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 test_change_constraint_bounds(self):
inner_prob = self.model.problem
inner_problem_bounds = [(glp_get_row_lb(inner_prob, i), glp_get_row_ub(inner_prob, i)) for i in
range(1, glp_get_num_rows(inner_prob) + 1)]
bounds = [(constr.lb, constr.ub) for constr in self.model.constraints]
self.assertEqual(bounds, inner_problem_bounds)
for constr in self.model.constraints:
constr.lb = random.uniform(-1000, constr.ub)
constr.ub = random.uniform(constr.lb, 1000)
inner_problem_bounds_new = [(glp_get_row_lb(inner_prob, i), glp_get_row_ub(inner_prob, i)) for i in
range(1, glp_get_num_rows(inner_prob) + 1)]
bounds_new = [(constr.lb, constr.ub) for constr in self.model.constraints]
self.assertNotEqual(bounds, bounds_new)
self.assertNotEqual(inner_problem_bounds, inner_problem_bounds_new)
self.assertEqual(bounds_new, inner_problem_bounds_new)
def add_rows_with_types(self, types, rhs_vec):
'''add rows to the LP with the given types
types is a vector of types: swiglpk.GLP_FX, swiglpk.GLP_UP, or swiglpk.GLP_LO
rhs_vector is the right-hand-side values of the constriants
'''
assert len(types) == len(rhs_vec)
if isinstance(rhs_vec, list):
rhs_vec = np.array(rhs_vec, dtype=float)
assert isinstance(rhs_vec, np.ndarray) and len(rhs_vec.shape) == 1, "expected 1-d right-hand-side vector"
if rhs_vec.shape[0] > 0:
num_rows = glpk.glp_get_num_rows(self.lp)
# create new row for each constraint
glpk.glp_add_rows(self.lp, len(rhs_vec))
for i, pair in enumerate(zip(rhs_vec, types)):
rhs, ty = pair
if ty == glpk.GLP_UP:
glpk.glp_set_row_bnds(self.lp, num_rows + i + 1, glpk.GLP_UP, 0, rhs) # '<=' constraint
elif ty == glpk.GLP_LO:
glpk.glp_set_row_bnds(self.lp, num_rows + i + 1, glpk.GLP_LO, rhs, 0) # '>=' constraint
else:
assert ty == glpk.GLP_FX
glpk.glp_set_row_bnds(self.lp, num_rows + i + 1, glpk.GLP_FX, rhs, rhs) # '>=' constraint
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 get_rhs(self, row_indices=None):
'''get the rhs vector of the constraints
row_indices - a list of requested indices (None=all)
this returns an np.array of rhs values for the requested indices
'''
rv = []
if row_indices is None:
lp_rows = glpk.glp_get_num_rows(self.lp)
row_indices = range(lp_rows)
for row in row_indices:
row_type = glpk.glp_get_row_type(self.lp, row + 1)
if row_type in [glpk.GLP_FX, glpk.GLP_UP]:
limit = glpk.glp_get_row_ub(self.lp, row + 1)
elif row_type == glpk.GLP_LO:
limit = glpk.glp_get_row_ub(self.lp, row + 1)
else:
raise RuntimeError(
"Error: Unsupported type ({}) in getRhs() in row {}".
format(row_type, row))
rv.append(limit)
return np.array(rv, dtype=float)
def get_types(self):
'''get the constraint types. These are swiglpk.GLP_FX, swiglpk.GLP_UP, or swiglpk.GLP_LO'''
lp_rows = glpk.glp_get_num_rows(self.lp)
rv = []
for row in range(lp_rows):
rv.append(glpk.glp_get_row_type(self.lp, row + 1))
return rv
def add_rows_equal_zero(self, num):
'''add rows to the LP with == 0 constraints'''
if num > 0:
num_rows = glpk.glp_get_num_rows(self.lp)
# create new row for each constraint
glpk.glp_add_rows(self.lp, num)
for i in range(num):
glpk.glp_set_row_bnds(self.lp, num_rows + i + 1, glpk.GLP_FX, 0, 0) # '== 0' constraints
def test_change_constraint_bounds(self):
inner_prob = self.model.problem
inner_problem_bounds = [
(glp_get_row_lb(inner_prob, i), glp_get_row_ub(inner_prob, i))
for i in range(1,
glp_get_num_rows(inner_prob) + 1)
]
bounds = [(constr.lb, constr.ub) for constr in self.model.constraints]
self.assertEqual(bounds, inner_problem_bounds)
for constr in self.model.constraints:
constr.lb = random.uniform(-1000, constr.ub)
constr.ub = random.uniform(constr.lb, 1000)
inner_problem_bounds_new = [
(glp_get_row_lb(inner_prob, i), glp_get_row_ub(inner_prob, i))
for i in range(1,
glp_get_num_rows(inner_prob) + 1)
]
bounds_new = [(constr.lb, constr.ub)
for constr in self.model.constraints]
self.assertNotEqual(bounds, bounds_new)
self.assertNotEqual(inner_problem_bounds, inner_problem_bounds_new)
self.assertEqual(bounds_new, inner_problem_bounds_new)
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 add_rows_equal(self, rhs_vec):
'''add rows to the LP with == rhs[i] constraints'''
num = len(rhs_vec)
if num > 0:
num_rows = glpk.glp_get_num_rows(self.lp)
# create new row for each constraint
glpk.glp_add_rows(self.lp, num)
for i, val in enumerate(rhs_vec):
glpk.glp_set_row_bnds(self.lp, num_rows + i + 1, glpk.GLP_FX,
val, val) # '== val' constraints
def _add_constraints(self, constraints, sloppy=False):
super(Model, self)._add_constraints(constraints, sloppy=sloppy)
for constraint in constraints:
constraint._problem = None # This needs to be dones in order to not trigger constraint._get_expression()
glp_add_rows(self.problem, 1)
index = glp_get_num_rows(self.problem)
glp_set_row_name(self.problem, index, str(constraint.name))
num_cols = glp_get_num_cols(self.problem)
index_array = intArray(num_cols + 1)
value_array = doubleArray(num_cols + 1)
num_vars = 0 # constraint.variables is too expensive for large problems
if constraint.expression.is_Atom and constraint.expression.is_Symbol:
var = constraint.expression
index_array[1] = var.index
value_array[1] = 1
num_vars += 1
elif constraint.expression.is_Mul:
args = constraint.expression.args
if len(args) > 2:
raise Exception(
"Term(s) %s from constraint %s is not a proper linear term." % (args, constraint))
coeff = float(args[0])
var = args[1]
index_array[1] = var.index
value_array[1] = coeff
num_vars += 1
else:
for i, term in enumerate(constraint.expression.args):
args = term.args
if args == ():
assert term.is_Symbol
coeff = 1
var = term
elif len(args) == 2:
assert args[0].is_Number
assert args[1].is_Symbol
var = args[1]
coeff = float(args[0])
elif len(args) > 2:
raise Exception(
"Term %s from constraint %s is not a proper linear term." % (term, constraint))
index_array[i + 1] = var.index
value_array[i + 1] = coeff
num_vars += 1
glp_set_mat_row(self.problem, index, num_vars,
index_array, value_array)
constraint._problem = self
self._glpk_set_row_bounds(constraint)
def add_rows_less_equal(self, rhs_vec):
'''add rows to the LP with <= constraints
rhs_vector is the right-hand-side values of the constriants
'''
if isinstance(rhs_vec, list):
rhs_vec = np.array(rhs_vec, dtype=float)
assert isinstance(rhs_vec, np.ndarray) and len(rhs_vec.shape) == 1, "expected 1-d right-hand-side vector"
if rhs_vec.shape[0] > 0:
num_rows = glpk.glp_get_num_rows(self.lp)
# create new row for each constraint
glpk.glp_add_rows(self.lp, len(rhs_vec))
for i, rhs in enumerate(rhs_vec):
glpk.glp_set_row_bnds(self.lp, num_rows + i + 1, glpk.GLP_UP, 0, rhs) # '<=' constraint
def set_constraint_rhs(self, row_index, rhs):
'''change an existing constraint's right hand side'''
rows = glpk.glp_get_num_rows(self.lp)
assert 0 <= row_index < rows, "Invalid row ({}) in set_constraint_rhs() (lp has {})".format(
row_index, rows)
row_type = glpk.glp_get_row_type(self.lp, row_index + 1)
if row_type == glpk.GLP_UP:
glpk.glp_set_row_bnds(self.lp, row_index + 1, glpk.GLP_UP, 0, rhs)
elif row_type == glpk.GLP_LO:
glpk.glp_set_row_bnds(self.lp, row_index + 1, glpk.GLP_LO, rhs, 0)
elif row_type == glpk.GLP_FX:
glpk.glp_set_row_bnds(self.lp, row_index + 1, glpk.GLP_FX, rhs, rhs)
else:
raise RuntimeError("Invalid constraint type {} in row {} in set_constraint_rhs()".format(
row_type, row_index))
def _add_constraints(self, constraints, sloppy=False):
super(Model, self)._add_constraints(constraints, sloppy=sloppy)
for constraint in constraints:
constraint._problem = None # This needs to be done in order to not trigger constraint._get_expression()
glp_add_rows(self.problem, 1)
index = glp_get_num_rows(self.problem)
glp_set_row_name(self.problem, index, str(constraint.name))
num_cols = glp_get_num_cols(self.problem)
index_array = intArray(num_cols + 1)
value_array = doubleArray(num_cols + 1)
num_vars = 0 # constraint.variables is too expensive for large problems
coef_dict, _ = parse_optimization_expression(constraint, linear=True)
num_vars = len(coef_dict)
for i, (var, coef) in enumerate(coef_dict.items()):
index_array[i + 1] = var._index
value_array[i + 1] = float(coef)
glp_set_mat_row(self.problem, index, num_vars,
index_array, value_array)
constraint._problem = self
self._glpk_set_row_bounds(constraint)
def _add_constraints(self, constraints, sloppy=False):
super(Model, self)._add_constraints(constraints, sloppy=sloppy)
for constraint in constraints:
constraint._problem = None # This needs to be done in order to not trigger constraint._get_expression()
glp_add_rows(self.problem, 1)
index = glp_get_num_rows(self.problem)
glp_set_row_name(self.problem, index, str(constraint.name))
num_cols = glp_get_num_cols(self.problem)
index_array = intArray(num_cols + 1)
value_array = doubleArray(num_cols + 1)
num_vars = 0 # constraint.variables is too expensive for large problems
offset, coef_dict, _ = parse_optimization_expression(constraint, linear=True)
num_vars = len(coef_dict)
for i, (var, coef) in enumerate(coef_dict.items()):
index_array[i + 1] = var._index
value_array[i + 1] = float(coef)
glp_set_mat_row(self.problem, index, num_vars,
index_array, value_array)
constraint._problem = self
self._glpk_set_row_bounds(constraint)
def get_num_rows(self):
'get the number of rows in the lp'
return glpk.glp_get_num_rows(self.lp)
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 _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 test_glpk_read_cplex():
problem = glpk_read_cplex(TESTMODELPATH)
nose.tools.assert_equal(glp_get_num_rows(problem), 72)
nose.tools.assert_equal(glp_get_num_cols(problem), 95)
def test_glpk_read_cplex():
problem = glpk_read_cplex(TESTMODELPATH)
nose.tools.assert_equal(glp_get_num_rows(problem), 72)
nose.tools.assert_equal(glp_get_num_cols(problem), 95)
Example
----------
>>> with TemporaryFilename() as tmp_file_name:
>>> with open(tmp_file_name, "w") as tmp_file:
>>> tmp_file.write(stuff)
>>> with open(tmp_file) as tmp_file:
>>> stuff = tmp_file.read()
"""
def __init__(self, suffix="tmp", content=None):
tmp_file = tempfile.NamedTemporaryFile(suffix=suffix, delete=False, mode="w")
if content is not None:
tmp_file.write(content)
self.name = tmp_file.name
tmp_file.close()
def __enter__(self):
return self.name
def __exit__(self, type, value, traceback):
os.remove(self.name)
if __name__ == '__main__':
from swiglpk import glp_create_prob, glp_read_lp, glp_get_num_rows
problem = glp_create_prob()
glp_read_lp(problem, None, "../tests/data/model.lp")
print("asdf", glp_get_num_rows(problem))
solution = solve_with_glpsol(problem)
print(solution['R_Biomass_Ecoli_core_w_GAM'])
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 test_add_constraints(self):
x = Variable('x', lb=0, ub=1, type='binary')
y = Variable('y', lb=-181133.3, ub=12000., type='continuous')
z = Variable('z', lb=0., ub=10., type='integer')
constr1 = Constraint(0.3 * x + 0.4 * y + 66. * z,
lb=-100,
ub=0.,
name='test')
constr2 = Constraint(2.333 * x + y + 3.333, ub=100.33, name='test2')
constr3 = Constraint(2.333 * x + y + z, lb=-300)
constr4 = Constraint(x, lb=-300, ub=-300)
constr5 = Constraint(3 * x)
self.model.add(constr1)
self.model.add(constr2)
self.model.add(constr3)
self.model.add([constr4, constr5])
self.assertIn(constr1.name, self.model.constraints)
self.assertIn(constr2.name, self.model.constraints)
self.assertIn(constr3.name, self.model.constraints)
self.assertIn(constr4.name, self.model.constraints)
self.assertIn(constr5.name, self.model.constraints)
# constr1
ia = intArray(glp_get_num_rows(self.model.problem) + 1)
da = doubleArray(glp_get_num_rows(self.model.problem) + 1)
nnz = glp_get_mat_row(self.model.problem, constr1._index, ia, da)
coeff_dict = dict()
for i in range(1, nnz + 1):
coeff_dict[glp_get_col_name(self.model.problem, ia[i])] = da[i]
self.assertDictEqual(coeff_dict, {'x': 0.3, 'y': 0.4, 'z': 66.})
self.assertEqual(glp_get_row_type(self.model.problem, constr1._index),
GLP_DB)
self.assertEqual(glp_get_row_lb(self.model.problem, constr1._index),
-100)
self.assertEqual(glp_get_row_ub(self.model.problem, constr1._index), 0)
# constr2
ia = intArray(glp_get_num_rows(self.model.problem) + 1)
da = doubleArray(glp_get_num_rows(self.model.problem) + 1)
nnz = glp_get_mat_row(self.model.problem, constr2._index, ia, da)
coeff_dict = dict()
for i in range(1, nnz + 1):
coeff_dict[glp_get_col_name(self.model.problem, ia[i])] = da[i]
self.assertDictEqual(coeff_dict, {'x': 2.333, 'y': 1.})
self.assertEqual(glp_get_row_type(self.model.problem, constr2._index),
GLP_UP)
self.assertEqual(glp_get_row_lb(self.model.problem, constr2._index),
-1.7976931348623157e+308)
self.assertEqual(glp_get_row_ub(self.model.problem, constr2._index),
96.997)
# constr3
ia = intArray(glp_get_num_rows(self.model.problem) + 1)
da = doubleArray(glp_get_num_rows(self.model.problem) + 1)
nnz = glp_get_mat_row(self.model.problem, constr3._index, ia, da)
coeff_dict = dict()
for i in range(1, nnz + 1):
coeff_dict[glp_get_col_name(self.model.problem, ia[i])] = da[i]
self.assertDictEqual(coeff_dict, {'x': 2.333, 'y': 1., 'z': 1.})
self.assertEqual(glp_get_row_type(self.model.problem, constr3._index),
GLP_LO)
self.assertEqual(glp_get_row_lb(self.model.problem, constr3._index),
-300)
self.assertEqual(glp_get_row_ub(self.model.problem, constr3._index),
1.7976931348623157e+308)
# constr4
ia = intArray(glp_get_num_rows(self.model.problem) + 1)
da = doubleArray(glp_get_num_rows(self.model.problem) + 1)
nnz = glp_get_mat_row(self.model.problem, constr4._index, ia, da)
coeff_dict = dict()
for i in range(1, nnz + 1):
coeff_dict[glp_get_col_name(self.model.problem, ia[i])] = da[i]
self.assertDictEqual(coeff_dict, {'x': 1})
self.assertEqual(glp_get_row_type(self.model.problem, constr4._index),
GLP_FX)
self.assertEqual(glp_get_row_lb(self.model.problem, constr4._index),
-300)
self.assertEqual(glp_get_row_ub(self.model.problem, constr4._index),
-300)
# constr5
ia = intArray(glp_get_num_rows(self.model.problem) + 1)
da = doubleArray(glp_get_num_rows(self.model.problem) + 1)
nnz = glp_get_mat_row(self.model.problem, constr5._index, ia, da)
coeff_dict = dict()
for i in range(1, nnz + 1):
coeff_dict[glp_get_col_name(self.model.problem, ia[i])] = da[i]
self.assertDictEqual(coeff_dict, {'x': 3})
self.assertEqual(glp_get_row_type(self.model.problem, constr5._index),
GLP_FR)
self.assertLess(glp_get_row_lb(self.model.problem, constr5._index),
-1e30)
self.assertGreater(glp_get_row_ub(self.model.problem, constr5._index),
1e30)
def test_add_constraints(self):
x = self.interface.Variable('x', lb=0, ub=1, type='binary')
y = self.interface.Variable('y', lb=-181133.3, ub=12000., type='continuous')
z = self.interface.Variable('z', lb=0., ub=10., type='integer')
constr1 = self.interface.Constraint(0.3 * x + 0.4 * y + 66. * z, lb=-100, ub=0., name='test')
constr2 = self.interface.Constraint(2.333 * x + y + 3.333, ub=100.33, name='test2')
constr3 = self.interface.Constraint(2.333 * x + y + z, lb=-300)
constr4 = self.interface.Constraint(x, lb=-300, ub=-300)
constr5 = self.interface.Constraint(3 * x)
self.model.add(constr1)
self.model.add(constr2)
self.model.add(constr3)
self.model.add([constr4, constr5])
self.assertIn(constr1.name, self.model.constraints)
self.assertIn(constr2.name, self.model.constraints)
self.assertIn(constr3.name, self.model.constraints)
self.assertIn(constr4.name, self.model.constraints)
self.assertIn(constr5.name, self.model.constraints)
# constr1
ia = intArray(glp_get_num_rows(self.model.problem) + 1)
da = doubleArray(glp_get_num_rows(self.model.problem) + 1)
nnz = glp_get_mat_row(self.model.problem, constr1._index, ia, da)
coeff_dict = dict()
for i in range(1, nnz + 1):
coeff_dict[glp_get_col_name(self.model.problem, ia[i])] = da[i]
self.assertDictEqual(coeff_dict, {'x': 0.3, 'y': 0.4, 'z': 66.})
self.assertEqual(glp_get_row_type(self.model.problem, constr1._index), GLP_DB)
self.assertEqual(glp_get_row_lb(self.model.problem, constr1._index), -100)
self.assertEqual(glp_get_row_ub(self.model.problem, constr1._index), 0)
# constr2
ia = intArray(glp_get_num_rows(self.model.problem) + 1)
da = doubleArray(glp_get_num_rows(self.model.problem) + 1)
nnz = glp_get_mat_row(self.model.problem, constr2._index, ia, da)
coeff_dict = dict()
for i in range(1, nnz + 1):
coeff_dict[glp_get_col_name(self.model.problem, ia[i])] = da[i]
self.assertDictEqual(coeff_dict, {'x': 2.333, 'y': 1.})
self.assertEqual(glp_get_row_type(self.model.problem, constr2._index), GLP_UP)
self.assertEqual(glp_get_row_lb(self.model.problem, constr2._index), -1.7976931348623157e+308)
self.assertEqual(glp_get_row_ub(self.model.problem, constr2._index), 96.997)
# constr3
ia = intArray(glp_get_num_rows(self.model.problem) + 1)
da = doubleArray(glp_get_num_rows(self.model.problem) + 1)
nnz = glp_get_mat_row(self.model.problem, constr3._index, ia, da)
coeff_dict = dict()
for i in range(1, nnz + 1):
coeff_dict[glp_get_col_name(self.model.problem, ia[i])] = da[i]
self.assertDictEqual(coeff_dict, {'x': 2.333, 'y': 1., 'z': 1.})
self.assertEqual(glp_get_row_type(self.model.problem, constr3._index), GLP_LO)
self.assertEqual(glp_get_row_lb(self.model.problem, constr3._index), -300)
self.assertEqual(glp_get_row_ub(self.model.problem, constr3._index), 1.7976931348623157e+308)
# constr4
ia = intArray(glp_get_num_rows(self.model.problem) + 1)
da = doubleArray(glp_get_num_rows(self.model.problem) + 1)
nnz = glp_get_mat_row(self.model.problem, constr4._index, ia, da)
coeff_dict = dict()
for i in range(1, nnz + 1):
coeff_dict[glp_get_col_name(self.model.problem, ia[i])] = da[i]
self.assertDictEqual(coeff_dict, {'x': 1})
self.assertEqual(glp_get_row_type(self.model.problem, constr4._index), GLP_FX)
self.assertEqual(glp_get_row_lb(self.model.problem, constr4._index), -300)
self.assertEqual(glp_get_row_ub(self.model.problem, constr4._index), -300)
# constr5
ia = intArray(glp_get_num_rows(self.model.problem) + 1)
da = doubleArray(glp_get_num_rows(self.model.problem) + 1)
nnz = glp_get_mat_row(self.model.problem, constr5._index, ia, da)
coeff_dict = dict()
for i in range(1, nnz + 1):
coeff_dict[glp_get_col_name(self.model.problem, ia[i])] = da[i]
self.assertDictEqual(coeff_dict, {'x': 3})
self.assertEqual(glp_get_row_type(self.model.problem, constr5._index), GLP_FR)
self.assertLess(glp_get_row_lb(self.model.problem, constr5._index), -1e30)
self.assertGreater(glp_get_row_ub(self.model.problem, constr5._index), 1e30)
def __getattr__(self, item):
try:
return self._functions[item][0]()
except KeyError:
raise AttributeError(
item +
" is not an available tolerance parameter with this solver")
def __setattr__(self, key, value):
if key not in self._functions:
raise AttributeError(
key +
" is not an available tolerance parameter with this solver")
self._functions[key][1](value)
def to_dict(self):
return {key: getattr(self, key) for key in self._functions.keys()}
def __dir__(self):
return list(self._functions)
if __name__ == '__main__':
from swiglpk import glp_create_prob, glp_read_lp, glp_get_num_rows
problem = glp_create_prob()
glp_read_lp(problem, None, "../tests/data/model.lp")
print("asdf", glp_get_num_rows(problem))
solution = solve_with_glpsol(problem)
print(solution['R_Biomass_Ecoli_core_w_GAM'])