检索scipy最小化函数lowes

Question

有没有办法在scipy.minimize收敛后直接检索最小化错误，或者必须直接编码到代价函数中

我只能检索到似乎收敛到的系数

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

根据我对函数scipy.optimize.minimize文档的理解，结果作为OptimizeResult对象返回

从这个类（here）的文档来看，它有一个属性fun，即“目标函数的值”

所以如果你做opt.fun，你应该得到你想要的结果(您可以检索更多的值，如Jacobian opt.jac，Hessian opt.hess。。。如文件所述）