[Numpy-discussion] argmax() indexes to value

[Eric Wieser]

> On my system plain fancy indexing is fastest

Hardly surprising, since take_along_axis is doing that under the hood,
after constructing the index for you :)

https://github.com/numpy/numpy/blob/v1.17.0/numpy/lib/shape_base.py#L58-L172

I deliberately didn't expose the internal function that constructs the
slice, since leaving it private frees us to move those functions to c or in
the distant future gufuncs.


On Fri, Nov 1, 2019, 15:54 Allan Haldane <allanhaldane at gmail.com> wrote:

> my thought was to try `take` or `take_along_axis`:
>
>    ind = np.argmin(a, axis=1)
>    np.take_along_axis(a, ind[:,None], axis=1)
>
> But those functions tend to simply fall back to fancy indexing, and are
> pretty slow. On my system plain fancy indexing is fastest:
>
> >>> %timeit a[np.arange(N),ind]
> 1.58 µs ± 18.1 ns per loop
> >>> %timeit np.take_along_axis(a, ind[:,None], axis=1)
> 6.49 µs ± 57.3 ns per loop
> >>> %timeit np.min(a, axis=1)
> 9.51 µs ± 64.1 ns per loop
>
> Probably `take_along_axis` was designed with uses like yours in mind,
> but it is not very optimized.
>
> (I think numpy is lacking a category of efficient
> indexing/search/reduction functions, like 'findfirst', 'groupby',
> short-circuiting any_*/all_*/nonzero, the proposed oindex/vindex, better
> gufunc broadcasting. There is slow but gradual infrastructure work
> towards these, potentially).
>
> Cheers,
> Allan
>
> On 10/30/19 11:31 PM, Daniele Nicolodi wrote:
> > On 30/10/2019 19:10, Neal Becker wrote:
> >> max(axis=1)?
> >
> > Hi Neal,
> >
> > I should have been more precise in stating the problem. Getting the
> > values in the array for which I'm looking at the maxima is only one step
> > in a more complex piece of code for which I need the indexes along the
> > second axis of the array. I would like to avoid to have to iterate the
> > array more than once.
> >
> > Thank you!
> >
> > Cheers,
> > Dan
> >
> >
> >> On Wed, Oct 30, 2019, 7:33 PM Daniele Nicolodi <daniele at grinta.net
> >> <mailto:daniele at grinta.net>> wrote:
> >>
> >>     Hello,
> >>
> >>     this is a very basic question, but I cannot find a satisfying
> answer.
> >>     Assume a is a 2D array and that I get the index of the maximum value
> >>     along the second dimension:
> >>
> >>     i = a.argmax(axis=1)
> >>
> >>     Is there a better way to get the value of the maximum array entries
> >>     along the second axis other than:
> >>
> >>     v = a[np.arange(len(a)), i]
> >>
> >>     ??
> >>
> >>     Thank you.
> >>
> >>     Cheers,
> >>     Daniele
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