Python - 切片索引必须是整数或无或具有 __index__ 方法

Question

我在使用 scipy 的 fmin 求解器时遇到问题。 我希望它为我的 ARIMA 模型优化参数。 当直接运行代码（没有 fmin）时，一切正常，但在放入优化器时出现错误：

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

这是代码：

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)

正如我之前所说 - 如果我在函数之外运行代码 - 一切正常。 问题是当它进入 fmin 求解器的函数时。

Answer 1

这是因为fmin正在将浮点数传递给参数，并且它们需要是int您可以通过将parametres为 int 来修复它，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，您在函数中使用变量values ，而它未在函数中定义，这也可能会导致问题。

注意：为了在fmin之外使用它，您需要传入一个 numpy 数组，而不是将其转换为函数中的元组。