class Cbc(PersistentSolver):
def __init__(self):
self._config = CbcConfig()
self._solver_options = dict()
self._writer = LPWriter()
self._filename = None
self._dual_sol = dict()
self._primal_sol = dict()
self._reduced_costs = dict()
def available(self, exception_flag=False):
if self.config.executable.path() is None:
if exception_flag:
raise RuntimeError('Cbc is not available')
return False
return True
def version(self):
results = subprocess.run([str(self.config.executable), '-stop'],
timeout=1,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
universal_newlines=True)
version = results.stdout.splitlines()[1]
version = version.split(':')[1]
version = version.strip()
version = tuple(int(i) for i in version.split('.'))
return version
def lp_filename(self):
if self._filename is None:
return None
else:
return self._filename + '.lp'
def log_filename(self):
if self._filename is None:
return None
else:
return self._filename + '.log'
def soln_filename(self):
if self._filename is None:
return None
else:
return self._filename + '.soln'
@property
def config(self):
return self._config
@property
def solver_options(self):
return self._solver_options
@property
def update_config(self):
return self._writer.update_config
def set_instance(self, model):
self._writer.set_instance(model)
def add_variables(self, variables: List[_GeneralVarData]):
self._writer.add_variables(variables)
def add_params(self, params: List[_ParamData]):
self._writer.add_params(params)
def add_constraints(self, cons: List[_GeneralConstraintData]):
self._writer.add_constraints(cons)
def add_block(self, block: _BlockData):
self._writer.add_block(block)
def remove_variables(self, variables: List[_GeneralVarData]):
self._writer.remove_variables(variables)
def remove_params(self, params: List[_ParamData]):
self._writer.remove_params(params)
def remove_constraints(self, cons: List[_GeneralConstraintData]):
self._writer.remove_constraints(cons)
def remove_block(self, block: _BlockData):
self._writer.remove_block(block)
def set_objective(self, obj: _GeneralObjectiveData):
self._writer.set_objective(obj)
def update_variables(self, variables: List[_GeneralVarData]):
self._writer.update_variables(variables)
def update_params(self):
self._writer.update_params()
def solve(self, model, timer: HierarchicalTimer = None):
self.available(exception_flag=True)
if timer is None:
timer = HierarchicalTimer()
try:
TempfileManager.push()
if self.config.filename is None:
self._filename = TempfileManager.create_tempfile()
else:
self._filename = self.config.filename
TempfileManager.add_tempfile(self._filename + '.lp', exists=False)
TempfileManager.add_tempfile(self._filename + '.soln', exists=False)
TempfileManager.add_tempfile(self._filename + '.log', exists=False)
timer.start('write lp file')
self._writer.write(model, self._filename+'.lp', timer=timer)
timer.stop('write lp file')
res = self._apply_solver(timer)
if self.config.report_timing:
logger.info('\n' + str(timer))
return res
finally:
# finally, clean any temporary files registered with the
# temp file manager, created/populated *directly* by this
# plugin.
TempfileManager.pop(remove=not self.config.keepfiles)
if not self.config.keepfiles:
self._filename = None
if self.config.report_timing:
print(timer)
def _parse_soln(self):
results = Results()
f = open(self._filename + '.soln', 'r')
all_lines = list(f.readlines())
f.close()
termination_line = all_lines[0].lower()
obj_val = None
if termination_line.startswith('optimal'):
results.termination_condition = TerminationCondition.optimal
obj_val = float(termination_line.split()[-1])
elif 'infeasible' in termination_line:
results.termination_condition = TerminationCondition.infeasible
elif 'unbounded' in termination_line:
results.termination_condition = TerminationCondition.unbounded
elif termination_line.startswith('stopped on time'):
results.termination_condition = TerminationCondition.maxTimeLimit
obj_val = float(termination_line.split()[-1])
elif termination_line.startswith('stopped on iterations'):
results.termination_condition = TerminationCondition.maxIterations
obj_val = float(termination_line.split()[-1])
else:
results.termination_condition = TerminationCondition.unknown
first_con_line = None
last_con_line = None
first_var_line = None
last_var_line = None
for ndx, line in enumerate(all_lines):
if line.strip('*').strip().startswith('0'):
if first_con_line is None:
first_con_line = ndx
else:
last_con_line = ndx - 1
first_var_line = ndx
last_var_line = len(all_lines) - 1
self._dual_sol = dict()
self._primal_sol = dict()
self._reduced_costs = dict()
symbol_map = self._writer.symbol_map
for line in all_lines[first_con_line:last_con_line+1]:
split_line = line.strip('*')
split_line = split_line.split()
name = split_line[1]
orig_name = name[:-3]
if orig_name == 'obj_const_con':
continue
con = symbol_map.bySymbol[orig_name]()
dual_val = float(split_line[-1])
if con in self._dual_sol:
if abs(dual_val) > abs(self._dual_sol[con]):
self._dual_sol[con] = dual_val
else:
self._dual_sol[con] = dual_val
for line in all_lines[first_var_line:last_var_line+1]:
split_line = line.strip('*')
split_line = split_line.split()
name = split_line[1]
if name == 'obj_const':
continue
val = float(split_line[2])
rc = float(split_line[3])
var = symbol_map.bySymbol[name]()
self._primal_sol[id(var)] = (var, val)
self._reduced_costs[id(var)] = (var, rc)
if (self.version() < (2, 10, 2) and
self._writer.get_active_objective() is not None and
self._writer.get_active_objective().sense == maximize):
obj_val = -obj_val
for con, dual_val in self._dual_sol.items():
self._dual_sol[con] = -dual_val
for v_id, (v, rc_val) in self._reduced_costs.items():
self._reduced_costs[v_id] = (v, -rc_val)
if results.termination_condition == TerminationCondition.optimal and self.config.load_solution:
for v_id, (v, val) in self._primal_sol.items():
v.value = val
if self._writer.get_active_objective() is None:
results.best_feasible_objective = None
else:
results.best_feasible_objective = obj_val
elif results.termination_condition == TerminationCondition.optimal:
if self._writer.get_active_objective() is None:
results.best_feasible_objective = None
else:
results.best_feasible_objective = obj_val
elif self.config.load_solution:
raise RuntimeError('A feasible solution was not found, so no solution can be loaded.'
'Please set opt.config.load_solution=False and check '
'results.termination_condition and '
'resutls.best_feasible_objective before loading a solution.')
return results
def _apply_solver(self, timer: HierarchicalTimer):
config = self.config
if config.time_limit is not None:
timeout = config.time_limit + min(max(1, 0.01 * config.time_limit), 100)
else:
timeout = None
def _check_and_escape_options():
for key, val in self.solver_options.items():
tmp_k = str(key)
_bad = ' ' in tmp_k
tmp_v = str(val)
if ' ' in tmp_v:
if '"' in tmp_v:
if "'" in tmp_v:
_bad = True
else:
tmp_v = "'" + tmp_v + "'"
else:
tmp_v = '"' + tmp_v + '"'
if _bad:
raise ValueError("Unable to properly escape solver option:"
"\n\t%s=%s" % (key, val) )
yield tmp_k, tmp_v
cmd = [str(config.executable)]
action_options = list()
if config.time_limit is not None:
cmd.extend(['-sec', str(config.time_limit)])
cmd.extend(['-timeMode', 'elapsed'])
for key, val in _check_and_escape_options():
if val.strip() != '':
cmd.append('-'+key, val)
else:
action_options.append('-'+key)
cmd.extend(['-printingOptions', 'all'])
cmd.extend(['-import', self._filename + '.lp'])
cmd.extend(action_options)
cmd.extend(['-stat=1'])
cmd.extend(['-solve'])
cmd.extend(['-solu', self._filename + '.soln'])
ostreams = [LogStream(level=self.config.log_level, logger=self.config.solver_output_logger)]
if self.config.stream_solver:
ostreams.append(sys.stdout)
with TeeStream(*ostreams) as t:
timer.start('subprocess')
cp = subprocess.run(cmd,
timeout=timeout,
stdout=t.STDOUT,
stderr=t.STDERR,
universal_newlines=True)
timer.stop('subprocess')
if cp.returncode != 0:
if self.config.load_solution:
raise RuntimeError('A feasible solution was not found, so no solution can be loaded.'
'Please set opt.config.load_solution=False and check '
'results.termination_condition and '
'results.best_feasible_objective before loading a solution.')
results = Results()
results.termination_condition = TerminationCondition.error
results.best_feasible_objective = None
self._primal_sol = None
self._dual_sol = None
self._reduced_costs = None
else:
timer.start('parse solution')
results = self._parse_soln()
timer.stop('parse solution')
if self._writer.get_active_objective() is None:
results.best_feasible_objective = None
results.best_objective_bound = None
else:
if self._writer.get_active_objective().sense == minimize:
results.best_objective_bound = -math.inf
else:
results.best_objective_bound = math.inf
return results
def load_vars(self, vars_to_load: Optional[Sequence[_GeneralVarData]] = None) -> NoReturn:
if vars_to_load is None:
for v_id, (v, val) in self._primal_sol.items():
v.value = val
else:
for v in vars_to_load:
v.value = self._primal_sol[id(v)][1]
def get_duals(self, cons_to_load = None):
if cons_to_load is None:
return {k: v for k, v in self._dual_sol.items()}
else:
return {c: self._dual_sol[c] for c in cons_to_load}
def get_reduced_costs(self, vars_to_load: Optional[Sequence[_GeneralVarData]] = None) -> Mapping[_GeneralVarData, float]:
if vars_to_load is None:
return ComponentMap((k, v) for k, v in self._reduced_costs.values())
else:
return ComponentMap((v, self._reduced_costs[id(v)][1]) for v in vars_to_load)
class Cplex(PersistentSolver):
_available = None
def __init__(self):
self._config = CplexConfig()
self._solver_options = dict()
self._writer = LPWriter()
self._filename = None
self._last_results_object: Optional[CplexResults] = None
try:
import cplex
self._cplex = cplex
self._cplex_model: Optional[cplex.Cplex] = None
self._cplex_available = True
except ImportError:
self._cplex = None
self._cplex_model = None
self._cplex_available = False
@property
def writer(self):
return self._writer
@property
def symbol_map(self):
return self._writer.symbol_map
def available(self):
if Cplex._available is None:
self._check_license()
return Cplex._available
def _check_license(self):
if self._cplex_available:
try:
m = self._cplex.Cplex()
m.variables.add(lb=[0] * 1001)
m.solve()
Cplex._available = self.Availability.FullLicense
except self._cplex.exceptions.errors.CplexSolverError:
try:
m = self._cplex.Cplex()
m.variables.add(lb=[0])
m.solve()
Cplex._available = self.Availability.LimitedLicense
except:
Cplex._available = self.Availability.BadLicense
else:
Cplex._available = self.Availability.NotFound
def version(self):
return tuple(
int(k) for k in self._cplex.Cplex().get_version().split('.'))
def lp_filename(self):
if self._filename is None:
return None
else:
return self._filename + '.lp'
def log_filename(self):
if self._filename is None:
return None
else:
return self._filename + '.log'
@property
def config(self):
return self._config
@config.setter
def config(self, val):
self._config = val
@property
def cplex_options(self):
"""
Returns
-------
cplex_options: dict
A dictionary mapping solver options to values for those options. These
are solver specific.
"""
return self._solver_options
@cplex_options.setter
def cplex_options(self, val: Dict):
self._solver_options = val
@property
def update_config(self):
return self._writer.update_config
def set_instance(self, model):
self._writer.set_instance(model)
def add_variables(self, variables: List[_GeneralVarData]):
self._writer.add_variables(variables)
def add_params(self, params: List[_ParamData]):
self._writer.add_params(params)
def add_constraints(self, cons: List[_GeneralConstraintData]):
self._writer.add_constraints(cons)
def add_block(self, block: _BlockData):
self._writer.add_block(block)
def remove_variables(self, variables: List[_GeneralVarData]):
self._writer.remove_variables(variables)
def remove_params(self, params: List[_ParamData]):
self._writer.remove_params(params)
def remove_constraints(self, cons: List[_GeneralConstraintData]):
self._writer.remove_constraints(cons)
def remove_block(self, block: _BlockData):
self._writer.remove_block(block)
def set_objective(self, obj: _GeneralObjectiveData):
self._writer.set_objective(obj)
def update_variables(self, variables: List[_GeneralVarData]):
self._writer.update_variables(variables)
def update_params(self):
self._writer.update_params()
def solve(self, model, timer: HierarchicalTimer = None):
avail = self.available()
if not avail:
raise PyomoException(
f'Solver {self.__class__} is not available ({avail}).')
if self._last_results_object is not None:
self._last_results_object.solution_loader.invalidate()
if timer is None:
timer = HierarchicalTimer()
try:
TempfileManager.push()
if self.config.filename is None:
self._filename = TempfileManager.create_tempfile()
else:
self._filename = self.config.filename
TempfileManager.add_tempfile(self._filename + '.lp', exists=False)
TempfileManager.add_tempfile(self._filename + '.log', exists=False)
timer.start('write lp file')
self._writer.write(model, self._filename + '.lp', timer=timer)
timer.stop('write lp file')
res = self._apply_solver(timer)
self._last_results_object = res
if self.config.report_timing:
logger.info('\n' + str(timer))
return res
finally:
# finally, clean any temporary files registered with the
# temp file manager, created/populated *directly* by this
# plugin.
TempfileManager.pop(remove=not self.config.keepfiles)
if not self.config.keepfiles:
self._filename = None
def _apply_solver(self, timer: HierarchicalTimer):
config = self.config
timer.start('cplex read lp')
self._cplex_model = cplex_model = self._cplex.Cplex()
cplex_model.read(self._filename + '.lp')
timer.stop('cplex read lp')
log_stream = LogStream(level=self.config.log_level,
logger=self.config.solver_output_logger)
if config.stream_solver:
def _process_stream(arg):
sys.stdout.write(arg)
return arg
cplex_model.set_results_stream(log_stream, _process_stream)
else:
cplex_model.set_results_stream(log_stream)
for key, option in self.cplex_options.items():
opt_cmd = cplex_model.parameters
key_pieces = key.split('_')
for key_piece in key_pieces:
opt_cmd = getattr(opt_cmd, key_piece)
opt_cmd.set(option)
if config.time_limit is not None:
cplex_model.parameters.timelimit.set(config.time_limit)
if config.mip_gap is not None:
cplex_model.parameters.mip.tolerances.mipgap.set(config.mip_gap)
timer.start('cplex solve')
t0 = time.time()
cplex_model.solve()
t1 = time.time()
timer.stop('cplex solve')
return self._postsolve(timer, t1 - t0)
def _postsolve(self, timer: HierarchicalTimer, solve_time):
config = self.config
cpxprob = self._cplex_model
results = CplexResults(solver=self)
results.wallclock_time = solve_time
status = cpxprob.solution.get_status()
if status in [1, 101, 102]:
results.termination_condition = TerminationCondition.optimal
elif status in [2, 40, 118, 133, 134]:
results.termination_condition = TerminationCondition.unbounded
elif status in [4, 119, 134]:
results.termination_condition = TerminationCondition.infeasibleOrUnbounded
elif status in [3, 103]:
results.termination_condition = TerminationCondition.infeasible
elif status in [10]:
results.termination_condition = TerminationCondition.maxIterations
elif status in [11, 25, 107, 131]:
results.termination_condition = TerminationCondition.maxTimeLimit
else:
results.termination_condition = TerminationCondition.unknown
if self._writer.get_active_objective() is None:
results.best_feasible_objective = None
results.best_objective_bound = None
else:
if cpxprob.solution.get_solution_type(
) != cpxprob.solution.type.none:
if (cpxprob.variables.get_num_binary() +
cpxprob.variables.get_num_integer()) == 0:
results.best_feasible_objective = cpxprob.solution.get_objective_value(
)
results.best_objective_bound = cpxprob.solution.get_objective_value(
)
else:
results.best_feasible_objective = cpxprob.solution.get_objective_value(
)
results.best_objective_bound = cpxprob.solution.MIP.get_best_objective(
)
else:
results.best_feasible_objective = None
if cpxprob.objective.get_sense(
) == cpxprob.objective.sense.minimize:
results.best_objective_bound = -math.inf
else:
results.best_objective_bound = math.inf
if config.load_solution:
if cpxprob.solution.get_solution_type(
) == cpxprob.solution.type.none:
raise RuntimeError(
'A feasible solution was not found, so no solution can be loades. '
'Please set opt.config.load_solution=False and check '
'results.termination_condition and '
'results.best_feasible_objective before loading a solution.'
)
else:
if results.termination_condition != TerminationCondition.optimal:
logger.warning(
'Loading a feasible but suboptimal solution. '
'Please set load_solution=False and check '
'results.termination_condition before loading a solution.'
)
timer.start('load solution')
self.load_vars()
timer.stop('load solution')
return results
def get_primals(
self,
vars_to_load: Optional[Sequence[_GeneralVarData]] = None
) -> Mapping[_GeneralVarData, float]:
if self._cplex_model.solution.get_solution_type(
) == self._cplex_model.solution.type.none:
raise RuntimeError(
'Cannot load variable values - no feasible solution was found.'
)
symbol_map = self._writer.symbol_map
if vars_to_load is None:
var_names = self._cplex_model.variables.get_names()
else:
var_names = [symbol_map.byObject[id(v)] for v in vars_to_load]
var_vals = self._cplex_model.solution.get_values(var_names)
res = ComponentMap()
for name, val in zip(var_names, var_vals):
if name == 'obj_const':
continue
v = symbol_map.bySymbol[name]()
res[v] = val
return res
def get_duals(
self,
cons_to_load: Optional[Sequence[_GeneralConstraintData]] = None
) -> Dict[_GeneralConstraintData, float]:
if self._cplex_model.solution.get_solution_type(
) == self._cplex_model.solution.type.none:
raise RuntimeError(
'Cannot get duals - no feasible solution was found.')
if self._cplex_model.get_problem_type() in [
self._cplex_model.problem_type.MILP,
self._cplex_model.problem_type.MIQP,
self._cplex_model.problem_type.MIQCP
]:
raise RuntimeError(
'Cannot get get duals for mixed-integer problems')
symbol_map = self._writer.symbol_map
if cons_to_load is None:
con_names = self._cplex_model.linear_constraints.get_names()
dual_values = self._cplex_model.solution.get_dual_values()
else:
con_names = list()
for con in cons_to_load:
orig_name = symbol_map.byObject[id(con)]
if con.equality:
con_names.append(orig_name + '_eq')
else:
if con.lower is not None:
con_names.append(orig_name + '_lb')
if con.upper is not None:
con_names.append(orig_name + '_ub')
dual_values = self._cplex_model.solution.get_dual_values(con_names)
res = dict()
for name, val in zip(con_names, dual_values):
orig_name = name[:-3]
if orig_name == 'obj_const_con':
continue
_con = symbol_map.bySymbol[orig_name]()
if _con in res:
if abs(val) > abs(res[_con]):
res[_con] = val
else:
res[_con] = val
return res
def get_reduced_costs(
self,
vars_to_load: Optional[Sequence[_GeneralVarData]] = None
) -> Mapping[_GeneralVarData, float]:
if self._cplex_model.solution.get_solution_type(
) == self._cplex_model.solution.type.none:
raise RuntimeError(
'Cannot get reduced costs - no feasible solution was found.')
if self._cplex_model.get_problem_type() in [
self._cplex_model.problem_type.MILP,
self._cplex_model.problem_type.MIQP,
self._cplex_model.problem_type.MIQCP
]:
raise RuntimeError(
'Cannot get get reduced costs for mixed-integer problems')
symbol_map = self._writer.symbol_map
if vars_to_load is None:
var_names = self._cplex_model.variables.get_names()
else:
var_names = [symbol_map.byObject[id(v)] for v in vars_to_load]
rc = self._cplex_model.solution.get_reduced_costs(var_names)
res = ComponentMap()
for name, val in zip(var_names, rc):
if name == 'obj_const':
continue
v = symbol_map.bySymbol[name]()
res[v] = val
return res