Python 使用scipy.optimize.brute

基于此，我试图使用brute在ARIMA模型上进行网格扫描，但我无法使其运行。我在做这个原则证明，但我的论点有什么错

y = pd.DataFrame([0,1,4,9,16]) + 3
def objfunc(coeffs, endog):
    exp = coeffs[0]
    const = coeffs[1]
    print(exp, const, endog)
    out = 0
    for i in range(4):
        out += i**exp + const
    return out

from scipy.optimize import brute
grid = (slice(0, 2, 1), slice(3, 4, 1))
brute(objfunc, ranges=grid, args=y)

(0, 3, 0)
(0, 3, 0)
(1, 3, 0)
...
TypeError: objfunc() takes exactly 2 arguments (1 given)

一旦我解决了这个问题，我的目标实际上是在顺序和季节顺序上优化这个函数，分别是这样的元组（u，u，u，u，u，12）

def objfunc(coeffs, endog):
    order = coeffs[0]
    seasonal = coeffs[1]
    fit = sm.tsa.statespace.SARIMAX(endog, trend='n', order=order, seasonal_order=seasonal).fit()
    return fit.aic()

编辑：这段代码有效（多亏了@sasha），变量名更清晰，更有意义（我最小化了错误函数）

---

这些变量名的代码看起来有点奇怪。endog，y；y变成了endog

但以下可能是方法，它完全遵循

args:tuple，可选

完全指定函数所需的任何附加固定参数

代码：

---

变量名是正确的。我编辑了我的问题并包括了你的答案。我可以在SARIMAX上进行优化，我可能会发布它。非常感谢。

import pandas as pd    
y = np.array([0,1,4,9,16]) + 3 #polynomial x^2+3 with x=0:4
def objfunc(coeffs, *args):
    arr = args[0]                       # access first element of tuple: y
    exp = coeffs[0]                       #     assuming y should become endog
    const = coeffs[1]
    pol = [i**exp + const for i in range(len(y))]
    print coeffs
    return abs(sum(pol) - sum(arr))

from scipy.optimize import brute
grid = (slice(1, 3, 1), slice(2, 5, 1))
resbrute = brute(objfunc, ranges=grid, args=(y,), full_output=True, finish=None)
print("Best coeffs: {}".format(resbrute[0]))
print("Score with best coeffs: {}".format(resbrute[1]))
print("Grid: {}".format(resbrute[2].tolist()))
print("Scores for grid: {}".format(resbrute[3].tolist()))

import pandas as pd

y = pd.DataFrame([0,1,4,9,16]) + 3
def objfunc(coeffs, *args):
    endog = args[0]                       # access first element of tuple: y
    exp = coeffs[0]                       #     assuming y should become endog
    const = coeffs[1]
    print(exp, const, endog)
    out = 0
    for i in range(4):
        out += i**exp + const
    return out

from scipy.optimize import brute
grid = (slice(0, 2, 1), slice(3, 4, 1))
brute(objfunc, ranges=grid, args=(y,))    # in general a tuple; first pos: y