[Numpy-discussion] un-silencing Numpy's deprecation warnings

Dag Sverre Seljebotn d.s.seljebotn at astro.uio.no
Tue May 22 06:14:56 EDT 2012


On 05/22/2012 12:06 PM, Robert Kern wrote:
> On Tue, May 22, 2012 at 9:27 AM, Nathaniel Smith<njs at pobox.com>  wrote:
>> So starting in Python 2.7 and 3.2, the Python developers have made
>> DeprecationWarnings invisible by default:
>>   http://docs.python.org/whatsnew/2.7.html#the-future-for-python-2-x
>>   http://mail.python.org/pipermail/stdlib-sig/2009-November/000789.html
>>   http://bugs.python.org/issue7319
>> The only way to see them is to explicitly request them by running
>> Python with -Wd.
>>
>> The logic seems to be that between the end-of-development for 2.7 and
>> the moratorium on 3.2 changes, there were a *lot* of added
>> deprecations that were annoying people, and deprecations in the Python
>> stdlib mean "this code is probably sub-optimal but it will still
>> continue to work indefinitely".
>
> That's not quite it, I think, since this change was also made in
> Python 3.2 and will remain for all future versions of Python.
> DeprecationWarning *is* used for things that will definitely be going
> away, not just things that are no longer recommended but will continue
> to live. Note that the 3.2 moratorium was for changes to the language
> proper. The point was to encourage stdlib development, including the
> removal of deprecated code. It was not a moratorium on removing
> deprecated things. The silencing discussion just came up first in a
> discussion on the moratorium.
>
> The main problem they were running into was that the people who saw
> these warnings the most were not people directly using the deprecated
> features; they were users of packages written by third parties that
> used the deprecated features. Those people can't do anything to fix
> the problem, and many of them think that something is broken when they
> see the warning (I don't know why people do this, but they do). This
> problem is exacerbated by the standard library's position as a
> standard library. It's at the base of everyone's stack so these
> indirect effects are quite frequent, quite possibly the majority case.
> Users would use a newer version of Python library than the third party
> developer tested on and see these errors instead of the developer.
>
> I think this concern is fairly general and applies to numpy nearly as
> much as it does the standard library. It is at the bottom of many
> people's stacks. Someone calling matplotlib.pyplot.plot() should not
> see a DeprecationWarning from numpy.
>
>> So they consider that deprecation
>> warnings are like a lint tool for conscientious developers who
>> remember to test their code with -Wd, but not something to bother
>> users with.
>>
>> In Numpy, the majority of our users are actually (relatively
>> unsophisticated) developers,
>
> Whether they sometimes wear a developer hat or not isn't the relevant
> distinction. The question to ask is, "Are they the ones writing the
> code that directly uses the deprecated features?"
>
>> and we don't plan to support deprecated
>> features indefinitely.
>
> Again, this is not relevant. The silencing of DeprecationWarnings was
> not driven by this.
>
>> Our deprecations seem to better match what
>> Python calls a "FutureWarning": "warnings about constructs that will
>> change semantically in the future."
>>   http://docs.python.org/library/warnings.html#warning-categories
>> FutureWarning is displayed by default, and available in all versions of Python.
>>
>> So maybe we should change all our DeprecationWarnings into
>> FutureWarnings (or at least the ones that we actually plan to follow
>> through on). Thoughts?
>
> Using FutureWarning for deprecated functions (i.e. functions that will
> disappear in future releases) is an abuse of the semantics.
> FutureWarning is for things like the numpy.histogram() changes from a
> few years ago: changes in default arguments that will change the
> semantics of a given function call. Some of our DeprecationWarnings
> possibly should be FutureWarnings, but most shouldn't I don't think.

I guess the diagonal() change would at least be a FutureWarning then? 
(When you write to the result?)

Dag

>
> I can see a case being made for using a custom non-silenced exception
> for some cases that really probably show up mostly in true end-user
> scenarios, e.g. genfromtxt(). But there are many other cases where we
> should continue to use DeprecationWarning, e.g. _array2string(). But
> on the whole, I would just leave the DeprecationWarnings as they are.
>




More information about the NumPy-Discussion mailing list