检索 scipy 最小化函数最低错误

Question

有没有办法在 scipy.minimize 收敛后直接检索最小化误差，或者必须将其直接编码到成本函数中？

我好像只能检索收敛到的系数。

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

据我从函数 scipy.optimize.minimize 的文档中了解到，结果作为 OptimizeResult 对象返回。

根据此 class (here) 的文档，它有一个属性 fun，即 "values of objective function"。

因此，如果您这样做 opt.fun，您应该会获得您要查找的结果。（您可以检索更多值，例如 Jacobian opt.jac、Hessian opt.hess，...如文档中所述）

检索 scipy 最小化函数最低错误

Retrieve scipy minimize function lowest error

python

minimize

scipy