def solver_statistics(solver_type, solver):
time_optimizer = None
d_solver = {}
if solver_type == "cp":
d_solver["n_booleans"] = solver.NumBooleans()
d_solver["n_branches"] = solver.NumBranches()
d_solver["n_conflicts"] = solver.NumConflicts()
d_solver["objective"] = int(solver.ObjectiveValue())
d_solver["best_objective_bound"] = int(solver.BestObjectiveBound())
time_optimizer = solver.WallTime()
elif solver_type == "mip":
d_solver["n_constraints"] = solver.NumConstraints()
d_solver["n_variables"] = solver.NumVariables()
d_solver["objective"] = solver.Objective().Value()
d_solver["best_bound"] = solver.Objective().BestBound()
elif solver_type == "ls":
if not LOCALSOLVER_AVAILABLE:
raise ImportError('Cannot import localsolver. Install LocalSolver '
'or choose another solver, options are "cp" and '
'"mip".')
d_solver["n_iterations"] = LSStatistics.get_nb_iterations(
solver.statistics)
elif solver_type == "lp":
d_solver["n_variables"] = solver.n_variables
d_solver["n_constraints"] = solver.n_constraints
d_solver["n_iterations"] = solver.n_iterations
d_solver["objective"] = solver.objective
return d_solver, time_optimizer
def print_solver_statistics(solver_type, solver):
if solver_type == "cp":
n_booleans = solver.NumBooleans()
n_branches = solver.NumBranches()
n_conflicts = solver.NumConflicts()
objective = int(solver.ObjectiveValue())
best_objective_bound = int(solver.BestObjectiveBound())
solver_stats = (
" Solver statistics\n"
" Type {:>10}\n"
" Number of booleans {:>10}\n"
" Number of branches {:>10}\n"
" Number of conflicts {:>10}\n"
" Objective value {:>10}\n"
" Best objective bound {:>10}\n"
).format(solver_type, n_booleans, n_branches, n_conflicts,
objective, best_objective_bound)
elif solver_type == "mip":
n_constraints = solver.NumConstraints()
n_variables = solver.NumVariables()
objective = solver.Objective().Value()
best_bound = solver.Objective().BestBound()
solver_stats = (
" Solver statistics\n"
" Type {:>10}\n"
" Number of variables {:>10}\n"
" Number of constraints {:>10}\n"
" Objective value {:>10.4f}\n"
" Best objective bound {:>10.4f}\n"
).format(solver_type, n_variables, n_constraints, objective,
best_bound)
elif solver_type == "ls":
if not LOCALSOLVER_AVAILABLE:
raise ImportError('Cannot import localsolver. Install LocalSolver '
'or choose another solver, options are "cp" and '
'"mip".')
n_iterations = LSStatistics.get_nb_iterations(solver.statistics)
solver_stats = (
" Solver statistics\n"
" Type {:>10}\n"
" Number of iterations {:>10}\n"
).format(solver_type, n_iterations)
print(solver_stats)