def test_pressure_drop_model_nlp(self):
m = self._create_pressure_drop_model()
cons = [m.c_con, m.F_con, m.Pin_con, m.P2_con]
inputs = [m.Pin, m.c, m.F]
outputs = [m.P2, m.Pout]
nlp = PyomoNLPWithGreyBoxBlocks(m)
n_primals = len(inputs) + len(outputs)
n_eq_con = len(cons) + len(outputs)
self.assertEqual(nlp.n_primals(), n_primals)
self.assertEqual(nlp.n_constraints(), n_eq_con)
constraint_names = [
"c_con",
"F_con",
"Pin_con",
"P2_con",
"egb.output_constraints[P2]",
"egb.output_constraints[Pout]",
]
primals = inputs + outputs
nlp_constraints = nlp.constraint_names()
nlp_vars = nlp.primals_names()
con_idx_map = {}
for name in constraint_names:
# Quadratic scan to get constraint indices is not ideal.
# Could this map be created while PyNLPwGBB is being constructed?
con_idx_map[name] = nlp_constraints.index(name)
var_idx_map = ComponentMap()
for var in primals:
name = var.name
var_idx_map[var] = nlp_vars.index(name)
incident_vars = {
con.name: list(identify_variables(con.expr))
for con in cons
}
incident_vars["egb.output_constraints[P2]"] = inputs + [outputs[0]]
incident_vars["egb.output_constraints[Pout]"] = inputs + [outputs[1]]
expected_nonzeros = set()
for con, varlist in incident_vars.items():
i = con_idx_map[con]
for var in varlist:
j = var_idx_map[var]
expected_nonzeros.add((i, j))
self.assertEqual(len(expected_nonzeros), nlp.nnz_jacobian())
jac = nlp.evaluate_jacobian()
for i, j in zip(jac.row, jac.col):
self.assertIn((i, j), expected_nonzeros)
def test_compare_evaluations(self):
A1 = 5
A2 = 10
c1 = 3
c2 = 4
N = 6
dt = 1
m = create_pyomo_model(A1, A2, c1, c2, N, dt)
solver = pyo.SolverFactory('ipopt')
solver.options['linear_solver'] = 'mumps'
status = solver.solve(m, tee=False)
m_nlp = PyomoNLP(m)
mex = create_pyomo_external_grey_box_model(A1, A2, c1, c2, N, dt)
# mex_nlp = PyomoGreyBoxNLP(mex)
mex_nlp = PyomoNLPWithGreyBoxBlocks(mex)
# get the variable and constraint order and create the maps
# reliable order independent comparisons
m_x_order = m_nlp.primals_names()
m_c_order = m_nlp.constraint_names()
mex_x_order = mex_nlp.primals_names()
mex_c_order = mex_nlp.constraint_names()
x1list = [
'h1[0]', 'h1[1]', 'h1[2]', 'h1[3]', 'h1[4]', 'h1[5]', 'h2[0]',
'h2[1]', 'h2[2]', 'h2[3]', 'h2[4]', 'h2[5]', 'F1[1]', 'F1[2]',
'F1[3]', 'F1[4]', 'F1[5]', 'F2[1]', 'F2[2]', 'F2[3]', 'F2[4]',
'F2[5]', 'F12[0]', 'F12[1]', 'F12[2]', 'F12[3]', 'F12[4]',
'F12[5]', 'Fo[0]', 'Fo[1]', 'Fo[2]', 'Fo[3]', 'Fo[4]', 'Fo[5]'
]
x2list = [
'egb.inputs[h1_0]', 'egb.inputs[h1_1]', 'egb.inputs[h1_2]',
'egb.inputs[h1_3]', 'egb.inputs[h1_4]', 'egb.inputs[h1_5]',
'egb.inputs[h2_0]', 'egb.inputs[h2_1]', 'egb.inputs[h2_2]',
'egb.inputs[h2_3]', 'egb.inputs[h2_4]', 'egb.inputs[h2_5]',
'egb.inputs[F1_1]', 'egb.inputs[F1_2]', 'egb.inputs[F1_3]',
'egb.inputs[F1_4]', 'egb.inputs[F1_5]', 'egb.inputs[F2_1]',
'egb.inputs[F2_2]', 'egb.inputs[F2_3]', 'egb.inputs[F2_4]',
'egb.inputs[F2_5]', 'egb.outputs[F12_0]', 'egb.outputs[F12_1]',
'egb.outputs[F12_2]', 'egb.outputs[F12_3]', 'egb.outputs[F12_4]',
'egb.outputs[F12_5]', 'egb.outputs[Fo_0]', 'egb.outputs[Fo_1]',
'egb.outputs[Fo_2]', 'egb.outputs[Fo_3]', 'egb.outputs[Fo_4]',
'egb.outputs[Fo_5]'
]
x1_x2_map = dict(zip(x1list, x2list))
x1idx_x2idx_map = {
i: mex_x_order.index(x1_x2_map[m_x_order[i]])
for i in range(len(m_x_order))
}
c1list = [
'h1bal[1]', 'h1bal[2]', 'h1bal[3]', 'h1bal[4]', 'h1bal[5]',
'h2bal[1]', 'h2bal[2]', 'h2bal[3]', 'h2bal[4]', 'h2bal[5]',
'F12con[0]', 'F12con[1]', 'F12con[2]', 'F12con[3]', 'F12con[4]',
'F12con[5]', 'Focon[0]', 'Focon[1]', 'Focon[2]', 'Focon[3]',
'Focon[4]', 'Focon[5]', 'min_inflow[1]', 'min_inflow[2]',
'min_inflow[3]', 'min_inflow[4]', 'min_inflow[5]',
'max_outflow[0]', 'max_outflow[1]', 'max_outflow[2]',
'max_outflow[3]', 'max_outflow[4]', 'max_outflow[5]', 'h10', 'h20'
]
c2list = [
'egb.h1bal_1', 'egb.h1bal_2', 'egb.h1bal_3', 'egb.h1bal_4',
'egb.h1bal_5', 'egb.h2bal_1', 'egb.h2bal_2', 'egb.h2bal_3',
'egb.h2bal_4', 'egb.h2bal_5', 'egb.output_constraints[F12_0]',
'egb.output_constraints[F12_1]', 'egb.output_constraints[F12_2]',
'egb.output_constraints[F12_3]', 'egb.output_constraints[F12_4]',
'egb.output_constraints[F12_5]', 'egb.output_constraints[Fo_0]',
'egb.output_constraints[Fo_1]', 'egb.output_constraints[Fo_2]',
'egb.output_constraints[Fo_3]', 'egb.output_constraints[Fo_4]',
'egb.output_constraints[Fo_5]', 'min_inflow[1]', 'min_inflow[2]',
'min_inflow[3]', 'min_inflow[4]', 'min_inflow[5]',
'max_outflow[0]', 'max_outflow[1]', 'max_outflow[2]',
'max_outflow[3]', 'max_outflow[4]', 'max_outflow[5]', 'h10', 'h20'
]
c1_c2_map = dict(zip(c1list, c2list))
c1idx_c2idx_map = {
i: mex_c_order.index(c1_c2_map[m_c_order[i]])
for i in range(len(m_c_order))
}
# get the primals from m and put them in the correct order for mex
m_x = m_nlp.get_primals()
mex_x = np.zeros(len(m_x))
for i in range(len(m_x)):
mex_x[x1idx_x2idx_map[i]] = m_x[i]
# get the duals from m and put them in the correct order for mex
m_lam = m_nlp.get_duals()
mex_lam = np.zeros(len(m_lam))
for i in range(len(m_x)):
mex_lam[c1idx_c2idx_map[i]] = m_lam[i]
mex_nlp.set_primals(mex_x)
mex_nlp.set_duals(mex_lam)
m_obj = m_nlp.evaluate_objective()
mex_obj = mex_nlp.evaluate_objective()
self.assertAlmostEqual(m_obj, mex_obj, places=4)
m_gobj = m_nlp.evaluate_grad_objective()
mex_gobj = mex_nlp.evaluate_grad_objective()
check_vectors_specific_order(self, m_gobj, m_x_order, mex_gobj,
mex_x_order, x1_x2_map)
m_c = m_nlp.evaluate_constraints()
mex_c = mex_nlp.evaluate_constraints()
check_vectors_specific_order(self, m_c, m_c_order, mex_c, mex_c_order,
c1_c2_map)
m_j = m_nlp.evaluate_jacobian()
mex_j = mex_nlp.evaluate_jacobian().todense()
check_sparse_matrix_specific_order(self, m_j, m_c_order, m_x_order,
mex_j, mex_c_order, mex_x_order,
c1_c2_map, x1_x2_map)
m_h = m_nlp.evaluate_hessian_lag()
mex_h = mex_nlp.evaluate_hessian_lag()
check_sparse_matrix_specific_order(self, m_h, m_x_order, m_x_order,
mex_h, mex_x_order, mex_x_order,
x1_x2_map, x1_x2_map)
mex_h = 0 * mex_h
mex_nlp.evaluate_hessian_lag(out=mex_h)
check_sparse_matrix_specific_order(self, m_h, m_x_order, m_x_order,
mex_h, mex_x_order, mex_x_order,
x1_x2_map, x1_x2_map)
def test_set_and_evaluate(self):
m = pyo.ConcreteModel()
m.ex_block = ExternalGreyBoxBlock(concrete=True)
block = m.ex_block
m_ex = _make_external_model()
input_vars = [m_ex.a, m_ex.b, m_ex.r, m_ex.x_out, m_ex.y_out]
external_vars = [m_ex.x, m_ex.y]
residual_cons = [m_ex.c_out_1, m_ex.c_out_2]
external_cons = [m_ex.c_ex_1, m_ex.c_ex_2]
ex_model = ExternalPyomoModel(
input_vars,
external_vars,
residual_cons,
external_cons,
)
block.set_external_model(ex_model)
a = m.ex_block.inputs["input_0"]
b = m.ex_block.inputs["input_1"]
r = m.ex_block.inputs["input_2"]
x = m.ex_block.inputs["input_3"]
y = m.ex_block.inputs["input_4"]
m.obj = pyo.Objective(expr=(x - 2.0)**2 + (y - 2.0)**2 + (a - 2.0)**2 +
(b - 2.0)**2 + (r - 2.0)**2)
_add_linking_constraints(m)
nlp = PyomoNLPWithGreyBoxBlocks(m)
# Set primals in model, get primals in nlp
# set/get duals
# evaluate constraints
# evaluate Jacobian
# evaluate Hessian
self.assertEqual(nlp.n_primals(), 8)
# PyomoNLPWithGreyBoxBlocks sorts variables by name
primals_names = [
"a",
"b",
"ex_block.inputs[input_0]",
"ex_block.inputs[input_1]",
"ex_block.inputs[input_2]",
"ex_block.inputs[input_3]",
"ex_block.inputs[input_4]",
"r",
]
self.assertEqual(nlp.primals_names(), primals_names)
np.testing.assert_equal(np.zeros(8), nlp.get_primals())
primals = np.array([0, 1, 2, 3, 4, 5, 6, 7])
nlp.set_primals(primals)
np.testing.assert_equal(primals, nlp.get_primals())
nlp.load_state_into_pyomo()
for name, val in zip(primals_names, primals):
var = m.find_component(name)
self.assertEqual(var.value, val)
constraint_names = [
"linking_constraint[0]",
"linking_constraint[1]",
"linking_constraint[2]",
"ex_block.residual_0",
"ex_block.residual_1",
]
self.assertEqual(constraint_names, nlp.constraint_names())
residuals = np.array([
-2.0,
-2.0,
3.0,
# These values were obtained by solving the same system
# with Ipopt in another script. It may be better to do
# the solve in this test in case the system changes.
5.0 - (-3.03051522),
6.0 - 3.583839997,
])
np.testing.assert_allclose(residuals,
nlp.evaluate_constraints(),
rtol=1e-8)
duals = np.array([1, 2, 3, 4, 5])
nlp.set_duals(duals)
self.assertEqual(ex_model.residual_con_multipliers, [4, 5])
np.testing.assert_equal(nlp.get_duals(), duals)