# check that the scaling was actually done correctly
# we only check this for the array version because
# it's much simpler
if key == "X":
scale = np.hstack(self.xScale)
offset = np.hstack(self.offset)
assert_allclose(val_opt, (val_user - offset) * scale)
else:
if key == "Obj":
scale = np.hstack(self.objScale)
else:
scale = np.hstack(self.conScale)
assert_allclose(val_opt, val_user * scale)
def test_finalize(self):
"""
Check that multiple finalize calls don't mess up the optProb
"""
self.setup_optProb(nObj=1,
nDV=[4, 8],
nCon=[2, 3],
xScale=[1.0, 1.0],
conScale=[1.0, 1.0],
offset=[0, 0])
self.assert_optProb_size(1, 12, 5)
self.optProb.addObj("obj2")
self.assert_optProb_size(2, 12, 5)
self.optProb.addVar("DV2")
self.assert_optProb_size(2, 13, 5)
self.optProb.addCon("CON2")
self.assert_optProb_size(2, 13, 6)
def assert_optProb_size(self, nObj, nDV, nCon):
"""Checks that nObj, nDV and nCon are correct for self.optProb"""
self.optProb.finalize()
self.assertEqual(self.optProb.nObj, nObj)
self.assertEqual(self.optProb.nCon, nCon)
self.assertEqual(self.optProb.ndvs, nDV)
def assert_dict_allclose(self, actual, desired, atol=tol, rtol=tol):
"""
Simple assert for two flat dictionaries, where the values are
assumed to be numpy arrays
The keys are checked first to make sure that they match
"""
self.assertEqual(set(actual.keys()), set(desired.keys()))
for key in actual.keys():
assert_allclose(actual[key], desired[key], atol=atol, rtol=rtol)
def assert_dict_not_allclose(self, actual, desired, atol=tol, rtol=tol):
"""
The opposite of assert_dict_allclose
"""
self.assertEqual(set(actual.keys()), set(desired.keys()))
for key in actual.keys():
if np.allclose(actual[key], desired[key], atol=tol, rtol=tol):
raise AssertionError(
"Dictionaries are close! Inputs are {} and {}".format(
actual, desired))
def assert_not_allclose(self, actual, desired, atol=tol, rtol=tol):
"""
The numpy array version
"""
if np.allclose(actual, desired, atol=atol, rtol=tol):
raise AssertionError(
"Arrays are close! Inputs are {} and {}".format(
actual, desired))
