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