for i in range(500):
print("epoch: ", i)
graph = generate_random_graph(100, 250).adj
trainer.train2(graph, 10 * beta ** i)
trainer.test()
Timer.end('all')
score = 0
coef = 1
for all_rewards in reversed(trainer.test_result):
coef *= 0.9
for rewards in all_rewards:
score += 10 * coef * np.max(rewards)
score += coef * np.mean(rewards)
Timer.print()
trainer.save_model()
trainer.save_test_result()
return -score
if __name__ == '__main__':
Timer.disable()
Counter.disable()
os.makedirs("model", exist_ok=True)
study = optuna.create_study()
study.optimize(objective, timeout=3600 * 8, n_jobs=-1)
print("params_{}".format(study.best_params))
print("value_{}".format(study.best_value))
