[Numpy-discussion] Allow __getitem__ to support custom objects

[Aaron Meurer]

On Fri, Oct 30, 2020 at 9:18 AM Sebastian Berg
<sebastian at sipsolutions.net> wrote:
>
> On Thu, 2020-10-29 at 23:58 -0600, Aaron Meurer wrote:
> > On Thu, Oct 29, 2020 at 6:09 PM Sebastian Berg
> > <sebastian at sipsolutions.net> wrote:
> > > On Tue, 2020-10-27 at 17:15 -0600, Aaron Meurer wrote:
> > > > For ndindex (https://quansight.github.io/ndindex/), the biggest
> > > > issue
> > > > with the API is that to use an ndindex object to actually index
> > > > an
> > > > array, you have to use a[idx.raw] instead of a[idx]. This is
> > > > because
> > > > for NumPy arrays, you cannot allow custom objects to be indices.
> > > > The
> > > > exception is objects that define __index__, but this only works
> > > > for
> > > > integer indices. If __index__ returns anything other than an
> > > > integer,
> > > > you get an IndexError. This is annoying because it's easy to
> > > > forget
> > > > to
> > > > do this when working with the ndindex API, and the error message
> > > > from
> > > > NumPy isn't informative about what went wrong unless you know to
> > > > expect it.
> > > >
> > > > I'd like to propose an API that would allow custom objects to
> > > > define
> > > > how they should be converted to a standard NumPy index, similar
> > > > to
> > > > __index__ but that supports all index types. I think there are
> > > > two
> > > > options here:
> > > >
> > > > - Allow __index__ to return any index type, not just integers.
> > > > This
> > > > is
> > > > the simplest because it reuses an existing API, and __index__ is
> > > > the
> > > > best possible name for this API. However, I'm not sure, but this
> > > > may
> > > > actually conflict with the text of PEP 357
> > > > (https://www.python.org/dev/peps/pep-0357/). Also, some other
> > > > APIs
> > > > use
> > > > __index__ to check if something is an indexable integer, which
> > > > wouldn't accept generic index. For example, elements of a slice
> > > > can
> > > > be
> > > > any object that defines __index__.
> > > >
> > >
> > > Index converts to an integer (safely).  There is an assumptions
> > > that
> > > the integer is good for indexing, but I the name shouldn't be taken
> > > to
> > > mean it is specific to indexing (even if that was the main
> > > motivation).
> > >
> > >
> > > > - Add a new __numpy_index__ API that works like
> > > >
> > > > def __numpy_index__(self):
> > > >     return <tuple, integer, slice, newaxis, ellipsis, or integer
> > > > or
> > > > boolean array>
> > > >
> > > > In NumPy, __getitem__ and __setitem__ on ndarray would first
> > > > check if
> > > > the input index type is one of the known types as it currently
> > > > does,
> > > > then it would try __index__, and if neither of those fails, it
> > > > would
> > > > call __numpy_index__(index) and use that.
> > >
> > > Do you anticipate just:
> > >
> > >     arr[index]
> > >
> > > or also:
> > >
> > >     arr[index1, index2]
> >
> > I think both should work. If the second one doesn't work it would be
> > surprising.
> >
> > > Would you expect pandas or array-like objects to support this as
> > > well?
> >
> > Yes, it would probably be best for array-like to also work with the
> > same API.
> >
> > I don't know much about Pandas. It seems like it already allows a lot
> > of indexing stuff. Do Series/Dataframe already have such an API?
>
> I do not think so, but indexing in pandas works differently often. So I
> was curious whether y
>
> >
> > > If we only do `arr[index]` might subclassing tuple be sufficient?
> >
> > I guess that technically works, except now your objects have to act
> > like a tuple, even if they represent something like a slice (Python
> > does not allow subclassing slice). For ndindex I've tried to make a
> > distinction between objects as representing indices and the built-in
> > objects that happen to be used to represent those indices by default.
> > So an ndindex.Tuple explicitly doesn't work like a Tuple, an
> > ndindex.Integer doesn't work like an int, and so on. That way there
> > is
> > a clear distinction between ndindex operations and operations on the
> > built-in types.
> >
> > > Do
> > > you have any thought on how this might play out with a potential
> > > `arr.oindex[...]`?
> >
> > I think oindex[idx] would call the same API on idx. I'm not sure if
> > it
> > matters that it's oindex, since that's at a higher level.
>
> It is at a higher level, but it seemed to me that `ndindex` largely
> plays at that level.  For example, you have a method to implement index
> chaining:
>
>     arr[idx1][idx2] == arr[idx1.as_subindex(idx2)]
>
> (or similar). But this will not work:
>
>     arr.oindex[idx1].oindex[idx2] != arr.idx[idx1.as_subindex(idx2)]

Just to be clear, this isn't how as_subindex works. as_subindex is
actually the inverse of composition (composition isn't implemented
yet). But I get the point anyway. Most of the ndindex API won't be
valid for oindex.  I haven't thought too much yet about how outer
indexing fits into ndindex, but it is something a lot of people seem
to be interested in.

>
> Also the "result" shape, or even questions like `.isempty()` will give
> different answers when  used as an `.oindex[...]`.
>
> This is why I though that `arr[idx1, idx2]` is possibly very different
> case from `arr[idx]` at least for current NumPy indexing logic (it
> would be better with `arr.oindex[]`).
> The difference doesn't matter in your proposal, but I had the
> impression that the `arr[idx1, idx2]` form might be rare/unused and
> that form would not be able to carry information such as whether this
> is supposed to be an "oindex".
>
> Maybe it helps to look back at `.oindex` to explain this. A possible
> solution to subclass handling if we add `arr.oindex` is to make it so
> that:
>
>     myarr.oindex[indx]
>
> could call:
>
>     myarr.__getitem__(indx_object)
>
> Where `index_object` knows that this is was an oindex.  The main reason
> is the expectation that many subclasses may implement `__getitem__`,
> but probably just do:
>
>     def __getitem__(self, indx):
>          new_data = self.data[indx]
>          # Do something with new_data.
>
> Now for `ndindex` it would seem to make a lot of sense to have an
> OIndex object, etc. for the same reason.
>
> Of course how we implement `.oindex` can be pretty separate from this.

Yeah, one of the ideas for the more general API is that you could have
a[oindex(idx)] where oindex() returns some object that does outer
indexing. If outer indices always map to normal indices, this could be
done with the simpler API (I haven't looked at it enough to say
whether that's the case or not yet).

>
> >
> > > Adding either to NumPy is probably fairly straight forward,
> > > although I
> > > prefer either not slow down every single indexing operation for an
> > > extremely niche use-case (which is likely possible) or timing that
> > > it
> > > is insignificant.
> >
> > I'm not sure it would. The current cases would all be tried first.
> > The
> > only time the new protocol would be used is when the index type isn't
> > one of the currently allowed types, which currently raises
> > IndexError.
> >
> > > What might help me is understanding that `ndindex` itself better.
> > > Since
> > > it seems like asking to add a protocol that may very well be used
> > > by
> > > only this one project?
> >
> > That's fair. Maybe the more general API would make more sense then? I
> > think it would need more thinking out, but it would allow a lot more
> > use-cases.
> >
>
> A general API might make sense, but I am edgy about reversing the roles
> of who performs the indexing. For one thing that probably would break
> subclassing and overriding of `__getitem__`?

Yeah it might. In Python, we have __getattr__, which lets A define how
A.x works, and __get__ (i.e., descriptors), which allows x to define
how A.x works. The whole thing is tied together by the higher level
__getattribute__, which defines the logic for both (as well as
__dict__ and all that other stuff). You should almost never override
__getattribute__ (unless you *really* know what you're doing).

I'm not sure what that says here. Maybe that __getattr__ shouldn't be
overridden by subclasses but rather some other NumPy specific method
that __getattr__ calls?

Aaron Meurer

>
>
> Cheers,
>
> Sebastian
>
>
>
> > Aaron Meurer
> >
> > > > Note: there is a more general way that NumPy arrays could allow
> > > > __getitem__ to be defined on custom objects, which I am NOT
> > > > proposing.
> > > > Instead of an API that returns one of the current predefined
> > > > index
> > > > types (tuple, integer, slice, newaxis, ellipsis, or integer or
> > > > boolean
> > > > array), there could instead be an API that takes the array as
> > > > input
> > > > and returns another array (or view) as an output. This would
> > > > allow an
> > > > object to define itself as an index in arbitrary ways, even if
> > > > such
> > > > an
> > > > index would not actually be possible via traditional indexing.
> > > > There
> > > > are definitely some interesting ideas that could be done with
> > > > this,
> > > > but this idea would be much more complicated, and isn't something
> > > > that
> > > > I need. Unless the community feels that a more general API like
> > > > this
> > > > would be preferred, I would suggest deferring something like it
> > > > to a
> > > > later discussion.
> > > >
> > > > What would be the best way to go about getting something like
> > > > this
> > > > implemented? Is it simple enough that we can just work out the
> > > > details
> > > > here and on a pull request, or should I write a NEP?
> > >
> > > A short NEP may make sense, at least if this is supposed to be a
> > > generic protocol for general array-likes, which I guess it would
> > > have
> > > to be ready for.
> > >
> > > Cheers,
> > >
> > > Sebastian
> > >
> > >
> > > > Aaron Meurer
> > > > _______________________________________________
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> > > > NumPy-Discussion at python.org
> > > > https://mail.python.org/mailman/listinfo/numpy-discussion
> > > >
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