Scipy optimization - incorrect number of arguments

user_10621193 0
Viewed 37

simple question/issue

def my_func(x,args):
  a = x[0]
  b = x[1]
  return a*args[0] + a*args[1] + b*args[2]

import scipy.optimize as optimize

params = [1,2,3]
guess = [3,4]
bounds = [(1,5),(2,7)]

result = optimize.minimize(my_func, x0=guess, args=(params), method='SLSQP', bounds = bounds)
print(result)
print('done')
result = optimize.brute(my_func, args = tuple(params), ranges = bounds)
print(result)

The first optimisation runs fine. However, the second optimisation seems to have an issue with the number of input arguments? Not sure why:

    fun: 9.0
     jac: array([3., 3.])
 message: 'Optimization terminated successfully'
    nfev: 6
     nit: 2
    njev: 2
  status: 0
 success: True
       x: array([1., 2.])
done
Traceback (most recent call last):
  File "c:\Users\bob\Desktop\optimiser.py", line 15, in <module>
    result = optimize.brute(my_func, args = tuple(params), ranges = bounds)
  File "C:\Users\bob\Anaconda3\lib\site-packages\scipy\optimize\optimize.py", line 3328, in brute
    Jout = np.array(list(mapper(wrapped_func, grid)))
  File "C:\Users\bob\Anaconda3\lib\site-packages\scipy\optimize\optimize.py", line 3400, in __call__
    return self.f(np.asarray(x).flatten(), *self.args)
TypeError: my_func() takes 2 positional arguments but 4 were given

I don't understand why it thinks 4 arguments are being given to my_func. Any ideas?

Cheers

scipy optimization python
1 Answers

Because optimize.brute expands the args argument when it passes these to the callable function. optimize.minimize does the same thing, but that function first turns args into a tuple if it is not already:

if not isinstance(args, tuple):
    args = (args,)

See here for details. optimize.brute does not do this. Thus, what you get is a tuple of a list ([1, 2, 3],) when calling optimize.minimize, and [1, 2, 3] when calling optimize.brute. The former expands to a single argument, the latter to three arguments.

You can fix this by playing nice. Define your function as taking a variadic number of arguments:

def my_func(x, *args):
    a = x[0]
    b = x[1]
    return a * args[0] + a * args[1] + b * args[2]

And specify the additional arguments as a tuple:

params = (1, 2, 3)
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