def test_change_of_objective_is_reflected_in_low_level_solver(self):
x = Variable('x', lb=-83.3, ub=1324422.)
y = Variable('y', lb=-181133.3, ub=12000.)
objective = Objective(0.3 * x + 0.4 * y, name='test', direction='max')
self.model.objective = objective
self.assertEqual((self.model.objective.expression -
(0.4 * y + 0.3 * x)).expand() - 0, 0)
self.assertEqual(self.model.objective.direction, "max")
self.assertEqual(glp_get_obj_coef(self.model.problem, x._index), 0.3)
self.assertEqual(glp_get_obj_coef(self.model.problem, y._index), 0.4)
for i in range(1, glp_get_num_cols(self.model.problem) + 1):
if i != x._index and i != y._index:
self.assertEqual(glp_get_obj_coef(self.model.problem, i), 0)
z = Variable('z', lb=4, ub=4, type='integer')
self.model.objective += 77. * z
self.assertEqual((self.model.objective.expression -
(0.4 * y + 0.3 * x + 77.0 * z)).expand() - 0, 0)
self.assertEqual(self.model.objective.direction, "max")
self.assertEqual(glp_get_obj_coef(self.model.problem, x._index), 0.3)
self.assertEqual(glp_get_obj_coef(self.model.problem, y._index), 0.4)
self.assertEqual(glp_get_obj_coef(self.model.problem, z._index), 77.)
for i in range(1, glp_get_num_cols(self.model.problem) + 1):
if i != x._index and i != y._index and i != z._index:
self.assertEqual(glp_get_obj_coef(self.model.problem, i), 0)
def test_change_of_objective_is_reflected_in_low_level_solver(self):
x = self.interface.Variable('x', lb=-83.3, ub=1324422.)
y = self.interface.Variable('y', lb=-181133.3, ub=12000.)
objective = self.interface.Objective(0.3 * x + 0.4 * y, name='test', direction='max')
self.model.objective = objective
self.assertEqual(
(self.model.objective.expression - (0.4 * y + 0.3 * x)).expand() - 0, 0
)
self.assertEqual(self.model.objective.direction, "max")
self.assertEqual(glp_get_obj_coef(self.model.problem, x._index), 0.3)
self.assertEqual(glp_get_obj_coef(self.model.problem, y._index), 0.4)
for i in range(1, glp_get_num_cols(self.model.problem) + 1):
if i != x._index and i != y._index:
self.assertEqual(glp_get_obj_coef(self.model.problem, i), 0)
z = self.interface.Variable('z', lb=4, ub=4, type='integer')
self.model.objective += 77. * z
self.assertEqual(
(self.model.objective.expression - (0.4 * y + 0.3 * x + 77.0 * z)).expand() - 0, 0
)
self.assertEqual(self.model.objective.direction, "max")
self.assertEqual(glp_get_obj_coef(self.model.problem, x._index), 0.3)
self.assertEqual(glp_get_obj_coef(self.model.problem, y._index), 0.4)
self.assertEqual(glp_get_obj_coef(self.model.problem, z._index), 77.)
for i in range(1, glp_get_num_cols(self.model.problem) + 1):
if i != x._index and i != y._index and i != z._index:
self.assertEqual(glp_get_obj_coef(self.model.problem, i), 0)
def get_linear_coefficients(self, variables):
if self.problem is not None:
return {
var: glp_get_obj_coef(self.problem.problem, var._index)
for var in variables
}
else:
raise Exception(
"Can't get coefficients from solver if objective is not in a model"
)
def test_imul_objective(self):
self.model.objective *= 2.
obj_coeff = list()
for i in range(len(self.model.variables)):
obj_coeff.append(glp_get_obj_coef(self.model.problem, i))
self.assertEqual(obj_coeff,
[0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0]
)
def test_iadd_objective(self):
v2, v3 = self.model.variables.values()[1:3]
self.model.objective += 2. * v2 - 3. * v3
obj_coeff = list()
for i in range(len(self.model.variables)):
obj_coeff.append(glp_get_obj_coef(self.model.problem, i))
self.assertEqual(obj_coeff,
[0.0, 1.0, 2.0, -3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0]
)
def test_imul_objective(self):
self.model.objective *= 2.
obj_coeff = list()
for i in range(len(self.model.variables)):
obj_coeff.append(glp_get_obj_coef(self.model.problem, i))
self.assertEqual(obj_coeff, [
0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0
])
def test_iadd_objective(self):
v2, v3 = self.model.variables.values()[1:3]
self.model.objective += 2. * v2 - 3. * v3
obj_coeff = list()
for i in range(len(self.model.variables)):
obj_coeff.append(glp_get_obj_coef(self.model.problem, i))
self.assertEqual(obj_coeff, [
0.0, 1.0, 2.0, -3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0
])
def _opt_dir_str(self, zero_print):
'get the optimization direction line for __str__'
lp = self.lp
cols = self.get_num_cols()
rv = "min"
for col in range(1, cols + 1):
val = glpk.glp_get_obj_coef(lp, col)
num = str(val)
num = num.rjust(6)[:6] # fix width to exactly 6
if val == 0:
rv += zero_print(num) + " "
else:
rv += num + " "
rv += "\n"
return rv
def _opt_dir_str(self, zero_print):
'get the optimization direction line for __str__'
lp = self.lp
cols = self.get_num_cols()
rv = "min"
for col in range(1, cols + 1):
val = glpk.glp_get_obj_coef(lp, col)
num = str(val)
if len(num) < 6:
num = (" " * (6 - len(num))) + num
else:
num = num[0:6]
if val == 0:
rv += zero_print(num) + " "
else:
rv += num + " "
rv += "\n"
return rv
def _initialize_model_from_problem(self, problem):
try:
self.problem = problem
glp_create_index(self.problem)
except TypeError:
raise TypeError("Provided problem is not a valid GLPK model.")
row_num = glp_get_num_rows(self.problem)
col_num = glp_get_num_cols(self.problem)
for i in range(1, col_num + 1):
var = Variable(
glp_get_col_name(self.problem, i),
lb=glp_get_col_lb(self.problem, i),
ub=glp_get_col_ub(self.problem, i),
problem=self,
type=_GLPK_VTYPE_TO_VTYPE[
glp_get_col_kind(self.problem, i)]
)
# This avoids adding the variable to the glpk problem
super(Model, self)._add_variables([var])
variables = self.variables
for j in range(1, row_num + 1):
ia = intArray(col_num + 1)
da = doubleArray(col_num + 1)
nnz = glp_get_mat_row(self.problem, j, ia, da)
constraint_variables = [variables[ia[i] - 1] for i in range(1, nnz + 1)]
# Since constraint expressions are lazily retrieved from the solver they don't have to be built here
# lhs = _unevaluated_Add(*[da[i] * constraint_variables[i - 1]
# for i in range(1, nnz + 1)])
lhs = 0
glpk_row_type = glp_get_row_type(self.problem, j)
if glpk_row_type == GLP_FX:
row_lb = glp_get_row_lb(self.problem, j)
row_ub = row_lb
elif glpk_row_type == GLP_LO:
row_lb = glp_get_row_lb(self.problem, j)
row_ub = None
elif glpk_row_type == GLP_UP:
row_lb = None
row_ub = glp_get_row_ub(self.problem, j)
elif glpk_row_type == GLP_DB:
row_lb = glp_get_row_lb(self.problem, j)
row_ub = glp_get_row_ub(self.problem, j)
elif glpk_row_type == GLP_FR:
row_lb = None
row_ub = None
else:
raise Exception(
"Currently, optlang does not support glpk row type %s"
% str(glpk_row_type)
)
log.exception()
if isinstance(lhs, int):
lhs = symbolics.Integer(lhs)
elif isinstance(lhs, float):
lhs = symbolics.Real(lhs)
constraint_id = glp_get_row_name(self.problem, j)
for variable in constraint_variables:
try:
self._variables_to_constraints_mapping[variable.name].add(constraint_id)
except KeyError:
self._variables_to_constraints_mapping[variable.name] = set([constraint_id])
super(Model, self)._add_constraints(
[Constraint(lhs, lb=row_lb, ub=row_ub, name=constraint_id, problem=self, sloppy=True)],
sloppy=True
)
term_generator = (
(glp_get_obj_coef(self.problem, index), variables[index - 1])
for index in range(1, glp_get_num_cols(problem) + 1)
)
self._objective = Objective(
symbolics.add(
[symbolics.mul((symbolics.Real(term[0]), term[1])) for term in term_generator if
term[0] != 0.]
),
problem=self,
direction={GLP_MIN: 'min', GLP_MAX: 'max'}[glp_get_obj_dir(self.problem)])
glp_scale_prob(self.problem, GLP_SF_AUTO)
def term_generator():
for index in range(1, glp_get_num_cols(self.problem.problem) + 1):
coeff = glp_get_obj_coef(self.problem.problem, index)
if coeff != 0.:
yield (symbolics.Real(coeff), variables[index - 1])
def test_set_linear_coefficients_objective(self):
self.model.objective.set_linear_coefficients(
{self.model.variables.R_TPI: 666.})
self.assertEqual(
glp_get_obj_coef(self.model.problem,
self.model.variables.R_TPI._index), 666.)
def test_set_linear_coefficients_objective(self):
self.model.objective.set_linear_coefficients({self.model.variables.R_TPI: 666.})
self.assertEqual(glp_get_obj_coef(self.model.problem, self.model.variables.R_TPI._index), 666.)
def __init__(self, problem=None, *args, **kwargs):
super(Model, self).__init__(*args, **kwargs)
self.configuration = Configuration()
if problem is None:
self.problem = glp_create_prob()
glp_create_index(self.problem)
if self.name is not None:
glp_set_prob_name(self.problem, str(self.name))
else:
try:
self.problem = problem
glp_create_index(self.problem)
except TypeError:
raise TypeError("Provided problem is not a valid GLPK model.")
row_num = glp_get_num_rows(self.problem)
col_num = glp_get_num_cols(self.problem)
for i in range(1, col_num + 1):
var = Variable(
glp_get_col_name(self.problem, i),
lb=glp_get_col_lb(self.problem, i),
ub=glp_get_col_ub(self.problem, i),
problem=self,
type=_GLPK_VTYPE_TO_VTYPE[
glp_get_col_kind(self.problem, i)]
)
# This avoids adding the variable to the glpk problem
super(Model, self)._add_variables([var])
variables = self.variables
for j in range(1, row_num + 1):
ia = intArray(col_num + 1)
da = doubleArray(col_num + 1)
nnz = glp_get_mat_row(self.problem, j, ia, da)
constraint_variables = [variables[ia[i] - 1] for i in range(1, nnz + 1)]
# Since constraint expressions are lazily retrieved from the solver they don't have to be built here
# lhs = _unevaluated_Add(*[da[i] * constraint_variables[i - 1]
# for i in range(1, nnz + 1)])
lhs = 0
glpk_row_type = glp_get_row_type(self.problem, j)
if glpk_row_type == GLP_FX:
row_lb = glp_get_row_lb(self.problem, j)
row_ub = row_lb
elif glpk_row_type == GLP_LO:
row_lb = glp_get_row_lb(self.problem, j)
row_ub = None
elif glpk_row_type == GLP_UP:
row_lb = None
row_ub = glp_get_row_ub(self.problem, j)
elif glpk_row_type == GLP_DB:
row_lb = glp_get_row_lb(self.problem, j)
row_ub = glp_get_row_ub(self.problem, j)
elif glpk_row_type == GLP_FR:
row_lb = None
row_ub = None
else:
raise Exception(
"Currently, optlang does not support glpk row type %s"
% str(glpk_row_type)
)
log.exception()
if isinstance(lhs, int):
lhs = sympy.Integer(lhs)
elif isinstance(lhs, float):
lhs = sympy.RealNumber(lhs)
constraint_id = glp_get_row_name(self.problem, j)
for variable in constraint_variables:
try:
self._variables_to_constraints_mapping[variable.name].add(constraint_id)
except KeyError:
self._variables_to_constraints_mapping[variable.name] = set([constraint_id])
super(Model, self)._add_constraints(
[Constraint(lhs, lb=row_lb, ub=row_ub, name=constraint_id, problem=self, sloppy=True)],
sloppy=True
)
term_generator = (
(glp_get_obj_coef(self.problem, index), variables[index - 1])
for index in range(1, glp_get_num_cols(problem) + 1)
)
self._objective = Objective(
_unevaluated_Add(
*[_unevaluated_Mul(sympy.RealNumber(term[0]), term[1]) for term in term_generator if
term[0] != 0.]),
problem=self,
direction={GLP_MIN: 'min', GLP_MAX: 'max'}[glp_get_obj_dir(self.problem)])
glp_scale_prob(self.problem, GLP_SF_AUTO)
def term_generator():
for index in range(1, glp_get_num_cols(self.problem.problem) + 1):
coeff = glp_get_obj_coef(self.problem.problem, index)
if coeff != 0.:
yield (sympy.RealNumber(coeff), variables[index - 1])
def get_linear_coefficients(self, variables):
if self.problem is not None:
self.problem.update()
return {var: glp_get_obj_coef(self.problem.problem, var._index) for var in variables}
else:
raise Exception("Can't get coefficients from solver if objective is not in a model")
