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AICup/run.py
UmbertoJr b8b4840408 run
2019-12-02 09:11:15 +01:00

64 lines
2.4 KiB
Python

from src import *
import pandas as pd
def add(solver, instance, improve, index, results, name, verbose, show_plots):
solver.bind(improve)
solver(instance, return_value=False, verbose=verbose)
if verbose:
print(f"the total length for the solution found is {solver.found_length}",
f"while the optimal length is {instance.best_sol}",
f"the gap is {solver.gap}%",
f"the solution is found in {solver.time_to_solve} seconds", sep="\n")
index.append((name, solver.name_method))
results.append([solver.found_length, instance.best_sol, solver.gap, solver.time_to_solve])
if show_plots:
solver.plot_solution()
def run(show_plots=False, verbose=False):
# names = [name_ for name_ in os.listdir("./problems") if "tsp" in name_]
names = ["eil76.tsp"]
initializers = Solver_TSP.available_initializers.keys()
improvements = Solver_TSP.available_improvements.keys()
results = []
index = []
for name in names:
filename = f"problems/{name}"
instance = Instance(filename)
if verbose:
print("\n\n#############################")
instance.print_info()
if show_plots:
instance.plot_data()
for init in initializers:
for improve in improvements:
solver = Solver_TSP(init)
add(solver, instance, improve, index, results, name, verbose, show_plots)
for improve2 in [j for j in improvements if j not in [improve]]:
add(solver, instance, improve2, index, results, name, verbose, show_plots)
for improve3 in [j for j in improvements if j not in [improve, improve2]]:
add(solver, instance, improve3, index, results, name, verbose, show_plots)
solver.pop()
solver.pop()
if instance.exist_opt and show_plots:
solver.solution = np.concatenate([instance.optimal_tour, [instance.optimal_tour[0]]])
solver.method = "optimal"
solver.plot_solution()
index = pd.MultiIndex.from_tuples(index, names=['problem', 'method'])
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")