[Numpy-discussion] Added atleast_nd, request for clarification/cleanup of atleast_3d

[Nathaniel Smith]

On Jul 5, 2016 11:21 PM, "Ralf Gommers" <ralf.gommers at gmail.com> wrote:
>
>
>
> On Wed, Jul 6, 2016 at 7:06 AM, Nathaniel Smith <njs at pobox.com> wrote:
>
>> On Jul 5, 2016 9:09 PM, "Joseph Fox-Rabinovitz" <jfoxrabinovitz at gmail.com>
wrote:
>> >
>> > Hi,
>> >
>> > I have generalized np.atleast_1d, np.atleast_2d, np.atleast_3d with a
>> > function np.atleast_nd in PR#7804
>> > (https://github.com/numpy/numpy/pull/7804).
>> >
>> > As a result of this PR, I have a couple of questions about
>> > `np.atleast_3d`. `np.atleast_3d` appears to do something weird with
>> > the dimensions: If the input is 1D, it prepends and appends a size-1
>> > dimension. If the input is 2D, it appends a size-1 dimension. This is
>> > inconsistent with `np.atleast_2d`, which always prepends (as does
>> > `np.atleast_nd`).
>> >
>> >   - Is there any reason for this behavior?
>> >   - Can it be cleaned up (e.g., by reimplementing `np.atleast_3d` in
>> > terms of `np.atleast_nd`, which is actually much simpler)? This would
>> > be a slight API change since the output would not be exactly the same.
>>
>> Changing atleast_3d seems likely to break a bunch of stuff...
>>
>> Beyond that, I find it hard to have an opinion about the best design for
these functions, because I don't think I've ever encountered a situation
where they were actually what I wanted. I'm not a big fan of coercing
dimensions in the first place, for the usual "refuse to guess" reasons. And
then generally if I do want to coerce an array to another dimension, then I
have some opinion about where the new dimensions should go, and/or I have
some opinion about the minimum acceptable starting dimension, and/or I have
a maximum dimension in mind. (E.g. "coerce 1d inputs into a column matrix;
0d or 3d inputs are an error" -- atleast_2d is zero-for-three on that
requirements list.)
>>
>> I don't know how typical I am in this. But it does make me wonder if the
atleast_* functions act as an attractive nuisance, where new users take
their presence as an implicit recommendation that they are actually a
useful thing to reach for, even though they... aren't that. And maybe we
should be recommending folk move away from them rather than trying to
extend them further?
>>
>> Or maybe they're totally useful and I'm just missing it. What's your use
case that motivates atleast_nd?
>
> I think you're just missing it:) atleast_1d/2d are used quite a bit in
Scipy and Statsmodels (those are the only ones I checked), and in the large
majority of cases it's the best thing to use there. There's a bunch of
atleast_2d calls with a transpose appended because the input needs to be
treated as columns instead of rows, but that's still efficient and readable
enough.

I know people *use* it :-). What I'm confused about is in what situations
you would invent it if it didn't exist. Can you point me to an example or
two where it's "the best thing"? I actually had statsmodels in mind with my
example of wanting the semantics "coerce 1d inputs into a column matrix; 0d
or 3d inputs are an error". I'm surprised if there are places where you
really want 0d arrays converted into 1x1, or want to allow high dimensional
arrays to pass through - and if you do want to allow high dimensional
arrays to pass through, then transposing might help with 2d cases but will
silently mangle high-d cases, right?

-n
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