Morning!
I find myself often requiring the indices and/or values of the top (or
bottom) k items in a numpy array. I am aware of solutions involving
*partition*/*argpartition *but I find these inelegant (or using *sort *but
these are inefficient).
Is this a feature that would benefit the numpy package, or bloat it? I am
happy to code it up.
Here are some examples:
>> import numpy as np
>> a = np.array( [ [5,8,1,3,0], [5,6,2,1,3], [1,4,9,1,3], [8,0,4,7,0] ] )
>> # PROPOSED FEATURE: return (ordered) top 4 values in array:
>> a.top_k(k=4)
array([9, 8, 8, 7])
>> # CURRENT METHOD: return (ordered) top 4 values in array:
>> np.sort( np.partition(a.flatten(), -4)[-4:] )[::-1]# faster method
array([9, 8, 8, 7])
>> np.sort(a.flatten())[::-1][:4] # slower method
array([9, 8, 8, 7])
>> # PROPOSED FEATURE: return INDICES of (ordered) top 4 values in array:
>> a.top_k(k=4, return_indices=True)
array([12,1,15,18])
>> # CURRENT METHOD: return INDICES of (ordered) top 4 values in array:
>> (-a.flatten()).argsort()[:4]
array([12,1,15,18])
>> # PROPOSED FEATURE: multidimensional examples:
>> a.top_k(k=3, axis=0)
array( [8,5,1], [8,6,4], [9,4,2], [7,3,1], [3,3,0] )
>> a.top_k(k=3, axis=1)
array( [8,5,3], [6,5,2], [9,4,3], [8,7,4] )
I'd also consider including functionality for bottom k values, and methods
for returning indices in the case of tied values (e.g. "first appearance",
"random" etc.).
Cheers
Joe
On Tue, 22 Feb 2022 at 15:30, Joseph Fox-Rabinovitz <
jfoxrabinov...@gmail.com> wrote:
> Joe,
>
> Could you show an example that you find inelegant and elaborate on how you
> intend to improve it? It's hard to discuss without more specific
> information.
>
> - Joe
>
> On Tue, Feb 22, 2022, 07:23 Joseph Bolton
> wrote:
>
>> Morning,
>>
>> My apologies if this deviates from the vision of numpy:
>>
>> I find myself often requiring the indices and/or values of the top (or
>> bottom) k items in a numpy array.
>>
>> I am aware of solutions involving partition/argpartition but these are
>> inelegant.
>>
>> I am thinking of 1-dimensional arrays, but this concept extends to an
>> arbitrary number of dimensions.
>>
>> Is this a feature that would benefit the numpy package? I am happy to
>> code it up.
>>
>> Thanks for your time!
>>
>> Best regards
>> Joe
>>
>>
>>
>>
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