[Numpy-discussion] Depreciating asfortranarray and ascontiguousarray

Sat Oct 27 01:36:43 EDT 2018

in order to be used prior to calling C or Fortran code that expected at
least a 1-d array

I’d argue that the behavior for these functions should have just been to
raise an error saying “this function does not support 0d arrays”, rather
than silently inserting extra dimensions. As a bonus, that would push the
function developers to add support for 0d. Obviously we can’t make it do
that now, but what we can do is have it emit a warning in those cases.

I think our options are:

   1. Deprecate the entire function
   2. Deprecate and eventually(?) throw an error upon calling the function
   on 0d arrays, with a message like *“in future using ascontiguousarray to
   promote 0d arrays to 1d arrays will not be supported. If promotion is
   intentional, use ascontiguousarray(atleast1d(x)) to silence this warning
   and keep the old behavior, and if not use asarray(x, order='C') to preserve
   0d arrays”*
   3. Deprecate (future-warning) when passed 0d arrays, and eventually skip
   the upcast to 1d.
   If the calling code really needed a 1d array, then it will probably
   fail, which is not really different to 2, but has the advantage that the
   names are less surprising.
   4. Only improve the documentation

My preference would be 3

Eric

On Fri, 26 Oct 2018 at 17:35 Travis Oliphant <teoliphant at gmail.com> wrote:

On Fri, Oct 26, 2018 at 7:14 PM Alex Rogozhnikov <alex.rogozhnikov at yandex.ru>
> wrote:
>
>> > If the desire is to shrink the API of NumPy, I could see that.
>>
>> Very good desire, but my goal was different.
>>
>
>> > For some users this is exactly what is wanted.
>>
>> Maybe so, but I didn't face such example (and nobody mentioned those so
>> far in the discussion).
>> The opposite (according to the issue) happened. Mxnet example is
>> sufficient in my opinion.
>>
>
> I agree that the old motivation of APIs that would make it easy to create
> SciPy is no longer a major motivation for most users and even developers
> and so these reasons would not be very present (as well as why it wasn't
> even mentioned in the documentation).
>
>
>> Simple example:
>> x = np.zeros([])
>> assert(x.flags.c_contiguous)
>> assert(np.ascontiguousarray(x).shape == x.shape)
>>
>> Behavior contradicts to documentation (shape is changed) and to name
>> (flags are saying - it is already c_contiguous)
>>
>> If you insist, that keeping ndmin=1 is important (I am not yet convinced,
>> but I am ready to believe your autority),
>> we can add ndmin=1 to functions' signatures, this way explicitly
>> notifying users about expected dimension.
>>
>
> I understand the lack of being convinced.  This is ultimately a problem of
> 0-d arrays not being fully embraced and accepted by the Numeric community
> originally (which NumPy inherited during the early days).   Is there a way
> to document functions that will be removed on a major version increase
> which don't print warnings on use? I would support this.
>
> I'm a big supporter of making a NumPy 2.0 and have been for several years.
> Now that Python 3 transition has happened, I think we could seriously
> discuss this.  I'm trying to raise funding for maintenance and progress for
> NumPy and SciPy right now via Quansight Labs http://www.quansight.com/labs
> and I hope to be able to help find grants to support the wonderful efforts
> that have been happening for some time.
>
> While I'm thrilled and impressed by the number of amazing devs who have
> kept NumPy and SciPy going in mostly their spare time, it has created
> challenges that we have not had continuous maintenance funding to allow
> continuous paid development so that several people who know about the early
> decisions could not be retained to spend time on helping the transition.
>
> Your bringing the problem of mxnet devs is most appreciated.  I will make
> a documentation PR.
>
> -Travis
>
>
>
>
>> Alex.
>>
>>
>> 27.10.2018, 02:27, "Travis Oliphant" <teoliphant at gmail.com>:
>>
>> What is the justification for deprecation exactly?  These functions have
>> been well documented and have had the intended behavior of producing arrays
>> with dimension at least 1 for some time.  Why is it unexpected to produce
>> arrays of at least 1 dimension?  For some users this is exactly what is
>> wanted.  I don't understand the statement that behavior with 0-d arrays is
>> unexpected.
>>
>> If the desire is to shrink the API of NumPy, I could see that.   But, it
>> seems odd to me to remove a much-used function with an established behavior
>> except as part of a wider API-shrinkage effort.
>>
>> 0-d arrays in NumPy are a separate conversation.  At this point, I think
>> it was a mistake not to embrace 0-d arrays in NumPy from day one.  In some
>> sense 0-d arrays *are* scalars at least conceptually and for JIT-producing
>> systems that exist now and will be growing in the future, they can be
>> equivalent to scalars.
>>
>> The array scalars should become how you define what is *in* a NumPy array
>> making them true Python types, rather than Python 1-style "instances" of a
>> single "Dtype" object.  You would then have 0-d arrays and these Python
>> "memory" types describing what is *in* the array.
>>
>> There is a clear way to do this, some of which has been outlined by
>> Nathaniel, and the rest I have an outline for how to implement.  I can
>> advise someone on how to do this.
>>
>> -Travis
>>
>>
>>
>>
>> On Thu, Oct 25, 2018 at 3:17 PM Alex Rogozhnikov <
>> alex.rogozhnikov at yandex.ru> wrote:
>>
>> Dear numpy community,
>>
>> I'm planning to depreciate np.asfortranarray and np.ascontiguousarray
>> functions due to their misbehavior on scalar (0-D tensors) with PR #12244
>> .
>>
>> Current behavior (converting scalars to 1-d array with single element)
>> - is unexpected and contradicts to documentation
>> - probably, can't be changed without breaking external code
>> - I believe, this was a cause for poor support of 0-d arrays in mxnet.
>> - both functions are easily replaced with asarray(..., order='...'),
>> which has expected behavior
>>
>> There is no timeline for removal - we just need to discourage from using
>> this functions in new code.
>>
>> Function naming may be related to how numpy treats 0-d tensors specially,
>>
>> and those probably should not be called arrays.
>> https://www.numpy.org/neps/nep-0027-zero-rank-arrarys.html
>> However, as a user I never thought about 0-d arrays being special and
>> being "not arrays".
>>
>>
>> Please see original discussion at github for more details
>> https://github.com/numpy/numpy/issues/5300
>>
>> Your comments welcome,
>> Alex Rogozhnikov
>>
>> _______________________________________________
>> NumPy-Discussion mailing list
>> NumPy-Discussion at python.org
>> https://mail.python.org/mailman/listinfo/numpy-discussion
>>
>> ,
>>
>> _______________________________________________
>> NumPy-Discussion mailing list
>> NumPy-Discussion at python.org
>> https://mail.python.org/mailman/listinfo/numpy-discussion
>>
>> _______________________________________________
>> NumPy-Discussion mailing list
>> NumPy-Discussion at python.org
>> https://mail.python.org/mailman/listinfo/numpy-discussion
>>
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at python.org
> https://mail.python.org/mailman/listinfo/numpy-discussion
>

-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/numpy-discussion/attachments/20181026/3ecde7ce/attachment-0001.html>