[Numpy-discussion] tighten up ufunc casting rule

Ralf Gommers ralf.gommers@googlemail....
Tue Jun 7 14:08:31 CDT 2011

On Tue, Jun 7, 2011 at 8:59 PM, Mark Wiebe <mwwiebe@gmail.com> wrote:

> On Tue, Jun 7, 2011 at 1:41 PM, Ralf Gommers <ralf.gommers@googlemail.com>wrote:
>> On Mon, Jun 6, 2011 at 6:56 PM, Mark Wiebe <mwwiebe@gmail.com> wrote:
>>> On Mon, Jun 6, 2011 at 10:30 AM, Mark Wiebe <mwwiebe@gmail.com> wrote:
>>>> On Sun, Jun 5, 2011 at 3:43 PM, Ralf Gommers <
>>>> ralf.gommers@googlemail.com> wrote:
>>>>> On Thu, Jun 2, 2011 at 10:12 PM, Mark Wiebe <mwwiebe@gmail.com> wrote:
>>>>>> On Thu, Jun 2, 2011 at 3:09 PM, Gael Varoquaux <
>>>>>> gael.varoquaux@normalesup.org> wrote:
>>>>>>> On Thu, Jun 02, 2011 at 03:06:58PM -0500, Mark Wiebe wrote:
>>>>>>> >    Would anyone object to, at least temporarily, tightening up the
>>>>>>> default
>>>>>>> >    ufunc casting rule to 'same_kind' in NumPy master? It's a one
>>>>>>> line change,
>>>>>>> >    so would be easy to undo, but such a change is very desirable in
>>>>>>> my
>>>>>>> >    opinion.
>>>>>>> >    This would raise an exception, since it's np.add(a, 1.9, out=a),
>>>>>>> >    converting a float to an int:
>>>>>>> >    >>> a = np.arange(3, dtype=np.int32)
>>>>>>> >    >>> a += 1.9
>>>>>>> That's probably going to break a huge amount of code which relies on
>>>>>>> the
>>>>>>> current behavior.
>>>>>>> Am I right in believing that this should only be considered for a
>>>>>>> major
>>>>>>> release of numpy, say numpy 2.0?
>>>>>> Absolutely, and that's why I'm proposing to do it in master now,
>>>>>> fairly early in a development cycle, so we can evaluate its effects. If the
>>>>>> next version is 1.7, we probably would roll it back for release (a 1 line
>>>>>> change), and if the next version is 2.0, we probably would keep it in.
>>>>>> I suspect at least some of the code relying on the current behavior
>>>>>> may have bugs, and tightening this up is a way to reveal them.
>>>>> Here are some results of testing your tighten_casting branch on a few
>>>>> projects - no need to first put it in master first to do that. Four failures
>>>>> in numpy, two in scipy, four in scikit-learn (plus two that don't look
>>>>> related), none in scikits.statsmodels. I didn't check how many of them are
>>>>> actual bugs.
>>>>> I'm not against trying out your change, but it would probably be good
>>>>> to do some more testing first and fix the issues found before putting it in.
>>>>> Then at least if people run into issues with the already tested packages,
>>>>> you can just tell them to update those to latest master.
>>>> Cool, thanks for running those. I already took a chunk out of the NumPy
>>>> failures. The ones_like function shouldn't really be a ufunc, but rather be
>>>> like zeros_like and empty_like, but that's probably not something to change
>>>> right now. The datetime-fixes type resolution change provides a mechanism to
>>>> fix that up pretty easily.
>>>> For Scipy, what do you think is the best way to resolve it? If NumPy 1.6
>>>> is the minimum version for the next scipy, I would add casting='unsafe' to
>>>> the failing sqrt call.
>> There's no reason to set the minimum required numpy to 1.6 AFAIK, and it's
>> definitely not desirable.
> Ok, I think there are two ways to resolve this kind of error. One would be
> to add a condition testing for a version >= 1.6, and setting
> casting='unsafe' when that occurs, and the other would be to insert a call
> to .astype() to force the type. The former is probably preferable, to avoid
> the unnecessary copy.
> Does numpy provide the version in tuple form, so a version comparison like
> this can be done easily? numpy.version doesn't seem to have one in it.
> No, just as a string. I think it's fine to do:

In [4]: np.version.short_version
Out[4]: '2.0.0'
In [5]: np.version.short_version > '1.5.1'
Out[5]: True

For a very convoluted version that produces a tuple see
parse_numpy_version() in scipy.pavement.py

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