def test_init_from_existing_problem(self):
inner_prob = self.model.problem
self.assertEqual(len(self.model.variables), glp_get_num_cols(inner_prob))
self.assertEqual(len(self.model.constraints), glp_get_num_rows(inner_prob))
self.assertEqual(self.model.variables.keys(),
[glp_get_col_name(inner_prob, i) for i in range(1, glp_get_num_cols(inner_prob) + 1)])
self.assertEqual(self.model.constraints.keys(),
[glp_get_row_name(inner_prob, j) for j in range(1, glp_get_num_rows(inner_prob) + 1)])
def solve_with_glpsol(glp_prob):
"""Solve glpk problem with glpsol commandline solver. Mainly for testing purposes.
# Examples
# --------
# >>> problem = glp_create_prob()
# ... glp_read_lp(problem, None, "../tests/data/model.lp")
# ... solution = solve_with_glpsol(problem)
# ... print 'asdf'
# 'asdf'
# >>> print solution
# 0.839784
# Returns
# -------
# dict
# A dictionary containing the objective value (key ='objval')
# and variable primals.
"""
from swiglpk import glp_get_row_name, glp_get_col_name, glp_write_lp, glp_get_num_rows, glp_get_num_cols
row_ids = [glp_get_row_name(glp_prob, i) for i in range(1, glp_get_num_rows(glp_prob) + 1)]
col_ids = [glp_get_col_name(glp_prob, i) for i in range(1, glp_get_num_cols(glp_prob) + 1)]
with tempfile.NamedTemporaryFile(suffix=".lp", delete=True) as tmp_file:
tmp_file_name = tmp_file.name
glp_write_lp(glp_prob, None, tmp_file_name)
cmd = ['glpsol', '--lp', tmp_file_name, '-w', tmp_file_name + '.sol', '--log', '/dev/null']
term = check_output(cmd)
log.info(term)
try:
with open(tmp_file_name + '.sol') as sol_handle:
# print sol_handle.read()
solution = dict()
for i, line in enumerate(sol_handle.readlines()):
if i <= 1 or line == '\n':
pass
elif i <= len(row_ids):
solution[row_ids[i - 2]] = line.strip().split(' ')
elif i <= len(row_ids) + len(col_ids) + 1:
solution[col_ids[i - 2 - len(row_ids)]] = line.strip().split(' ')
else:
print(i)
print(line)
raise Exception("Argggh!")
finally:
os.remove(tmp_file_name + ".sol")
return solution
def solve_with_glpsol(glp_prob):
"""Solve glpk problem with glpsol commandline solver. Mainly for testing purposes.
# Examples
# --------
# >>> problem = glp_create_prob()
# ... glp_read_lp(problem, None, "../tests/data/model.lp")
# ... solution = solve_with_glpsol(problem)
# ... print 'asdf'
# 'asdf'
# >>> print solution
# 0.839784
# Returns
# -------
# dict
# A dictionary containing the objective value (key ='objval')
# and variable primals.
"""
from swiglpk import glp_get_row_name, glp_get_col_name, glp_write_lp, glp_get_num_rows, glp_get_num_cols
row_ids = [glp_get_row_name(glp_prob, i) for i in range(1, glp_get_num_rows(glp_prob) + 1)]
col_ids = [glp_get_col_name(glp_prob, i) for i in range(1, glp_get_num_cols(glp_prob) + 1)]
with tempfile.NamedTemporaryFile(suffix=".lp", delete=True) as tmp_file:
tmp_file_name = tmp_file.name
glp_write_lp(glp_prob, None, tmp_file_name)
cmd = ['glpsol', '--lp', tmp_file_name, '-w', tmp_file_name + '.sol', '--log', '/dev/null']
term = check_output(cmd)
log.info(term)
try:
with open(tmp_file_name + '.sol') as sol_handle:
# print sol_handle.read()
solution = dict()
for i, line in enumerate(sol_handle.readlines()):
if i <= 1 or line == '\n':
pass
elif i <= len(row_ids):
solution[row_ids[i - 2]] = line.strip().split(' ')
elif i <= len(row_ids) + len(col_ids) + 1:
solution[col_ids[i - 2 - len(row_ids)]] = line.strip().split(' ')
else:
print(i)
print(line)
raise Exception("Argggh!")
finally:
os.remove(tmp_file_name + ".sol")
return solution
def test_init_from_existing_problem(self):
inner_prob = self.model.problem
self.assertEqual(len(self.model.variables),
glp_get_num_cols(inner_prob))
self.assertEqual(len(self.model.constraints),
glp_get_num_rows(inner_prob))
self.assertEqual(self.model.variables.keys(), [
glp_get_col_name(inner_prob, i)
for i in range(1,
glp_get_num_cols(inner_prob) + 1)
])
self.assertEqual(self.model.constraints.keys(), [
glp_get_row_name(inner_prob, j)
for j in range(1,
glp_get_num_rows(inner_prob) + 1)
])
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 get_rates(problem: SwigPyObject) -> Iterable[Tuple[str, float]]:
for i in range(1, 1 + lp.glp_get_num_rows(problem)):
yield (
lp.glp_get_row_name(problem, i),
lp.glp_mip_row_val(problem, i) / 100,
)
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)
