How to optimize MAPE code in Python?

Question

I need to have a MAPE function, however I was not able to find it in standard packages ... Below, my implementation of this function.

def mape(actual, predic         


        

Answer 1

Another similar way of doing it using masked_Arrays to mask division by zero is:

import numpy.ma as ma
masked_actual = ma.masked_array(actual, mask=actual==0)
MAPE = (np.fabs(masked_actual - predict)/masked_actual).mean()

Answer 2

Here's one vectorized approach with masking -

def mape_vectorized(a, b): 
    mask = a <> 0
    return (np.fabs(a[mask] - b[mask])/a[mask]).mean()

Probably a faster one with masking after division computation -

def mape_vectorized_v2(a, b): 
    mask = a <> 0
    return (np.fabs(a - b)/a)[mask].mean() 

Runtime test -

In [217]: a = np.random.randint(-10,10,(10000))
     ...: b = np.random.randint(-10,10,(10000))
     ...: 

In [218]: %timeit mape(a,b)
100 loops, best of 3: 11.7 ms per loop

In [219]: %timeit mape_vectorized(a,b)
1000 loops, best of 3: 273 µs per loop

In [220]: %timeit mape_vectorized_v2(a,b)
1000 loops, best of 3: 220 µs per loop