I believe the main lessons to draw from this are just how incredibly important a complete test suite and staying on top of code reviews are. I'm of the opinion that any explicit design choice of this nature should be reflected in the test suite, so that if someone changes it years later, they get immediate feedback that they're breaking something important. NumPy has gradually increased its test suite coverage, and when I dealt with the type promotion subsystem, I added fairly extensive tests:
https://github.com/numpy/numpy/blob/master/numpy/core/tests/test_numeric.py#L345 Another subsystem which is in a similar state as what the type promotion subsystem was, is the subscript operator and how regular/fancy indexing work. What this means is that any attempt to improve it that doesn't coincide with the original intent years ago can easily break things that were originally intended without them being caught by a test. I believe this subsystem needs improvement, and the transition to new/improved code will probably be trickier to manage than for the dtype promotion case. Let's try to learn from the type promotion case as best we can, and use it to improve NumPy's process. I believe Charles and Ralph have been doing a great job of enforcing high standards in new NumPy code, and managing the release process in a way that has resulted in very few bugs and regressions in the release. Most of these quality standards are still informal, however, and it's probably a good idea to write them down in a canonical location. It will be especially helpful for newcomers, who can treat the standards as a checklist before submitting pull requests. Thanks, -Mark On Mon, Feb 13, 2012 at 7:11 PM, Travis Oliphant <tra...@continuum.io>wrote: > The problem is that these sorts of things take a while to emerge. The > original system was more consistent than I think you give it credit. What > you are seeing is that most people get NumPy from distributions and are > relying on us to keep things consistent. > > The scalar coercion rules were deterministic and based on the idea that a > scalar does not determine the output dtype unless it is of a different > kind. The new code changes that unfortunately. > > Another thing I noticed is that I thought that int16 <op> scalar float > would produce float32 originally. This seems to have changed, but I need > to check on an older version of NumPy. > > Changing the scalar coercion rules is an unfortunate substantial change in > semantics and should not have happened in the 1.X series. > > I understand you did not get a lot of feedback and spent a lot of time on > the code which we all appreciate. I worked to stay true to the Numeric > casting rules incorporating the changes to prevent scalar upcasting due to > the absence of single precision Numeric literals in Python. > > We will need to look in detail at what has changed. I will write a test > to do that. > > Thanks, > > Travis > > -- > Travis Oliphant > (on a mobile) > 512-826-7480 > > > On Feb 13, 2012, at 7:58 PM, Mark Wiebe <mwwi...@gmail.com> wrote: > > On Mon, Feb 13, 2012 at 5:00 PM, Travis Oliphant <tra...@continuum.io>wrote: > >> Hmmm. This seems like a regression. The scalar casting API was fairly >> intentional. >> >> What is the reason for the change? >> > > In order to make 1.6 ABI-compatible with 1.5, I basically had to rewrite > this subsystem. There were virtually no tests in the test suite specifying > what the expected behavior should be, and there were clear inconsistencies > where for example "a+b" could result in a different type than "b+a". I > recall there being some bugs in the tracker related to this as well, but I > don't remember those details. > > This change felt like an obvious extension of an existing behavior for > eliminating overflow, where the promotion changed unsigned -> signed based > on the value of the scalar. This change introduced minimal upcasting only > in a set of cases where an overflow was guaranteed to happen without that > upcasting. > > During the 1.6 beta period, I signaled that this subsystem had changed, as > the bullet point starting "The ufunc uses a more consistent algorithm for > loop selection.": > > http://mail.scipy.org/pipermail/numpy-discussion/2011-March/055156.html > > The behavior Matthew has observed is a direct result of how I designed the > minimization function mentioned in that bullet point, and the algorithm for > it is documented in the 'Notes' section of the result_type page: > > http://docs.scipy.org/doc/numpy/reference/generated/numpy.result_type.html > > Hopefully that explains it well enough. I made the change intentionally > and carefully, tested its impact on SciPy and other projects, and advocated > for it during the release cycle. > > Cheers, > Mark > > -- >> Travis Oliphant >> (on a mobile) >> 512-826-7480 >> >> >> On Feb 13, 2012, at 6:25 PM, Matthew Brett <matthew.br...@gmail.com> >> wrote: >> >> > Hi, >> > >> > I recently noticed a change in the upcasting rules in numpy 1.6.0 / >> > 1.6.1 and I just wanted to check it was intentional. >> > >> > For all versions of numpy I've tested, we have: >> > >> >>>> import numpy as np >> >>>> Adata = np.array([127], dtype=np.int8) >> >>>> Bdata = np.int16(127) >> >>>> (Adata + Bdata).dtype >> > dtype('int8') >> > >> > That is - adding an integer scalar of a larger dtype does not result >> > in upcasting of the output dtype, if the data in the scalar type fits >> > in the smaller. >> > >> > For numpy < 1.6.0 we have this: >> > >> >>>> Bdata = np.int16(128) >> >>>> (Adata + Bdata).dtype >> > dtype('int8') >> > >> > That is - even if the data in the scalar does not fit in the dtype of >> > the array to which it is being added, there is no upcasting. >> > >> > For numpy >= 1.6.0 we have this: >> > >> >>>> Bdata = np.int16(128) >> >>>> (Adata + Bdata).dtype >> > dtype('int16') >> > >> > There is upcasting... >> > >> > I can see why the numpy 1.6.0 way might be preferable but it is an API >> > change I suppose. >> > >> > Best, >> > >> > Matthew >> > _______________________________________________ >> > NumPy-Discussion mailing list >> > NumPy-Discussion@scipy.org >> > http://mail.scipy.org/mailman/listinfo/numpy-discussion >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
