[Numpy-discussion] Add an axis argument to generalized ufuncs?

Stephan Hoyer shoyer at gmail.com
Sun Oct 19 03:25:43 EDT 2014

On Sat, Oct 18, 2014 at 6:46 PM, Nathaniel Smith <njs at pobox.com> wrote:

> One thing we'll have to watch out for is that for reduction operations
> (which are basically gufuncs with (n)->() signatures), we already
> allow axis=(0,1) to mean "reshape axes 0 and 1 together into one big
> axis, and then use that as the gufunc core axis". I don't know if
> we'll ever want to support this functionality for gufuncs in general,
> but we shouldn't rule it out with the syntax.

This is a great point.

In fact, I think supporting this sort of functionality for gufuncs would be
quite valuable, since there are a plenty of reduction operations that can't
fit into the model provided by ufunc.reduce. An excellent example is
np.median, which currently can only act on either one axis or an entire
flattened array.

If the syntax (m?,n),(n,p?)->(m?,p?) is accepted, then I think the natural
extension to reduction operators that can act on one or more axes would be
(n+)->() (this is regex syntax).

Actually, adding using an axis keyword seems like the only elegant way to
handle disambiguating cases like this.

> One option would be to add a new argument axes=... for gufunc core
> specification, and say that axis=foo is an alias for axes=[[foo]].

Indeed, this is exactly what I was thinking. The "canonical form" for the
axis argument would be doubly nested tuples, but if an integer or unnested
tuple is encountered, additional nesting should be added until reaching
canoncial form, e.g., axis=0 -> axis=(0,) -> axis=((0,),).

The only particularly tricky case will be scenarios like my second one,
axis=(0, 1) for (n)(m)->() or (n,m)->(). To deal with cases like this, the
parsing will need to take the gufunc signature into consideration, and
start by asking whether or not tuple is of the right size to match each
function argument separately.

To make it clear that this proposal covers all the bases, I would be happy
to write some prototype code (and test cases) to demonstrate such a
transformation to canonical form, including all these edge cases.

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