class PyomoNLPWithGreyBoxBlocks(NLP):
def __init__(self, pyomo_model):
super(PyomoNLPWithGreyBoxBlocks,self).__init__()
# get the list of all grey box blocks and build _ExternalGreyBoxAsNLP objects
greybox_components = []
# build a map from the names to the variable data objects
# this is done over *all* variables in active blocks, even
# if they are not included in this model
self._pyomo_model_var_names_to_datas = None
try:
# We support Pynumero's ExternalGreyBoxBlock modeling
# objects that are provided through ExternalGreyBoxBlock objects
# We reclassify these as Pyomo Block objects before building the
# PyomoNLP object to expose any variables on the block to
# the underlying Pyomo machinery
for greybox in pyomo_model.component_objects(
ExternalGreyBoxBlock, descend_into=True):
greybox.parent_block().reclassify_component_type(
greybox, pyo.Block)
greybox_components.append(greybox)
# store the pyomo model
self._pyomo_model = pyomo_model
# build a PyomoNLP object (will include the "pyomo"
# part of the model only)
self._pyomo_nlp = PyomoNLP(pyomo_model)
self._pyomo_model_var_names_to_datas = {
v.getname(
fully_qualified=True
): v
for v in pyomo_model.component_data_objects(
ctype=pyo.Var, descend_into=True
)
}
self._pyomo_model_constraint_names_to_datas = {
c.getname(
fully_qualified=True
): c
for c in pyomo_model.component_data_objects(
ctype=pyo.Constraint, descend_into=True
)
}
finally:
# Restore the ctypes of the ExternalGreyBoxBlock components
for greybox in greybox_components:
greybox.parent_block().reclassify_component_type(
greybox, ExternalGreyBoxBlock)
if self._pyomo_nlp.n_primals() == 0:
raise ValueError(
"No variables were found in the Pyomo part of the model."
" PyomoGreyBoxModel requires at least one variable"
" to be active in a Pyomo objective or constraint")
# build the list of NLP wrappers for the greybox objects
greybox_nlps = []
fixed_vars = []
for greybox in greybox_components:
# iterate through the data objects if component is indexed
for data in greybox.values():
if data.active:
# check that no variables are fixed
fixed_vars.extend(v for v in data.inputs.values() if v.fixed)
fixed_vars.extend(v for v in data.outputs.values() if v.fixed)
greybox_nlp = _ExternalGreyBoxAsNLP(data)
greybox_nlps.append(greybox_nlp)
if fixed_vars:
logging.getLogger(__name__).error('PyomoNLPWithGreyBoxBlocks found fixed variables for the'
' inputs and/or outputs of an ExternalGreyBoxBlock. This'
' is not currently supported. The fixed variables were:\n\t'
+ '\n\t'.join(f.getname(fully_qualified=True) for f in fixed_vars)
)
raise NotImplementedError('PyomoNLPWithGreyBoxBlocks does not support fixed inputs or outputs')
# let's build up the union of all the primal variables names
# RBP: Why use names here? Why not just ComponentSet of all
# data objects?
primals_names = set(self._pyomo_nlp.primals_names())
for gbnlp in greybox_nlps:
primals_names.update(gbnlp.primals_names())
# sort the names for consistency run to run
self._n_primals = len(primals_names)
self._primals_names = primals_names = sorted(primals_names)
self._pyomo_model_var_datas = [self._pyomo_model_var_names_to_datas[nm] for nm in self._primals_names]
# get the names of all the constraints
self._constraint_names = list(self._pyomo_nlp.constraint_names())
self._constraint_datas = [self._pyomo_model_constraint_names_to_datas.get(nm) for nm in self._constraint_names]
for gbnlp in greybox_nlps:
self._constraint_names.extend(gbnlp.constraint_names())
self._constraint_datas.extend([(gbnlp._block, nm) for nm in gbnlp.constraint_names()])
self._n_constraints = len(self._constraint_names)
self._has_hessian_support = True
for nlp in greybox_nlps:
if not nlp.has_hessian_support():
self._has_hessian_support = False
# wrap all the nlp objects with projected nlp objects
self._pyomo_nlp = ProjectedNLP(self._pyomo_nlp, primals_names)
for i,gbnlp in enumerate(greybox_nlps):
greybox_nlps[i] = ProjectedNLP(greybox_nlps[i], primals_names)
# build a list of all the nlps in order
self._nlps = nlps = [self._pyomo_nlp]
nlps.extend(greybox_nlps)
# build the primal and dual inits and lb, ub vectors
self._init_primals = self._pyomo_nlp.init_primals()
self._primals_lb = self._pyomo_nlp.primals_lb()
self._primals_ub = self._pyomo_nlp.primals_ub()
for gbnlp in greybox_nlps:
local = gbnlp.init_primals()
mask = ~np.isnan(local)
self._init_primals[mask] = local[mask]
local = gbnlp.primals_lb()
mask = ~np.isnan(local)
self._primals_lb[mask] = np.maximum(self._primals_lb[mask], local[mask])
local = gbnlp.primals_ub()
mask = ~np.isnan(local)
self._primals_ub[mask] = np.minimum(self._primals_ub[mask], local[mask])
# all the nan's should be gone (every primal should be initialized)
if np.any(np.isnan(self._init_primals)) \
or np.any(np.isnan(self._primals_lb)) \
or np.any(np.isnan(self._primals_ub)):
raise ValueError('NaN values found in initialization of primals or'
' primals_lb or primals_ub in _PyomoNLPWithGreyBoxBlocks.')
self._init_duals = BlockVector(len(nlps))
self._dual_values_blockvector = BlockVector(len(nlps))
self._constraints_lb = BlockVector(len(nlps))
self._constraints_ub = BlockVector(len(nlps))
for i,nlp in enumerate(nlps):
self._init_duals.set_block(i, nlp.init_duals())
self._constraints_lb.set_block(i, nlp.constraints_lb())
self._constraints_ub.set_block(i, nlp.constraints_ub())
self._dual_values_blockvector.set_block(i, np.nan*np.zeros(nlp.n_constraints()))
self._init_duals = self._init_duals.flatten()
self._constraints_lb = self._constraints_lb.flatten()
self._constraints_ub = self._constraints_ub.flatten()
# verify that there are no nans in the init_duals
if np.any(np.isnan(self._init_duals)) \
or np.any(np.isnan(self._constraints_lb)) \
or np.any(np.isnan(self._constraints_ub)):
raise ValueError('NaN values found in initialization of duals or'
' constraints_lb or constraints_ub in'
' _PyomoNLPWithGreyBoxBlocks.')
self._primal_values = np.nan*np.ones(self._n_primals)
# set the values of the primals and duals to make sure initial
# values get all the way through to the underlying models
self.set_primals(self._init_primals)
self.set_duals(self._init_duals)
assert not np.any(np.isnan(self._primal_values))
assert not np.any(np.isnan(self._dual_values_blockvector))
# if any of the problem is scaled (i.e., one or more of primals,
# constraints, or objective), then we want scaling factors for
# all of them (defaulted to 1)
need_scaling = False
# objective is owned by self._pyomo_nlp, not in any of the greybox models
self._obj_scaling = self._pyomo_nlp.get_obj_scaling()
if self._obj_scaling is None:
self._obj_scaling = 1.0
else:
need_scaling = True
self._primals_scaling = np.ones(self.n_primals())
scaling_suffix = pyomo_model.component('scaling_factor')
if scaling_suffix and scaling_suffix.ctype is pyo.Suffix:
need_scaling = True
for i,v in enumerate(self._pyomo_model_var_datas):
if v in scaling_suffix:
self._primals_scaling[i] = scaling_suffix[v]
self._constraints_scaling = BlockVector(len(nlps))
for i,nlp in enumerate(nlps):
local_constraints_scaling = nlp.get_constraints_scaling()
if local_constraints_scaling is None:
self._constraints_scaling.set_block(i, np.ones(nlp.n_constraints()))
else:
self._constraints_scaling.set_block(i, local_constraints_scaling)
need_scaling = True
if need_scaling:
self._constraints_scaling = self._constraints_scaling.flatten()
else:
self._obj_scaling = None
self._primals_scaling = None
self._constraints_scaling = None
# compute the jacobian and the hessian to get nnz
jac = self.evaluate_jacobian()
self._nnz_jacobian = len(jac.data)
self._sparse_hessian_summation = None
self._nnz_hessian_lag = None
if self._has_hessian_support:
hess = self.evaluate_hessian_lag()
self._nnz_hessian_lag = len(hess.data)
# overloaded from NLP
def n_primals(self):
return self._n_primals
# overloaded from NLP
def primals_names(self):
return self._primals_names
# overloaded from NLP
def n_constraints(self):
return self._n_constraints
# overloaded from NLP
def constraint_names(self):
return self._constraint_names
# overloaded from NLP
def nnz_jacobian(self):
return self._nnz_jacobian
# overloaded from NLP
def nnz_hessian_lag(self):
return self._nnz_hessian_lag
# overloaded from NLP
def primals_lb(self):
return self._primals_lb
# overloaded from NLP
def primals_ub(self):
return self._primals_ub
# overloaded from NLP
def constraints_lb(self):
return self._constraints_lb
# overloaded from NLP
def constraints_ub(self):
return self._constraints_ub
# overloaded from NLP
def init_primals(self):
return self._init_primals
# overloaded from NLP
def init_duals(self):
return self._init_duals
# overloaded from NLP / Extended NLP
def create_new_vector(self, vector_type):
if vector_type == 'primals':
return np.zeros(self.n_primals(), dtype=np.float64)
elif vector_type == 'constraints' or vector_type == 'duals':
return np.zeros(self.n_constraints(), dtype=np.float64)
else:
raise RuntimeError('Called create_new_vector with an unknown vector_type')
# overloaded from NLP
def set_primals(self, primals):
np.copyto(self._primal_values, primals)
for nlp in self._nlps:
nlp.set_primals(primals)
# overloaded from AslNLP
def get_primals(self):
return np.copy(self._primal_values)
# overloaded from NLP
def set_duals(self, duals):
self._dual_values_blockvector.copyfrom(duals)
for i,nlp in enumerate(self._nlps):
nlp.set_duals(self._dual_values_blockvector.get_block(i))
# overloaded from NLP
def get_duals(self):
return self._dual_values_blockvector.flatten()
# overloaded from NLP
def set_obj_factor(self, obj_factor):
# objective is owned by the pyomo model
self._pyomo_nlp.set_obj_factor(obj_factor)
# overloaded from NLP
def get_obj_factor(self):
# objective is owned by the pyomo model
return self._pyomo_nlp.get_obj_factor()
# overloaded from NLP
def get_obj_scaling(self):
return self._obj_scaling
# overloaded from NLP
def get_primals_scaling(self):
return self._primals_scaling
# overloaded from NLP
def get_constraints_scaling(self):
return self._constraints_scaling
# overloaded from NLP
def evaluate_objective(self):
# objective is owned by the pyomo model
return self._pyomo_nlp.evaluate_objective()
# overloaded from NLP
def evaluate_grad_objective(self, out=None):
return self._pyomo_nlp.evaluate_grad_objective(out=out)
# overloaded from NLP
def evaluate_constraints(self, out=None):
# todo: implement the "out" version more efficiently
ret = BlockVector(len(self._nlps))
for i,nlp in enumerate(self._nlps):
ret.set_block(i, nlp.evaluate_constraints())
if out is not None:
ret.copyto(out)
return out
return ret.flatten()
# overloaded from NLP
def evaluate_jacobian(self, out=None):
ret = BlockMatrix(len(self._nlps),1)
for i,nlp in enumerate(self._nlps):
ret.set_block(i, 0, nlp.evaluate_jacobian())
ret = ret.tocoo()
if out is not None:
assert np.array_equal(ret.row, out.row)
assert np.array_equal(ret.col, out.col)
np.copyto(out.data, ret.data)
return out
return ret
def evaluate_hessian_lag(self, out=None):
list_of_hessians = [nlp.evaluate_hessian_lag() for nlp in self._nlps]
if self._sparse_hessian_summation is None:
# This is assuming that the nonzero structures of Hessians
# do not change
self._sparse_hessian_summation = CondensedSparseSummation(list_of_hessians)
ret = self._sparse_hessian_summation.sum(list_of_hessians)
if out is not None:
assert np.array_equal(ret.row, out.row)
assert np.array_equal(ret.col, out.col)
np.copyto(out.data, ret.data)
return out
return ret
def report_solver_status(self, status_code, status_message):
raise NotImplementedError('This is not yet implemented.')
def load_state_into_pyomo(self, bound_multipliers=None):
# load the values of the primals into the pyomo
primals = self.get_primals()
for value,vardata in zip(primals, self._pyomo_model_var_datas):
vardata.set_value(value)
# get the active suffixes
m = self._pyomo_model
model_suffixes = dict(
pyo.suffix.active_import_suffix_generator(m))
# we need to correct the sign of the multipliers based on whether or
# not we are minimizing or maximizing - this is done in the ASL interface
# for ipopt, but does not appear to be done in cyipopt.
obj_sign = 1.0
objs = list(m.component_data_objects(ctype=pyo.Objective, descend_into=True))
assert len(objs) == 1
if objs[0].sense == pyo.maximize:
obj_sign = -1.0
if 'dual' in model_suffixes:
model_suffixes['dual'].clear()
dual_values = self._dual_values_blockvector.flatten()
for value,t in zip(dual_values, self._constraint_datas):
if type(t) is tuple:
model_suffixes['dual'].setdefault(t[0], {})[t[1]] = -obj_sign*value
else:
# t is a constraint data
model_suffixes['dual'][t] = -obj_sign*value
if 'ipopt_zL_out' in model_suffixes:
model_suffixes['ipopt_zL_out'].clear()
if bound_multipliers is not None:
model_suffixes['ipopt_zL_out'].update(
zip(self._pyomo_model_var_datas, obj_sign*bound_multipliers[0]))
if 'ipopt_zU_out' in model_suffixes:
model_suffixes['ipopt_zU_out'].clear()
if bound_multipliers is not None:
model_suffixes['ipopt_zU_out'].update(
zip(self._pyomo_model_var_datas, -obj_sign*bound_multipliers[1]))
def test_projected(self):
m = create_pyomo_model()
nlp = PyomoNLP(m)
projected_nlp = ProjectedNLP(nlp, ['x[0]', 'x[1]', 'x[2]'])
expected_names = ['x[0]', 'x[1]', 'x[2]']
self.assertEqual(projected_nlp.primals_names(), expected_names)
self.assertTrue(
np.array_equal(projected_nlp.get_primals(),
np.asarray([1.0, 2.0, 4.0])))
self.assertTrue(
np.array_equal(projected_nlp.evaluate_grad_objective(),
np.asarray([8.0, 1.0, 9.0])))
self.assertEqual(projected_nlp.nnz_jacobian(), 5)
self.assertEqual(projected_nlp.nnz_hessian_lag(), 6)
J = projected_nlp.evaluate_jacobian()
self.assertEqual(len(J.data), 5)
denseJ = J.todense()
expected_jac = np.asarray([[6.0, 1.0, 1.0], [1.0, 0.0, 1.0]])
self.assertTrue(np.array_equal(denseJ, expected_jac))
# test the use of "out"
J = 0.0 * J
projected_nlp.evaluate_jacobian(out=J)
denseJ = J.todense()
self.assertTrue(np.array_equal(denseJ, expected_jac))
H = projected_nlp.evaluate_hessian_lag()
self.assertEqual(len(H.data), 6)
expectedH = np.asarray([[2.0, 1.0, 1.0], [1.0, 0.0, 0.0],
[1.0, 0.0, 2.0]])
denseH = H.todense()
self.assertTrue(np.array_equal(denseH, expectedH))
# test the use of "out"
H = 0.0 * H
projected_nlp.evaluate_hessian_lag(out=H)
denseH = H.todense()
self.assertTrue(np.array_equal(denseH, expectedH))
# now test a reordering
projected_nlp = ProjectedNLP(nlp, ['x[0]', 'x[2]', 'x[1]'])
expected_names = ['x[0]', 'x[2]', 'x[1]']
self.assertEqual(projected_nlp.primals_names(), expected_names)
self.assertTrue(
np.array_equal(projected_nlp.get_primals(),
np.asarray([1.0, 4.0, 2.0])))
self.assertTrue(
np.array_equal(projected_nlp.evaluate_grad_objective(),
np.asarray([8.0, 9.0, 1.0])))
self.assertEqual(projected_nlp.nnz_jacobian(), 5)
self.assertEqual(projected_nlp.nnz_hessian_lag(), 6)
J = projected_nlp.evaluate_jacobian()
self.assertEqual(len(J.data), 5)
denseJ = J.todense()
expected_jac = np.asarray([[6.0, 1.0, 1.0], [1.0, 1.0, 0.0]])
self.assertTrue(np.array_equal(denseJ, expected_jac))
# test the use of "out"
J = 0.0 * J
projected_nlp.evaluate_jacobian(out=J)
denseJ = J.todense()
self.assertTrue(np.array_equal(denseJ, expected_jac))
H = projected_nlp.evaluate_hessian_lag()
self.assertEqual(len(H.data), 6)
expectedH = np.asarray([[2.0, 1.0, 1.0], [1.0, 2.0, 0.0],
[1.0, 0.0, 0.0]])
denseH = H.todense()
self.assertTrue(np.array_equal(denseH, expectedH))
# test the use of "out"
H = 0.0 * H
projected_nlp.evaluate_hessian_lag(out=H)
denseH = H.todense()
self.assertTrue(np.array_equal(denseH, expectedH))
# now test an expansion
projected_nlp = ProjectedNLP(nlp, ['x[0]', 'x[2]', 'y', 'x[1]'])
expected_names = ['x[0]', 'x[2]', 'y', 'x[1]']
self.assertEqual(projected_nlp.primals_names(), expected_names)
np.testing.assert_equal(projected_nlp.get_primals(),
np.asarray([1.0, 4.0, np.nan, 2.0]))
self.assertTrue(
np.array_equal(projected_nlp.evaluate_grad_objective(),
np.asarray([8.0, 9.0, 0.0, 1.0])))
self.assertEqual(projected_nlp.nnz_jacobian(), 5)
self.assertEqual(projected_nlp.nnz_hessian_lag(), 6)
J = projected_nlp.evaluate_jacobian()
self.assertEqual(len(J.data), 5)
denseJ = J.todense()
expected_jac = np.asarray([[6.0, 1.0, 0.0, 1.0], [1.0, 1.0, 0.0, 0.0]])
self.assertTrue(np.array_equal(denseJ, expected_jac))
# test the use of "out"
J = 0.0 * J
projected_nlp.evaluate_jacobian(out=J)
denseJ = J.todense()
self.assertTrue(np.array_equal(denseJ, expected_jac))
H = projected_nlp.evaluate_hessian_lag()
self.assertEqual(len(H.data), 6)
expectedH = np.asarray([[2.0, 1.0, 0.0, 1.0], [1.0, 2.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0]])
denseH = H.todense()
self.assertTrue(np.array_equal(denseH, expectedH))
# test the use of "out"
H = 0.0 * H
projected_nlp.evaluate_hessian_lag(out=H)
denseH = H.todense()
self.assertTrue(np.array_equal(denseH, expectedH))
# now test an expansion
projected_nlp = ProjectedNLP(nlp, ['x[0]', 'x[2]'])
expected_names = ['x[0]', 'x[2]']
self.assertEqual(projected_nlp.primals_names(), expected_names)
np.testing.assert_equal(projected_nlp.get_primals(),
np.asarray([1.0, 4.0]))
self.assertTrue(
np.array_equal(projected_nlp.evaluate_grad_objective(),
np.asarray([8.0, 9.0])))
self.assertEqual(projected_nlp.nnz_jacobian(), 4)
self.assertEqual(projected_nlp.nnz_hessian_lag(), 4)
J = projected_nlp.evaluate_jacobian()
self.assertEqual(len(J.data), 4)
denseJ = J.todense()
expected_jac = np.asarray([[6.0, 1.0], [1.0, 1.0]])
self.assertTrue(np.array_equal(denseJ, expected_jac))
# test the use of "out"
J = 0.0 * J
projected_nlp.evaluate_jacobian(out=J)
denseJ = J.todense()
self.assertTrue(np.array_equal(denseJ, expected_jac))
H = projected_nlp.evaluate_hessian_lag()
self.assertEqual(len(H.data), 4)
expectedH = np.asarray([[2.0, 1.0], [1.0, 2.0]])
denseH = H.todense()
self.assertTrue(np.array_equal(denseH, expectedH))
# test the use of "out"
H = 0.0 * H
projected_nlp.evaluate_hessian_lag(out=H)
denseH = H.todense()
self.assertTrue(np.array_equal(denseH, expectedH))