[Numpy-discussion] broadcasting behavior for 1.6 (was: Numpy 1.6 schedule)
Charles R Harris
Fri Mar 11 08:42:32 CST 2011
On Fri, Mar 11, 2011 at 2:01 AM, Ralf Gommers
> I'm just going through the very long 1.6 schedule thread to see what
> is still on the TODO list before a 1.6.x branch can be made. So I'll
> send a few separate mails, one for each topic.
> On Mon, Mar 7, 2011 at 8:30 PM, Francesc Alted <email@example.com>
> > A Sunday 06 March 2011 06:47:34 Mark Wiebe escrigué:
> >> I think it's ok to revert this behavior for backwards compatibility,
> >> but believe it's an inconsistent and unintuitive choice. In
> >> broadcasting, there are two operations, growing a dimension 1 -> n,
> >> and appending a new 1 dimension to the left. The behaviour under
> >> discussion in assignment is different from normal broadcasting in
> >> that only the second one is permitted. It is broadcasting the output
> >> to the input, rather than broadcasting the input to the output.
> >> Suppose a has shape (20,), b has shape (1,20), and c has shape
> >> (20,1). Then a+b has shape (1,20), a+c has shape (20,20), and b+c
> >> has shape (20,20).
> >> If we do "b[...] = a", a will be broadcast to match b by adding a 1
> >> dimension to the left. This is reasonable and consistent with
> >> addition.
> >> If we do "a[...]=b", under 1.5 rules, a will once again be broadcast
> >> to match b by adding a 1 dimension to the left.
> >> If we do "a[...]=c", we could broadcast both a and c together to the
> >> shape (20,20). This results in multiple assignments to each element
> >> of a, which is inconsistent. This is not analogous to a+c, but
> >> rather to np.add(c, c, out=a).
> >> The distinction is subtle, but the inconsistent behavior is harmless
> >> enough for assignment that keeping backwards compatibility seems
> >> reasonable.
> > For what is worth, I also like the behaviour that Mark proposes, and
> > have updated tables test suite to adapt to this. But I'm fine if it is
> > decided to revert to the previous behaviour.
> The conclusion on this topic, as I read the discussion, is that we
> need to keep backwards compatible behavior (even though the proposed
> change is more intuitive). Has backwards compatibility been fixed
I don't think an official conclusion was reached, at least in so far as
numpy has an official anything ;) But this change does show up as an error
in one of the pandas tests, so it is likely to affect other folks as well.
Probably the route of least compatibility hassle is to revert to the old
behavior and maybe switch to the new behavior, which I prefer, for 2.0.
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