import glob import pandas as pd from src.io_tsp import ProblemInstance from src.TSP_solver import TSPSolver, available_improvers, available_solvers import numpy as np def use_solver_to_compute_solution(solver, improve, index, results, name, verbose, show_plots): #solver.bind(improve) # solver.bind("2-opt") # solver.bind("2.5-opt") solver.compute_solution(return_value=False, verbose=verbose) # solver.pop() # solver.pop() if verbose: print(f"the total length for the solution found is {solver.found_length}", f"while the optimal length is {solver.problem_instance.best_sol}", f"the gap is {solver.gap}%", f"the solution is found in {solver.duration} seconds", sep="\n") index.append((name, solver.name_method)) results.append([solver.found_length, solver.problem_instance.best_sol, solver.gap, solver.duration]) if show_plots: solver.plot_solution() def run(show_plots=False, verbose=False): problems = glob.glob('./problems/*.tsp') # problems = ["./problems/fl1577.tsp"] solvers_names = available_solvers.keys() improvers_names = available_improvers.keys() results = [] index = [] for problem_path in problems: prob_instance = ProblemInstance(problem_path) if verbose: prob_instance.print_info() if show_plots: prob_instance.plot_data() for solver_name in solvers_names: solver = TSPSolver(solver_name, prob_instance) use_solver_to_compute_solution(solver, None, index, results, problem_path, verbose, show_plots) # for improve in improvers_names: # solver = TSPSolver(solver_name, prob_instance) # use_solver_to_compute_solution(solver, improve, index, results, problem_path, verbose, show_plots) # for improve2 in [j for j in improvers_names if j not in [improve]]: # use_solver_to_compute_solution(solver, improve2, index, results, problem_path, verbose, show_plots) # # for improve3 in [j for j in improvers_names if j not in [improve, improve2]]: # use_solver_to_compute_solution(solver, improve3, index, results, problem_path, verbose, # show_plots) # solver.pop() # # solver.pop() if prob_instance.exist_opt and show_plots: solver = TSPSolver("optimal", prob_instance) solver.solved = True solver.solution = np.concatenate([prob_instance.optimal_tour, [prob_instance.optimal_tour[0]]]) solver.plot_solution() #index = pd.MultiIndex.from_tuples(index, names=['problem', 'method']) #return None return pd.DataFrame(results, index=index, columns=["tour length", "optimal solution", "gap", "time to solve"]) if __name__ == '__main__': df = run(show_plots=True, verbose=True) df.to_csv("./results.csv")