python - 属性错误: 'float' object has no attribute 'gradient' scipy python

Question

我正在使用 scipy 优化线性函数，

   def func(weights):
        var = ['x1', 'x2', 'x3', 'x4']
        if weights is None:
           weights = np.ones(len(var)) / len(var)
        return len(set([var[i] for i in range(len(weights)) if weights[i]>0]))/len(var)

res = minimize(lambda x: func(x), x0=[0.25,0.25,0.25,0.25],method='SLSQP',
                       jac=ad.gh(lambda x: func(x))[0], bounds=((0.,1.),)*4,
                       options = {'disp':True, 'ftol': 1e-20, 'maxiter': 1000},
                       constraints= {'type': 'eq', 'fun': lambda x: sum(x) - 1.0})

我收到以下错误。

Traceback (most recent call last):
  File "D:/applicatio/Sub Applicatio/main.py", line 338, in <module>
    constraints= {'type': 'eq', 'fun': lambda x: sum(x) - 1.0})
  File "C:\Users\hp\Downloads\WinPython-64bit-3.5.1.2\python-3.5.1.amd64\lib\site-packages\scipy\optimize\_minimize.py", line 455, in minimize
    constraints, callback=callback, **options)
  File "C:\Users\hp\Downloads\WinPython-64bit-3.5.1.2\python-3.5.1.amd64\lib\site-packages\scipy\optimize\slsqp.py", line 383, in _minimize_slsqp
    g = append(fprime(x),0.0)
  File "C:\Users\hp\Downloads\WinPython-64bit-3.5.1.2\python-3.5.1.amd64\lib\site-packages\scipy\optimize\optimize.py", line 289, in function_wrapper
    return function(*(wrapper_args + args))
  File "C:\Users\hp\Downloads\WinPython-64bit-3.5.1.2\python-3.5.1.amd64\lib\site-packages\ad\__init__.py", line 1090, in grad
    return numpy.array(ans.gradient(list(xa)))
AttributeError: 'float' object has no attribute 'gradient'

如何优化这种简单的线性函数？有什么建议 ？谢谢。

Answer 1

你到底是什么func在做什么？

def func(weights):
    ....
    return len(set())/len(var)

您会得到 set对象，然后是长度，项数。那代表什么？这不是线性的；它需要整数跳转。

In [318]: x0=[0.25,0.25,0.25,0.25]
In [319]: def func(weights):
     ...:         var = ['x1', 'x2', 'x3', 'x4']
     ...:         if weights is None:
     ...:            weights = np.ones(len(var)) / len(var)
     ...:         return len(set([var[i] for i in range(len(weights)) if weights
     ...: [i]>0]))/len(var)
     ...:     
In [320]: func(x0)
Out[320]: 1.0
In [321]: x0=np.array(x0)
In [322]: func(x0)
Out[322]: 1.0
In [323]: func(x0+.1)
Out[323]: 1.0
In [324]: func(x0-.1)
Out[324]: 1.0
In [325]: func(x0-1)
Out[325]: 0.0

事实上，它所做的只是计算 x0 有多少个。值为 >0除以 4 - 因此生成 0、.25、.5、.75 或 1。

minimize将从 x0 开始，并找出如何func(x0)因 x0 中的微小变化而变化。

还有你的jac ，是基于此 func 的东西还有，jac=ad.gh(lambda x: func(x))[0]

==============

我认为您不需要使用 lambda

 `lambda x: func(x)`

只要给 func作为论点。它需要正确数量的参数(例如初始的 x0 )。

====================

运行代码，但没有 jac参数(我不知道 ad.gh 是什么):

In [543]: def func(weights):
     ...:         var = ['x1', 'x2', 'x3', 'x4']
     ...:         if weights is None:
     ...:            weights = np.ones(len(var)) / len(var)
     ...:         return len(set([var[i] for i in range(len(weights)) if weights
     ...: [i]>0]))/len(var)
     ...:     
In [544]: optimize.minimize(lambda x: func(x), x0=[0.25,0.25,0.25,0.25],method='
     ...: SLSQP',bounds=((0.,1.),)*4,options = {'disp':True, 'ftol': 1e-20, 'max
     ...: iter': 1000},constraints= {'type': 'eq', 'fun': lambda x: sum(x) - 1.0
     ...: })
Optimization terminated successfully.    (Exit mode 0)
            Current function value: 1.0
            Iterations: 1
            Function evaluations: 6
            Gradient evaluations: 1
Out[544]: 
     fun: 1.0
     jac: array([ 0.,  0.,  0.,  0.,  0.])
 message: 'Optimization terminated successfully.'
    nfev: 6
     nit: 1
    njev: 1
  status: 0
 success: True
       x: array([ 0.25,  0.25,  0.25,  0.25])

看起来它正在尝试围绕 x0 进行一些小更改，并发现没有任何变化(小的变化不会使任何元素变为 0)。换句话说，您的func已经处于局部最小值，平坦区域。