检索枕叶最小函数最小误差

Question

是否有一种方法可以在scipy.minimize收敛后直接检索最小化的错误，还是必须直接编码到成本函数中？

我只能检索似乎收敛到系数的信息。

def errorFunction(params,series,loss_function,slen = 12):
    alpha, beta, gamma = params
    breakUps = int(len(series) / slen)
    end = breakUps * slen
    test = series[end:]
    errors = []

    for i in range(2,breakUps+1):
        model = HoltWinters(series=series[:i * 12], slen=slen,
                            alpha=alpha, beta=beta, gamma=gamma, n_preds=len(test))
        model.triple_exponential_smoothing()
        predictions = model.result[-len(test):]
        actual = test
        error = loss_function(predictions, actual)
        errors.append(error)
    return np.mean(np.array(errors))

opt = scipy.optimize.minimize(errorFunction, x0=x,
                   args=(train, mean_squared_log_error),
                   method="L-BFGS-B", bounds = ((0, 1), (0, 1), (0, 1))
                  )
#gets the converged values
optimal values = opt.x
#I would like to know what the error with errorFunction is when using opt.x values, without having to manually run the script again
#Is the minimum error stored somewhere in the returned object opt

Answer 1

根据我从函数scipy.optimize.minimize的文档中了解到的情况，结果将作为OptimizeResult对象返回。

从这个类(这里)的文档来看，它有一个属性fun，即“目标函数的值”。

因此，如果您执行opt.fun，您应该获得您正在寻找的结果。(您可以检索更多的值，如Jacobian opt.jac，Hessian opt.hess，.如文件所述)