Python - 切片索引必須是整數或無或具有 __index__ 方法

Question

我在使用 scipy 的 fmin 求解器時遇到問題。 我希望它為我的 ARIMA 模型優化參數。 當直接運行代碼（沒有 fmin）時，一切正常，但在放入優化器時出現錯誤：

Traceback (most recent call last):   
File "D:/Work/repo_python/bandwidth_estimation/estima.py", line 169, in <module>
    optimum = fmin(criterion, x0)   
File "C:\Users\Pigeon\AppData\Local\Programs\Python\Python36\lib\site-packages\scipy\optimize\optimize.py", line 442, in fmin
    res = _minimize_neldermead(func, x0, args, callback=callback, **opts)   
File "C:\Users\Pigeon\AppData\Local\Programs\Python\Python36\lib\site-packages\scipy\optimize\optimize.py", line 585, in _minimize_neldermead
    fsim[k] = func(sim[k])   
File "C:\Users\Pigeon\AppData\Local\Programs\Python\Python36\lib\site-packages\scipy\optimize\optimize.py", line 326, in function_wrapper
    return function(*(wrapper_args + args))   
File "D:/Work/repo_python/bandwidth_estimation/estima.py", line 18, in criterion
    model_arima = ARIMA(train, order=parametres)   
File "C:\Users\Pigeon\AppData\Local\Programs\Python\Python36\lib\site-packages\statsmodels\tsa\arima_model.py", line 988, in __new__
    mod.__init__(endog, order, exog, dates, freq, missing)   
File "C:\Users\Pigeon\AppData\Local\Programs\Python\Python36\lib\site-packages\statsmodels\tsa\arima_model.py", line 1009, in __init__
    self._first_unintegrate = unintegrate_levels(self.endog[:d], d) 
TypeError: slice indices must be integers or None or have an __index__ method

這是代碼：

def criterion(parametres):
    parametres = tuple(parametres)
    control_sum = 0
    train = [5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.361409086288781, 5.899251779517743, 5.766256093003701, 5.795872889382786, 5.637485909370455, 5.7453759249241045, 5.066030606246879, 5.0944229577563425, 5.0944229577563425, 5.0944229577563425, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.361409086288781, 5.899251779517743, 5.766256093003701, 5.795872889382786, 5.637485909370455, 5.7453759249241045, 5.066030606246879, 5.0944229577563425, 5.0944229577563425, 5.0944229577563425, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.361409086288781, 5.899251779517743, 5.766256093003701, 5.795872889382786, 5.637485909370455, 5.7453759249241045, 5.066030606246879, 5.0944229577563425, 5.0944229577563425, 5.0944229577563425, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.361409086288781, 5.899251779517743, 5.766256093003701, 5.795872889382786, 5.637485909370455, 5.7453759249241045, 5.066030606246879, 5.0944229577563425, 5.0944229577563425, 5.0944229577563425, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.361409086288781, 5.899251779517743, 5.766256093003701, 5.795872889382786, 5.637485909370455, 5.7453759249241045, 5.066030606246879, 5.0944229577563425, 5.0944229577563425, 5.0944229577563425, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.361409086288781, 5.899251779517743, 5.766256093003701, 5.795872889382786, 5.637485909370455, 5.7453759249241045, 5.066030606246879, 5.0944229577563425, 5.0944229577563425, 5.0944229577563425, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.361409086288781, 5.899251779517743, 5.766256093003701, 5.795872889382786, 5.637485909370455, 5.7453759249241045, 5.066030606246879, 5.0944229577563425, 5.0944229577563425, 5.0944229577563425, 5.545763155811349]
    model_arima = ARIMA(train, order=parametres)
    model_arima_fit = model_arima.fit()

    predictions = model_arima_fit.forecast(steps=24)[0]
    for i in range(0,len(predictions)):
        control_sum += (predictions[i]-values[i])**2
    print(control_sum)
    return control_sum

x0 = [1,1,1]
optimum = fmin(criterion, x0)

正如我之前所說 - 如果我在函數之外運行代碼 - 一切正常。 問題是當它進入 fmin 求解器的函數時。

Answer 1

這是因為fmin正在將浮點數傳遞給參數，並且它們需要是int您可以通過將parametres為 int 來修復它，如下所示

parametres = parametres.astype(int)

和所有在一起：

def criterion(parametres):
    parametres = parametres.astype(int)
    control_sum = 0
    train = [5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.361409086288781, 5.899251779517743, 5.766256093003701, 5.795872889382786, 5.637485909370455, 5.7453759249241045, 5.066030606246879, 5.0944229577563425, 5.0944229577563425, 5.0944229577563425, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.361409086288781, 5.899251779517743, 5.766256093003701, 5.795872889382786, 5.637485909370455, 5.7453759249241045, 5.066030606246879, 5.0944229577563425, 5.0944229577563425, 5.0944229577563425, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.361409086288781, 5.899251779517743, 5.766256093003701, 5.795872889382786, 5.637485909370455, 5.7453759249241045, 5.066030606246879, 5.0944229577563425, 5.0944229577563425, 5.0944229577563425, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.361409086288781, 5.899251779517743, 5.766256093003701, 5.795872889382786, 5.637485909370455, 5.7453759249241045, 5.066030606246879, 5.0944229577563425, 5.0944229577563425, 5.0944229577563425, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.361409086288781, 5.899251779517743, 5.766256093003701, 5.795872889382786, 5.637485909370455, 5.7453759249241045, 5.066030606246879, 5.0944229577563425, 5.0944229577563425, 5.0944229577563425, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.361409086288781, 5.899251779517743, 5.766256093003701, 5.795872889382786, 5.637485909370455, 5.7453759249241045, 5.066030606246879, 5.0944229577563425, 5.0944229577563425, 5.0944229577563425, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.545763155811349, 5.361409086288781, 5.899251779517743, 5.766256093003701, 5.795872889382786, 5.637485909370455, 5.7453759249241045, 5.066030606246879, 5.0944229577563425, 5.0944229577563425, 5.0944229577563425, 5.545763155811349]
    print(parametres)
    model_arima = ARIMA(train, order=parametres)
    model_arima_fit = model_arima.fit()

    predictions = model_arima_fit.forecast(steps=24)[0]
    for i in range(0,len(predictions)):
        control_sum += (predictions[i]-values[i])**2
    print(control_sum)
    return control_sum

x0 = [1,1,1]
optimum = fmin(criterion, x0)

此外，您在函數中使用變量values ，而它未在函數中定義，這也可能會導致問題。

注意：為了在fmin之外使用它，您需要傳入一個 numpy 數組，而不是將其轉換為函數中的元組。