AICup/code/run.py

import glob
import pandas as pd
from aco.io_tsp import ProblemInstance
from aco.TSP_solver import TSPSolver
import os
import sys

def run(show_plots=False, verbose=False):
    os.system("rm -f " + " ".join(glob.glob("sol/*.sol") + glob.glob("c_prob/*.txt")))
    if sys.argv[1] == "all":
        os.system("c++ -O2 -lpthread --std=c++11 -o c_prob/aco aco.cc opt.cc")
    problems = glob.glob('./problems/*.tsp') if sys.argv[1] == "all" else [f"./problems/{sys.argv[1]}.tsp"]

    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()

        solver = TSPSolver(prob_instance)
        solution = solver.compute_solution(verbose=verbose)
        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((problem_path, "'C++ ant colony optimization'"))
        results.append([solver.found_length,
                        solver.problem_instance.best_sol,
                        solver.gap,
                        solver.duration])

        with open("sol/" + prob_instance.name + ".sol", "w") as f:
            print(solution, file=f)

        if show_plots:
            solver.plot_solution()

    return pd.DataFrame(results, index=index,
                        columns=["tour length", "optimal solution",
                                 "gap", "time to solve"])


if __name__ == '__main__':
    df = run(show_plots=False, verbose=True)
    df.to_csv("./results.csv")