How to find symmetric mean absolute error in python?

Question

How can I calculate symmetric mean absolute error in python using numpy or pandas? Is there are metric present in scikit sklearn ?

Example data:

Actual value:   2,3,4,5,6,7,8,9
Forecast value: 1,3,5,4,6,7,10,7

Formula for SMAPE see screenshot below:

How can I do it in python using pandas or numpy and calculate SMAPE.

Note: More info about SMAPE: https://en.wikipedia.org/wiki/Symmetric_mean_absolute_percentage_error

Answer 1

It's pretty straightforward to convert the equation to numpy

import numpy as np

def smape(A, F):
    return 100/len(A) * np.sum(2 * np.abs(F - A) / (np.abs(A) + np.abs(F)))

A = np.array([2,3,4,5,6,7,8,9])
F = np.array([1,3,5,4,6,7,10,7])
print(smape(A, F))

Answer 2

I commented on the accepted answer but if you just want to copy and paste:

import numpy as np

def smape(A, F):
    tmp = 2 * np.abs(F - A) / (np.abs(A) + np.abs(F))
    len_ = np.count_nonzero(~np.isnan(tmp))
    if len_ == 0 and np.nansum(tmp) == 0: # Deals with a special case
        return 100
    return 100 / len_ * np.nansum(tmp)

A = np.array([2,3,4,5,6,7,8,0])
F = np.array([1,3,5,4,6,7,10,0])
print(smape(A, F))