[Numpy-discussion] Depreciating asfortranarray and ascontiguousarray

Travis Oliphant teoliphant at gmail.com
Sat Oct 27 12:00:58 EDT 2018


I agree with Number 2 and 4.

On Sat, Oct 27, 2018 at 12:38 AM Eric Wieser <wieser.eric+numpy at gmail.com>
wrote:

> 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
>>>
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