def test_construct_with_sloppy(self):
x, y, z, w = self.model.variables[:4]
obj = self.interface.Objective(
symbolics.add([symbolics.mul((symbolics.One, var)) for var in [x, y, z]]),
direction="min",
sloppy=True
)
self.model.objective = obj
self.assertTrue(obj.get_linear_coefficients([x, y, z, w]) == {x: 1, y: 1, z: 1, w: 0})
def test_construct_with_sloppy(self):
x, y, z, w = self.model.variables[:4]
obj = self.interface.Objective(
symbolics.add([symbolics.mul((symbolics.One, var)) for var in [x, y, z]]),
direction="min",
sloppy=True
)
self.model.objective = obj
self.assertTrue(obj.get_linear_coefficients([x, y, z, w]) == {x: 1, y: 1, z: 1, w: 0})
def _get_expression(self):
if self.problem is not None:
variables = self.problem._variables
all_coefs = self.problem.problem.constraint_coefs
coefs = [(v, all_coefs.get((self.name, v.name), 0.0))
for v in variables]
expression = add(
[mul((symbolics.Real(co), v)) for (v, co) in coefs])
self._expression = expression
return self._expression
def test_construct_with_sloppy(self):
x, y, z, w = self.model.variables[:4]
const = self.interface.Constraint(
symbolics.add([symbolics.mul(symbolics.One, var) for var in [x, y, z]]),
lb=0,
sloppy=True
)
self.model.add(const)
self.model.update()
self.assertTrue(const.get_linear_coefficients([x, y, z, w]) == {x: 1, y: 1, z: 1, w: 0})
def _get_expression(self):
if self.problem is not None:
cplex_problem = self.problem.problem
cplex_row = cplex_problem.linear_constraints.get_rows(self.name)
variables = self.problem._variables
expression = add([
mul((symbolics.Real(cplex_row.val[i]), variables[ind]))
for i, ind in enumerate(cplex_row.ind)
])
self._expression = expression
return self._expression
def test_construct_with_sloppy(self):
x, y, z, w = self.model.variables[:4]
const = self.interface.Constraint(
symbolics.add([symbolics.mul(symbolics.One, var) for var in [x, y, z]]),
lb=0,
sloppy=True
)
self.model.add(const)
self.model.update()
self.assertTrue(const.get_linear_coefficients([x, y, z, w]) == {x: 1, y: 1, z: 1, w: 0})
def _get_expression(self):
if self.problem is not None:
col_num = glp_get_num_cols(self.problem.problem)
ia = intArray(col_num + 1)
da = doubleArray(col_num + 1)
nnz = glp_get_mat_row(self.problem.problem, self._index, ia, da)
constraint_variables = [self.problem._variables[glp_get_col_name(self.problem.problem, ia[i])] for i in
range(1, nnz + 1)]
expression = symbolics.add(
[symbolics.mul((symbolics.Real(da[i]), constraint_variables[i - 1])) for i in
range(1, nnz + 1)])
self._expression = expression
return self._expression
def _get_expression(self):
if self.problem is not None:
col_num = glp_get_num_cols(self.problem.problem)
ia = intArray(col_num + 1)
da = doubleArray(col_num + 1)
nnz = glp_get_mat_row(self.problem.problem, self._index, ia, da)
constraint_variables = [self.problem._variables[glp_get_col_name(self.problem.problem, ia[i])] for i in
range(1, nnz + 1)]
expression = symbolics.add(
[symbolics.mul((symbolics.Real(da[i]), constraint_variables[i - 1])) for i in
range(1, nnz + 1)])
self._expression = expression
return self._expression
def _get_expression(self):
if self.problem is not None and self._expression_expired:
variables = self.problem._variables
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])
expression = symbolics.add([symbolics.mul(term) for term in term_generator()])
self._expression = expression + getattr(self.problem, "_objective_offset", 0)
self._expression_expired = False
return self._expression
def _get_expression(self):
if self.problem is not None and self._expression_expired:
variables = self.problem._variables
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])
expression = symbolics.add([symbolics.mul(term) for term in term_generator()])
self._expression = expression + getattr(self.problem, "_objective_offset", 0)
self._expression_expired = False
return self._expression
def _get_expression(self):
if self.problem is not None:
gurobi_problem = self.problem.problem
gurobi_constraint = self._internal_constraint
row = gurobi_problem.getRow(gurobi_constraint)
terms = []
for i in range(row.size()):
internal_var_name = row.getVar(i).VarName
if internal_var_name == self.name + '_aux':
continue
variable = self.problem._variables[internal_var_name]
coeff = symbolics.Real(row.getCoeff(i))
terms.append(symbolics.mul((coeff, variable)))
self._expression = symbolics.add(terms)
return self._expression
def _get_expression(self):
if self.problem is not None:
gurobi_problem = self.problem.problem
gurobi_constraint = self._internal_constraint
row = gurobi_problem.getRow(gurobi_constraint)
terms = []
for i in range(row.size()):
internal_var_name = row.getVar(i).VarName
if internal_var_name == self.name + '_aux':
continue
variable = self.problem._variables[internal_var_name]
coeff = symbolics.Real(row.getCoeff(i))
terms.append(symbolics.mul((coeff, variable)))
self._expression = symbolics.add(terms)
return self._expression
def _get_expression(self):
if self.problem is not None:
cplex_problem = self.problem.problem
try:
cplex_row = cplex_problem.linear_constraints.get_rows(self.name)
except CplexSolverError as e:
if 'CPLEX Error 1219:' not in str(e):
raise e
else:
cplex_row = cplex_problem.indicator_constraints.get_linear_components(self.name)
variables = self.problem._variables
expression = add(
[mul((symbolics.Real(cplex_row.val[i]), variables[ind])) for i, ind in
enumerate(cplex_row.ind)])
self._expression = expression
return self._expression
def _get_expression(self):
if self.problem is not None:
cplex_problem = self.problem.problem
try:
cplex_row = cplex_problem.linear_constraints.get_rows(self.name)
except CplexSolverError as e:
if 'CPLEX Error 1219:' not in str(e):
raise e
else:
cplex_row = cplex_problem.indicator_constraints.get_linear_components(self.name)
variables = self.problem._variables
expression = add(
[mul((symbolics.Real(cplex_row.val[i]), variables[ind])) for i, ind in
enumerate(cplex_row.ind)])
self._expression = expression
return self._expression
def parse_expr(expr, local_dict=None):
"""
Parses a json-object created with 'expr_to_json' into a Sympy expression.
If a local_dict argument is passed, symbols with be looked up by name, and a new symbol will
be created only if the name is not in local_dict.
"""
if local_dict is None:
local_dict = {}
if expr["type"] == "Add":
return add([parse_expr(arg, local_dict) for arg in expr["args"]])
elif expr["type"] == "Mul":
return mul([parse_expr(arg, local_dict) for arg in expr["args"]])
elif expr["type"] == "Pow":
return Pow(parse_expr(arg, local_dict) for arg in expr["args"])
elif expr["type"] == "Symbol":
try:
return local_dict[expr["name"]]
except KeyError:
return symbolics.Symbol(expr["name"])
elif expr["type"] == "Number":
return symbolics.sympify(expr["value"])
else:
raise NotImplementedError(expr["type"] + " is not implemented")
def parse_expr(expr, local_dict=None):
"""
Parses a json-object created with 'expr_to_json' into a Sympy expression.
If a local_dict argument is passed, symbols with be looked up by name, and a new symbol will
be created only if the name is not in local_dict.
"""
if local_dict is None:
local_dict = {}
if expr["type"] == "Add":
return add([parse_expr(arg, local_dict) for arg in expr["args"]])
elif expr["type"] == "Mul":
return mul([parse_expr(arg, local_dict) for arg in expr["args"]])
elif expr["type"] == "Pow":
return Pow(parse_expr(arg, local_dict) for arg in expr["args"])
elif expr["type"] == "Symbol":
try:
return local_dict[expr["name"]]
except KeyError:
return symbolics.Symbol(expr["name"])
elif expr["type"] == "Number":
return symbolics.sympify(expr["value"])
else:
raise NotImplementedError(expr["type"] + " is not implemented")
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 __init__(self, problem=None, *args, **kwargs):
super(Model, self).__init__(*args, **kwargs)
if problem is None:
self.problem = cplex.Cplex()
elif isinstance(problem, cplex.Cplex):
self.problem = problem
zipped_var_args = zip(self.problem.variables.get_names(),
self.problem.variables.get_lower_bounds(),
self.problem.variables.get_upper_bounds(),
# self.problem.variables.get_types(), # TODO uncomment when cplex is fixed
)
for name, lb, ub in zipped_var_args:
var = Variable(name, lb=lb, ub=ub, problem=self) # Type should also be in there
super(Model, self)._add_variables([var]) # This avoids adding the variable to the glpk problem
zipped_constr_args = zip(self.problem.linear_constraints.get_names(),
self.problem.linear_constraints.get_rows(),
self.problem.linear_constraints.get_senses(),
self.problem.linear_constraints.get_rhs()
)
variables = self._variables
for name, row, sense, rhs in zipped_constr_args:
constraint_variables = [variables[i - 1] for i in row.ind]
# Since constraint expressions are lazily retrieved from the solver they don't have to be built here
# lhs = _unevaluated_Add(*[val * variables[i - 1] for i, val in zip(row.ind, row.val)])
lhs = symbolics.Integer(0)
if sense == 'E':
constr = Constraint(lhs, lb=rhs, ub=rhs, name=name, problem=self)
elif sense == 'G':
constr = Constraint(lhs, lb=rhs, name=name, problem=self)
elif sense == 'L':
constr = Constraint(lhs, ub=rhs, name=name, problem=self)
elif sense == 'R':
range_val = self.problem.linear_constraints.get_range_values(name)
if range_val > 0:
constr = Constraint(lhs, lb=rhs, ub=rhs + range_val, name=name, problem=self)
else:
constr = Constraint(lhs, lb=rhs + range_val, ub=rhs, name=name, problem=self)
else: # pragma: no cover
raise Exception('%s is not a recognized constraint sense.' % sense)
for variable in constraint_variables:
try:
self._variables_to_constraints_mapping[variable.name].add(name)
except KeyError:
self._variables_to_constraints_mapping[variable.name] = set([name])
super(Model, self)._add_constraints(
[constr],
sloppy=True
)
try:
objective_name = self.problem.objective.get_name()
except CplexSolverError as e:
if 'CPLEX Error 1219:' not in str(e):
raise e
else:
linear_expression = add(
[mul(symbolics.Real(coeff), variables[index]) for index, coeff in enumerate(self.problem.objective.get_linear()) if coeff != 0.]
)
try:
quadratic = self.problem.objective.get_quadratic()
except IndexError:
quadratic_expression = Zero
else:
quadratic_expression = self._get_quadratic_expression(quadratic)
self._objective = Objective(
linear_expression + quadratic_expression,
problem=self,
direction=
{self.problem.objective.sense.minimize: 'min', self.problem.objective.sense.maximize: 'max'}[
self.problem.objective.get_sense()],
name=objective_name
)
else:
raise TypeError("Provided problem is not a valid CPLEX model.")
self.configuration = Configuration(problem=self, verbosity=0)
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:
_glpk_validate_id(self.name)
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 = 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 __init__(self, problem=None, *args, **kwargs):
super(Model, self).__init__(*args, **kwargs)
if problem is None:
self.problem = cplex.Cplex()
elif isinstance(problem, cplex.Cplex):
self.problem = problem
zipped_var_args = zip(
self.problem.variables.get_names(),
self.problem.variables.get_lower_bounds(),
self.problem.variables.get_upper_bounds(),
# self.problem.variables.get_types(), # TODO uncomment when cplex is fixed
)
for name, lb, ub in zipped_var_args:
var = Variable(name, lb=lb, ub=ub,
problem=self) # Type should also be in there
super(Model, self)._add_variables([
var
]) # This avoids adding the variable to the glpk problem
zipped_constr_args = zip(
self.problem.linear_constraints.get_names(),
self.problem.linear_constraints.get_rows(),
self.problem.linear_constraints.get_senses(),
self.problem.linear_constraints.get_rhs())
variables = self._variables
for name, row, sense, rhs in zipped_constr_args:
constraint_variables = [variables[i - 1] for i in row.ind]
# Since constraint expressions are lazily retrieved from the solver they don't have to be built here
# lhs = _unevaluated_Add(*[val * variables[i - 1] for i, val in zip(row.ind, row.val)])
lhs = symbolics.Integer(0)
if sense == 'E':
constr = Constraint(lhs,
lb=rhs,
ub=rhs,
name=name,
problem=self)
elif sense == 'G':
constr = Constraint(lhs, lb=rhs, name=name, problem=self)
elif sense == 'L':
constr = Constraint(lhs, ub=rhs, name=name, problem=self)
elif sense == 'R':
range_val = self.problem.linear_constraints.get_range_values(
name)
if range_val > 0:
constr = Constraint(lhs,
lb=rhs,
ub=rhs + range_val,
name=name,
problem=self)
else:
constr = Constraint(lhs,
lb=rhs + range_val,
ub=rhs,
name=name,
problem=self)
else: # pragma: no cover
raise Exception(
'%s is not a recognized constraint sense.' % sense)
for variable in constraint_variables:
try:
self._variables_to_constraints_mapping[
variable.name].add(name)
except KeyError:
self._variables_to_constraints_mapping[
variable.name] = set([name])
super(Model, self)._add_constraints([constr], sloppy=True)
try:
objective_name = self.problem.objective.get_name()
except CplexSolverError as e:
if 'CPLEX Error 1219:' not in str(e):
raise e
else:
linear_expression = add([
mul(symbolics.Real(coeff), variables[index]) for index,
coeff in enumerate(self.problem.objective.get_linear())
if coeff != 0.
])
try:
quadratic = self.problem.objective.get_quadratic()
except IndexError:
quadratic_expression = Zero
else:
quadratic_expression = self._get_quadratic_expression(
quadratic)
self._objective = Objective(
linear_expression + quadratic_expression,
problem=self,
direction={
self.problem.objective.sense.minimize: 'min',
self.problem.objective.sense.maximize: 'max'
}[self.problem.objective.get_sense()],
name=objective_name)
else:
raise TypeError("Provided problem is not a valid CPLEX model.")
self.configuration = Configuration(problem=self, verbosity=0)