On Thu, Sep 16, 2021 at 12:32 AM Nico Schlömer wrote:
>
> > I was playing with this though and was a little surprised to find
> > NumPy allows things like this:
> >
> > >>> a = np.array([1, 2, 3])
> > >>> a[:] = np.array([[[5, 6, 7]]])
> > >>> a
> > array([5, 6, 7])
>
> Thanks Aaron for this example! I hadn't seen this before, and indeed
> the suggested PR doesn't intercept this case. Perhaps that's something
> we should consider deprecating as well. I'll add a comment to the PR;
> let's take it from there.
Is there some valid use-case for this "reverse broadcasting"? It seems
to me like an error would be better. Clearly a[idx] = b should work if
a[idx] and b broadcast to a[idx].shape, but here the broadcast shape
is not the same as a[idx]. I would expect this to generally be a bug
in user code, but maybe I'm missing why this was implemented in the
first place.
The semantics are a bit odd. Clearly something like a[:] =
np.array([[[5, 6, 7], [6, 7, 8]]]) in the above example can't work,
even though they are also both broadcast compatible. So it only works
if they are broadcast compatible and the broadcasting of the lhs just
adds 1s to its shape (which get implicitly removed in the assignment
since slice assignment can't change an array's shape)?
Aaron Meurer
>
> Cheers,
> Nico
>
> On Thu, Sep 16, 2021 at 2:00 AM Aaron Meurer wrote:
> >
> > Presumably this also changes int(), bool(), and complex() in the same way.
> >
> > The array API standard (and numpy.array_api) only requires float(),
> > bool(), and int() (and soon complex()) for dimension 0 arrays (the
> > standard does not have scalars), in part because of this NumPy issue
> > https://data-apis.org/array-api/latest/API_specification/array_object.html#float-self.
> >
> > On Wed, Sep 15, 2021 at 6:18 AM Nico Schlömer
> > wrote:
> > >
> > > Hi everyone,
> > >
> > > This is seeking input on PR [1] which I've worked on with @eric-wieser
> > > and @seberg. It deprecates
> > > ```
> > > float(x)
> > > ```
> > > if `x` is an array of ndim > 0. (It works with all arrays of size 1
> > > right now.) This aligns the behavior of float() on ndarrays with
> > > float() on lists which already fails today. It also deprecates the
> > > implicit conversion to float in assignment expressions like
> > > ```
> > > a = np.array([1, 2, 3])
> > > a[0] = [5] # deprecated, should be a[0] = 5
> >
> > This already gives a ValueError in NumPy 1.21.1. Do you mean a[0] =
> > np.array([5]) is deprecated?
> >
> > I was playing with this though and was a little surprised to find
> > NumPy allows things like this:
> >
> > >>> a = np.array([1, 2, 3])
> > >>> a[:] = np.array([[[5, 6, 7]]])
> > >>> a
> > array([5, 6, 7])
> >
> > Array assignment allows some sort of reverse broadcasting? Given this
> > behavior, it seems to me that a[0] = np.array([5]) actually should
> > work. Or is the idea that this entire behavior would be deprecated?
> >
> > Aaron Meurer
> >
> > > ```
> > > In general, the PR makes numpy a tad bit stricter on how it treats
> > > scalars vs. single-item arrays.
> > >
> > > The change also prevents the #1 wrong usage of float(), namely for
> > > extracting the scalar value from an array. One should rather use
> > > `x[0]` or `x.item()` to that which doesn't convert the value to a
> > > Python float.
> > >
> > > To estimate the impact of the PR, I looked at major numpy dependents
> > > like matplotlib, scipy, pandas etc., and of course numpy itself.
> > > Except scipy, all projects were virtually clean to start with. Scipy
> > > needed some changes for all tests to pass without warning, and all of
> > > the changes were improvements. In particular, the deprecation
> > > motivates users to use actual scalars when scalars are needed, e.g.,
> > > in the case of scipy, as the return value of a goal functional.
> > >
> > > It'd be great if you could try the branch against your own project and
> > > let us know (here or in the PR) about and problems that you might
> > > have.
> > >
> > > Thanks!
> > > Nico
> > >
> > > [1] https://github.com/numpy/numpy/pull/10615
> > > [2] https://github.com/numpy/numpy/issues/10404
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