[Numpy-discussion] Put type annotations in NumPy proper?

[Ralf Gommers]

On Tue, Mar 24, 2020 at 11:43 PM Joshua Wilson <josh.craig.wilson at gmail.com>
wrote:

> > That is, is this an all-or-nothing thing where as soon as we start,
> numpy-stubs becomes unusable?
>
> Until NumPy is made PEP 561 compatible by adding a `py.typed` file,
> type checkers will ignore the types in the repo, so in theory you can
> avoid the all or nothing. In practice it's maybe trickier because
> currently people can use the stubs, but they won't be able to use the
> types in the repo until the PEP 561 switch is flipped. So e.g.
> currently SciPy pulls the stubs from `numpy-stubs` master, allowing
> for a short
>
> find place where NumPy stubs are lacking -> improve stubs -> improve SciPy
> types
>
> loop. If all development moves into the main repo then SciPy is
> blocked on it becoming PEP 561 compatible before moving forward. But,
> you could complain that I put the cart before the horse with
> introducing typing in the SciPy repo before the NumPy types were more
> resolved, and that's probably a fair complaint.
>

I think it makes a lot of sense to add types to SciPy, and then improving
numpy-stubs the moment you are blocked on something. This allows for
finding relevant issues and iterating quickly.

Moving things into the main NumPy repo could be done right before branching
off 1.19.x, which is still a few months away. Until then there's probably
only downsides to moving it into the main repo - it's easier to depend on
master of numpy-stubs than on master of numpy in CI and dev workflows.


>
> > Anyone interested in taking the lead on this?
>
> Not that I am a core developer or anything, but I am interested in
> helping to improve typing in NumPy.
>

Thanks again Josh!

Cheers,
Ralf


> On Tue, Mar 24, 2020 at 11:15 AM Eric Wieser
> <wieser.eric+numpy at gmail.com> wrote:
> >
> > >  Putting
> > > aside ndarray, as more challenging, even annotations for numpy
> functions
> > > and method parameters with built-in types would help, as a start.
> >
> > This is a good idea in principle, but one thing concerns me.
> >
> > If we add type annotations to numpy, does it become an error to have
> numpy-stubs installed?
> > That is, is this an all-or-nothing thing where as soon as we start,
> numpy-stubs becomes unusable?
> >
> > Eric
> >
> > On Tue, 24 Mar 2020 at 17:28, Roman Yurchak <rth.yurchak at gmail.com>
> wrote:
> >>
> >> Thanks for re-starting this discussion, Stephan! I think there is
> >> definitely significant interest in this topic:
> >> https://github.com/numpy/numpy/issues/7370 is the issue with the
> largest
> >> number of user likes in the issue tracker (FWIW).
> >>
> >> Having them in numpy, as opposed to a separate numpy-stubs repository
> >> would indeed be ideal from a user perspective. When looking into it in
> >> the past, I was never sure how well in sync numpy-stubs was. Putting
> >> aside ndarray, as more challenging, even annotations for numpy functions
> >> and method parameters with built-in types would help, as a start.
> >>
> >> To add to the previously listed projects that would benefit from this,
> >> we are currently considering to start using some (minimal) type
> >> annotations in scikit-learn.
> >>
> >> --
> >> Roman Yurchak
> >>
> >> On 24/03/2020 18:00, Stephan Hoyer wrote:
> >> > When we started numpy-stubs [1] a few years ago, putting type
> >> > annotations in NumPy itself seemed premature. We still supported
> Python
> >> > 2, which meant that we would need to use awkward comments for type
> >> > annotations.
> >> >
> >> > Over the past few years, using type annotations has become
> increasingly
> >> > popular, even in the scientific Python stack. For example, off-hand I
> >> > know that at least SciPy, pandas and xarray have at least part of
> their
> >> > APIs type annotated. Even without annotations for shapes or dtypes, it
> >> > would be valuable to have near complete annotations for NumPy, the
> >> > project at the bottom of the scientific stack.
> >> >
> >> > Unfortunately, numpy-stubs never really took off. I can think of a few
> >> > reasons for that:
> >> > 1. Missing high level guidance on how to write type annotations,
> >> > particularly for how (or if) to annotate particularly dynamic parts of
> >> > NumPy (e.g., consider __array_function__), and whether we should
> >> > prioritize strictness or faithfulness [2].
> >> > 2. We didn't have a good experience for new contributors. Due to the
> >> > relatively low level of interest in the project, when a contributor
> >> > would occasionally drop in, I often didn't even notice their PR for a
> >> > few weeks.
> >> > 3. Developing type annotations separately from the main codebase makes
> >> > them a little harder to keep in sync. This means that type annotations
> >> > couldn't serve their typical purpose of self-documenting code. Part of
> >> > this may be necessary for NumPy (due to our use of C extensions), but
> >> > large parts of NumPy's user facing APIs are written in Python. We no
> >> > longer support Python 2, so at least we no longer need to worry about
> >> > putting annotations in comments.
> >> >
> >> > We eventually could probably use a formal NEP (or several) on how we
> >> > want to use type annotations in NumPy, but I think a good first step
> >> > would be to think about how to start moving the annotations from
> >> > numpy-stubs into numpy proper.
> >> >
> >> > Any thoughts? Anyone interested in taking the lead on this?
> >> >
> >> > Cheers,
> >> > Stephan
> >> >
> >> > [1] https://github.com/numpy/numpy-stubs
> >> > [2] https://github.com/numpy/numpy-stubs/issues/12
> >> >
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