50 lines
1.9 KiB
Python
50 lines
1.9 KiB
Python
from src import *
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import pandas as pd
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def run(show_plots=False, verbose=False):
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# names = [name_ for name_ in os.listdir("./problems") if "tsp" in name_]
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names = ["eil76.tsp", "kroA100.tsp"]
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initializers = Solver_TSP.available_initializers.keys()
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improvements = Solver_TSP.available_improvements.keys()
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results = []
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index = []
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for name in names:
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filename = f"problems/{name}"
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instance = Instance(filename)
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if verbose:
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print("\n\n#############################")
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instance.print_info()
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if show_plots:
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instance.plot_data()
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for init in initializers:
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solver = Solver_TSP(init)
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for improve in improvements:
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solver.bind(improve)
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solver(instance, return_value=False, verbose=verbose)
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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 {instance.best_sol}",
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f"the gap is {solver.gap}%",
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f"the solution is found in {solver.time_to_solve} seconds", sep="\n")
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index.append((name, solver.name_method))
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results.append([solver.found_length, instance.best_sol, solver.gap, solver.time_to_solve])
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if show_plots:
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solver.plot_solution()
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if instance.exist_opt:
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solver.solution = np.concatenate([instance.optimal_tour, [instance.optimal_tour[0]]])
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solver.method = "optimal"
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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 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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run(show_plots=False, verbose=True).to_csv("./results.csv")
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