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Merge pull request #13 from biosustain/transactions
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Switch to transaction based model modifications
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@phantomas1234
phantomas1234 committed May 15, 2016
2 parents eb36df5 + 7f1035b commit a6ec0ab
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Showing 7 changed files with 306 additions and 214 deletions.
165 changes: 89 additions & 76 deletions optlang/cplex_interface.py
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Expand Up @@ -153,18 +153,6 @@ class Variable(interface.Variable):
def __init__(self, name, *args, **kwargs):
super(Variable, self).__init__(name, **kwargs)

@interface.Variable.lb.setter
def lb(self, value):
super(Variable, self.__class__).lb.fset(self, value)
if self.problem is not None:
self.problem.problem.variables.set_lower_bounds(self.name, value)

@interface.Variable.ub.setter
def ub(self, value):
super(Variable, self.__class__).ub.fset(self, value)
if self.problem is not None:
self.problem.problem.variables.set_upper_bounds(self.name, value)

@interface.Variable.type.setter
def type(self, value):
if self.problem is not None:
Expand Down Expand Up @@ -221,7 +209,7 @@ 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
variables = self.problem._variables
expression = sympy.Add._from_args([sympy.Mul._from_args((sympy.RealNumber(cplex_row.val[i]), variables[ind])) for i, ind in enumerate(cplex_row.ind)])
self._expression = expression
return self._expression
Expand Down Expand Up @@ -526,14 +514,14 @@ def __init__(self, problem=None, *args, **kwargs):
)
for name, lb, ub in zipped_var_args:
var = Variable(name, lb=lb, ub=ub, problem=self)
super(Model, self)._add_variable(var) # This avoids adding the variable to the glpk problem
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
variables = self._variables
for name, row, sense, rhs in zipped_constr_args:
constraint_variables = [variables[i - 1] for i in row.ind]
lhs = _unevaluated_Add(*[val * variables[i - 1] for i, val in zip(row.ind, row.val)])
Expand Down Expand Up @@ -562,8 +550,8 @@ def __init__(self, problem=None, *args, **kwargs):
except KeyError:
self._variables_to_constraints_mapping[variable.name] = set([name])

super(Model, self)._add_constraint(
constr,
super(Model, self)._add_constraints(
[constr],
sloppy=True
)
try:
Expand Down Expand Up @@ -620,6 +608,7 @@ def objective(self):

@objective.setter
def objective(self, value):
self.update()
if self._objective is not None:
for variable in self.objective.variables:
try:
Expand Down Expand Up @@ -738,73 +727,97 @@ def _cplex_sense_to_sympy(sense, translation=None):
except KeyError as e:
raise Exception(' '.join(('Sense', sense, 'is not a proper relational operator, e.g. >, <, == etc.')))

def _add_variable(self, variable):
super(Model, self)._add_variable(variable)
if variable.lb is None:
lb = -cplex.infinity
else:
lb = variable.lb
if variable.ub is None:
ub = cplex.infinity
else:
ub = variable.ub
vtype = _VTYPE_TO_CPLEX_VTYPE[variable.type]
if vtype == 'C': # this is needed because CPLEX will automatically set the problem_type to MILP if types are specified
self.problem.variables.add([0.], lb=[lb], ub=[ub], names=[variable.name])
def _set_variable_bounds_on_problem(self, var_lb, var_ub):
lb = [(variable.name, value) for variable, value in var_lb]
if len(lb) > 0:
self.problem.variables.set_lower_bounds(lb)
ub = [(variable.name, value) for variable, value in var_ub]
if len(ub) > 0:
self.problem.variables.set_upper_bounds(ub)


def _add_variables(self, variables):
super(Model, self)._add_variables(variables)
lb, ub, vtype, names, coeff = list(), list(), list(), list(), list()
for variable in variables:
if variable.lb is None:
lb.append(-cplex.infinity)
else:
lb.append(variable.lb)
if variable.ub is None:
ub.append(cplex.infinity)
else:
ub.append(variable.ub)
vtype.append(_VTYPE_TO_CPLEX_VTYPE[variable.type])
names.append(variable.name)
coeff.append(0.)
variable.problem = self

if set(vtype) == set(['C']): # this is needed because CPLEX will automatically set the problem_type to MILP if types are specified
self.problem.variables.add(coeff, lb=lb, ub=ub, names=names)
else:
self.problem.variables.add([0.], lb=[lb], ub=[ub], types=[vtype], names=[variable.name])
variable.problem = self
return variable
self.problem.variables.add(coeff, lb=lb, ub=ub, types=vtype, names=names)

def _remove_variables(self, variables):
# Not calling parent method to avoid expensive variable removal from sympy expressions
self.objective._expression = self.objective.expression.xreplace({var: 0 for var in variables})
if self.objective is not None:
self.objective._expression = self.objective.expression.xreplace({var: 0 for var in variables})
self.problem.variables.delete([variable.name for variable in variables])
for variable in variables:
del self._variables_to_constraints_mapping[variable.name]
variable.problem = None
del self.variables[variable.name]

def _add_constraint(self, constraint, sloppy=False):
super(Model, self)._add_constraint(constraint, sloppy=sloppy)
constraint._problem = None
if constraint.is_Linear:
if constraint.expression.is_Add:
coeff_dict = constraint.expression.as_coefficients_dict()
indices = [var.name for var in coeff_dict.keys()]
values = [float(val) for val in coeff_dict.values()]
elif constraint.expression.is_Mul:
variable = list(constraint.expression.atoms(sympy.Symbol))[0]
indices = [variable.name]
values = [float(constraint.expression.coeff(variable))]
elif constraint.expression.is_Atom and constraint.expression.is_Symbol:
indices = [constraint.expression.name]
values = [1.]
elif constraint.expression.is_Number:
indices = []
values = []
else:
raise ValueError('Something is fishy with constraint %s' % constraint)
del self._variables[variable.name]

sense, rhs, range_value = _constraint_lb_and_ub_to_cplex_sense_rhs_and_range_value(constraint.lb, constraint.ub)
if constraint.indicator_variable is None:
self.problem.linear_constraints.add(
lin_expr=[cplex.SparsePair(ind=indices, val=values)], senses=[sense], rhs=[rhs],
range_values=[range_value], names=[constraint.name])
else:
if sense == 'R':
raise ValueError('CPLEX does not support indicator constraints that have both an upper and lower bound.')
def _add_constraints(self, constraints, sloppy=False):
super(Model, self)._add_constraints(constraints, sloppy=sloppy)

for constraint in constraints:
constraint._problem = None
linear_constraints = dict(lin_expr=[], senses=[], rhs=[], range_values=[], names=[])
if constraint.is_Linear:
if constraint.expression.is_Add:
coeff_dict = constraint.expression.as_coefficients_dict()
indices = [var.name for var in coeff_dict.keys()]
values = [float(val) for val in coeff_dict.values()]
elif constraint.expression.is_Mul:
variable = list(constraint.expression.atoms(sympy.Symbol))[0]
indices = [variable.name]
values = [float(constraint.expression.coeff(variable))]
elif constraint.expression.is_Atom and constraint.expression.is_Symbol:
indices = [constraint.expression.name]
values = [1.]
elif constraint.expression.is_Number:
indices = []
values = []
else:
self.problem.indicator_constraints.add(
lin_expr=cplex.SparsePair(ind=indices, val=values), sense=sense, rhs=rhs, name=constraint.name,
indvar=constraint.indicator_variable.name, complemented=abs(constraint.active_when)-1)
# TODO: Implement quadratic constraints
elif constraint.is_Quadratic:
raise NotImplementedError('Quadratic constraints (like %s) are not supported yet.' % constraint)
else:
raise ValueError("CPLEX only supports linear or quadratic constraints. %s is neither linear nor quadratic." % constraint)
constraint.problem = self
return constraint
raise ValueError('Something is fishy with constraint %s' % constraint)

sense, rhs, range_value = _constraint_lb_and_ub_to_cplex_sense_rhs_and_range_value(constraint.lb,
constraint.ub)
if constraint.indicator_variable is None:
linear_constraints['lin_expr'].append(cplex.SparsePair(ind=indices, val=values))
linear_constraints['senses'].append(sense)
linear_constraints['rhs'].append(rhs)
linear_constraints['range_values'].append(range_value)
linear_constraints['names'].append(constraint.name)
else:
if sense == 'R':
raise ValueError(
'CPLEX does not support indicator constraints that have both an upper and lower bound.')
else:
# Indicator constraints cannot be added in batch
self.problem.indicator_constraints.add(
lin_expr=cplex.SparsePair(ind=indices, val=values), sense=sense, rhs=rhs,
name=constraint.name,
indvar=constraint.indicator_variable.name, complemented=abs(constraint.active_when) - 1)
# TODO: Implement quadratic constraints
elif constraint.is_Quadratic:
raise NotImplementedError('Quadratic constraints (like %s) are not supported yet.' % constraint)
else:
raise ValueError(
"CPLEX only supports linear or quadratic constraints. %s is neither linear nor quadratic." % constraint)
self.problem.linear_constraints.add(**linear_constraints)
constraint.problem = self

def _remove_constraints(self, constraints):
super(Model, self)._remove_constraints(constraints)
Expand All @@ -827,9 +840,9 @@ def _get_quadratic_expression(self, quadratic=None):
for i, sparse_pair in enumerate(quadratic):
for j, val in zip(sparse_pair.ind, sparse_pair.val):
if i < j:
terms.append(val*self.variables[i]*self.variables[j])
terms.append(val*self._variables[i]*self._variables[j])
elif i == j:
terms.append(0.5*val*self.variables[i]**2)
terms.append(0.5*val*self._variables[i]**2)
else:
pass # Only look at upper triangle
return _unevaluated_Add(*terms)
Expand Down

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