[Numpy-discussion] Numpy 1.11.0b2 released

[Charles R Harris]

On Wed, Feb 3, 2016 at 10:18 PM, Nathaniel Smith <njs at pobox.com> wrote:

> On Tue, Feb 2, 2016 at 8:45 AM, Pauli Virtanen <pav at iki.fi> wrote:
> > 01.02.2016, 23:25, Ralf Gommers kirjoitti:
> > [clip]
> >> So: it would really help if someone could pick up the automation part of
> >> this and improve the stack testing, so the numpy release manager doesn't
> >> have to do this.
> >
> > quick hack: https://github.com/pv/testrig
> >
> > Not that I'm necessarily volunteering to maintain the setup, though, but
> > if it seems useful, move it under numpy org.
>
> That's pretty cool :-). I also was fiddling with a similar idea a bit,
> though much less fancy... my little script cheats and uses miniconda to
> fetch pre-built versions of some packages, and then runs the tests against
> numpy 1.10.2 (as shipped by anaconda) + the numpy master, and does a diff
> (with a bit of massaging to make things more readable, like summarizing
> warnings):
>
>     https://travis-ci.org/njsmith/numpy/builds/106865202
>
> Search for "#####" to jump between sections of the output.
>
> Some observations:
>
> testing* matplotlib* this way doesn't work, b/c they need special test
> data files that anaconda doesn't ship :-/
>
> *scipy*:
>    *one new failure*, in test_nanmedian_all_axis
>    250 calls to np.testing.rand (wtf), 92 calls to random_integers, 3 uses
> of datetime64 with timezones. And for some reason the new numpy gives more
> "invalid value encountered in greater"-type warnings.
>
> *astropy*:
>   *two weird failures* that hopefully some astropy person will look into;
> two spurious failures due to over-strict testing of warnings
>
> *scikit-learn*:
>   several* new failures:* 1 "invalid slice" (?), 2 "OverflowError: value
> too large to convert to int". No idea what's up with these. Hopefully some
> scikit-learn person will investigate?
>   2 np.ma view warnings, 16 multi-character strings used where "C" or "F"
> expected, 1514 (!!) calls to random_integers
>
> *pandas:*
>   zero new failures, only new warnings are about NaT, as expected. I guess
> their whole "running their tests against numpy master" thing works!
>
>
> *statsmodels:*
> *  absolute disaster*. *261 *new failures, I think mostly because of
> numpy getting pickier about float->int conversions. Also a few "invalid
> slice".
>   102 np.ma view warnings.
>
> I don't have a great sense of whether the statsmodels breakages are ones
> that will actually impact users, or if they're just like, 1 bad utility
> function that only gets used in the test suite. (well, probably not the
> latter, because they do have different tracebacks). If this is typical
> though then we may need to back those integer changes out and replace them
> by a really loud obnoxious warning for a release or two :-/ The other
> problem here is that statsmodels hasn't done a release since 2014 :-/
>

I'm going to do a second beta this weekend and will try putting it up on
pypi. The statsmodels are a concern, we may need to put off the transition
to integer only indexes. OTOH, if statsmodels can't fix things up we will
have to deal with that at some point. Apparently we also need to do
something about invisible deprecation warnings. Python changing the default
to ignore was, IIRC, due to a Python screw up in backporting PyCapsule to
2.7 and deprecating PyCObject in the process. The easiest way out of that
hole was painting it over.

Chuck
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