[Numpy-discussion] Use-case for np.choose

josef.pktd@gmai... josef.pktd@gmai...
Sun Nov 8 21:23:44 CST 2009


On Sun, Nov 8, 2009 at 10:03 PM, David Goldsmith
<d.l.goldsmith@gmail.com> wrote:
> OK, now I'm trying to wrap my brain around broadcasting in choose when both
> `a` *and* `choices` need to be (non-trivially) broadcast in order to arrive
> at a common shape, e.g.:
>
>>>> c=np.arange(4).reshape((2,1,2)) # shape is (2,1,2)
>>>> a=np.eye(2, dtype=int) # shape is (2,2)
>>>> np.choose(a,c)
> array([[2, 1],
>        [0, 3]])
>
> (Unfortunately, the implementation is in C, so I can't easily insert print
> statements to see intermediate results.)
>
> First, let me confirm that the above is indeed an example of what I think it
> is, i.e., both `a` and `choices` are broadcast in order for this to work,
> correct?  (And if incorrect, how is one broadcast to the shape of the
> other?)  Second, both are broadcast to shape (2,2,2), correct?

Not,

You only have one choice array, so first it is interpreted as a
sequence with c[0], c[1], then c[0] and c[0] are broadcasted to each
other (1,2), then they are broadcasted to a (2,2), then a picks from

0,0
1,1

or
2,2
3,3

which is
2,0
1,3

qed

Josef

>>> np.choose(a,[c[0],c[1]])
array([[2, 1],
       [0, 3]])
>>> c[0]
array([[0, 1]])
>>> c[1]
array([[2, 3]])
>>> c[1].shape
(1, 2)
>>> c[0].shape
(1, 2)
>>> a
array([[1, 0],
       [0, 1]])


But how,
> precisely, i.e., does c become
>
> [[[0, 1], [2, 3]],        [[[0, 1], [0, 1]],
>  [[0, 1], [2, 3]]]   or   [[2, 3], [2, 3]]]
>
> and same question for a?  Then, once a is broadcast to a (2,2,2) shape,
> precisely how does it "pick and choose" from c to create a (2,2) result?
> For example, suppose a is broadcast to:
>
> [[[1, 0], [0, 1]],
>  [[1, 0], [0, 1]]]
>
> (as indicated above, I'm uncertain at this point if this is indeed what a is
> broadcast to); how does this create the (2,2) result obtained above?
> (Obviously this depends in part on precisely how c is broadcast, I do
> recognize that much.)
>
> Finally, a seemingly relevant comment in the C source is:
>
> /* Broadcast all arrays to each other, index array at the end.*/
>
> This would appear to confirm that "co-broadcasting" is performed if
> necessary, but what does the "index array at the end" phrase mean?
>
> Thanks for your continued patience and tutelage.
>
> DG
>
> On Sun, Nov 8, 2009 at 5:36 AM, <josef.pktd@gmail.com> wrote:
>>
>> On Sun, Nov 8, 2009 at 5:00 AM, David Goldsmith <d.l.goldsmith@gmail.com>
>> wrote:
>> > On Sun, Nov 8, 2009 at 12:57 AM, Anne Archibald
>> > <peridot.faceted@gmail.com>
>> > wrote:
>> >>
>> >> 2009/11/8 David Goldsmith <d.l.goldsmith@gmail.com>:
>> >> > On Sat, Nov 7, 2009 at 11:59 PM, Anne Archibald
>> >> > <peridot.faceted@gmail.com>
>> >> > wrote:
>> >> >>
>> >> >> 2009/11/7 David Goldsmith <d.l.goldsmith@gmail.com>:
>> >> >> > So in essence, at least as it presently functions, the shape of
>> >> >> > 'a'
>> >> >> > *defines* what the individual choices are within 'choices`, and if
>> >> >> > 'choices'
>> >> >> > can't be parsed into an integer number of such individual choices,
>> >> >> > that's
>> >> >> > when an exception is raised?
>> >> >>
>> >> >> Um, I don't think so.
>> >> >>
>> >> >> Think of it this way: you provide np.choose with a selector array,
>> >> >> a,
>> >> >> and a list (not array!) [c0, c1, ..., cM] of choices. You construct
>> >> >> an
>> >> >> output array, say r, the same shape as a (no matter how many
>> >> >> dimensions it has).
>> >> >
>> >> > Except that I haven't yet seen a working example with 'a' greater
>> >> > than
>> >> > 1-D,
>> >> > Josef's last two examples notwithstanding; or is that what you're
>> >> > saying
>> >> > is
>> >> > the bug.
>> >>
>> >> There's nothing magic about A being one-dimensional.
>> >>
>> >> C = np.random.randn(2,3,5)
>> >> A = (C>-1).astype(int) + (C>0).astype(int) + (C>1).astype(int)
>> >>
>> >> R = np.choose(A, (-1, -C, C, 1))
>> >
>> > OK, now I get it: np.choose(A[0,:,:], (-1,-C,C,-1)) and
>> > np.choose(A[0,:,0].reshape((3,1)), (-1,-C,C,1)), e.g., also work, but
>> > np.choose(A[0,:,0], (-1,-C,C,-1)) doesn't - what's necessary for
>> > choose's
>> > arguments is that both can be broadcast to a common shape (as you state
>> > below), but choose won't reshape the arguments for you to make this
>> > possible, you have to do so yourself first, if necessary.  That does
>> > appear
>> > to be what's happening now; but do we want choose to be smarter than
>> > that
>> > (e.g., for np.choose(A[0,:,0], (-1,-C,C,-1)) to work, so that the user
>> > doesn't need to include the .reshape((3,1)))?
>>
>> No, I don't think we want to be that smart.
>>
>> If standard broadcasting rules apply, as I think they do, then I wouldn't
>> want
>> any special newaxis or reshapes done automatically. It will be confusing,
>> the function wouldn't know what to do if there are, e.g., as many rows as
>> columns, and this looks like a big source of errors.
>> Standard broadcasting is pretty nice (once I got the hang of it), and
>> adding
>> a lot of np.newaxis (or some reshapes) to the code is only a small price
>> to pay.
>>
>> Josef
>>
>>
>>
>> >
>> > DG
>> >
>> >>
>> >> Requv = np.minimum(np.abs(C),1)
>> >>
>> >> or:
>> >>
>> >> def wedge(*functions):
>> >>     """Return a function whose value is the minimum of those of
>> >> functions"""
>> >>     def wedgef(X):
>> >>          fXs = [f(X) for f in functions]
>> >>          A = np.argmin(fXs, axis=0)
>> >>          return np.choose(A,fXs)
>> >>     return wedgef
>> >>
>> >> so e.g. np.abs is -wedge(lambda X: X, lambda X: -X)
>> >>
>> >> This works no matter what shape of X the user supplies - so a wedged
>> >> function can be somewhat ufunclike - by making A the same shape.
>> >>
>> >> >> The (i0, i1, ..., iN) element of the output array
>> >> >> is obtained by looking at the (i0, i1, ..., iN) element of a, which
>> >> >> should be an integer no larger than M; say j. Then r[i0, i1, ...,
>> >> >> iN]
>> >> >> = cj[i0, i1, ..., iN]. That is, each element of the selector array
>> >> >> determines which of the choice arrays to pull the corresponding
>> >> >> element from.
>> >> >
>> >> > That's pretty clear (thanks for doing my work for me). ;-), Yet, see
>> >> > above.
>> >> >
>> >> >> For example, suppose that you are processing an array C, and have
>> >> >> constructed a selector array A the same shape as C in which a value
>> >> >> is
>> >> >> 0, 1, or 2 depending on whether the C value is too small, okay, or
>> >> >> too
>> >> >> big respectively. Then you might do something like:
>> >> >>
>> >> >> C = np.choose(A, [-inf, C, inf])
>> >> >>
>> >> >> This is something you might want to do no matter what shape A and C
>> >> >> have. It's important not to require that the choices be an array of
>> >> >> choices, because they often have quite different shapes (here, two
>> >> >> are
>> >> >> scalars) and it would be wasteful to broadcast them up to the same
>> >> >> shape as C, just to stack them.
>> >> >
>> >> > OK, that's a pretty generic use-case, thanks; let me see if I
>> >> > understand
>> >> > it
>> >> > correctly: A is some how created independently with a 0 everywhere C
>> >> > is
>> >> > too
>> >> > small, a 1 everywhere C is OK, and a 2 everywhere C is too big; then
>> >> > np.choose(A, [-inf, C, inf]) creates an array that is -inf everywhere
>> >> > C
>> >> > is
>> >> > too small, inf everywhere C is too large, and C otherwise (and since
>> >> > -inf
>> >> > and inf are scalars, this implies broadcasting of these is taking
>> >> > place).
>> >> > This is what you're asserting *should* be the behavior.  So, unless
>> >> > there is
>> >> > disagreement about this (you yourself said the opposite viewpoint
>> >> > might
>> >> > rationally be held) np.choose definitely presently has a bug, namely,
>> >> > the
>> >> > index array can't be of arbitrary shape.
>> >>
>> >> There seems to be some disagreement between versions, but both Josef
>> >> and I find that the index array *can* be arbitrary shape. In numpy
>> >> 1.2.1 I find that all the choose items must be the same shape as it,
>> >> which I think is a bug.
>> >>
>> >> What I suggested might be okay was if the index array was not
>> >> broadcasted, so that the outputs always had exactly the same shape as
>> >> the index array. But upon reflection it's useful to be able to use a
>> >> 1-d array to select rows from a set of matrices, so I now think that
>> >> all of A and the elements of choose should be broadcast to the same
>> >> shape. This seems to be what Josef observes in his version of numpy,
>> >> so maybe there's nothing to do.
>> >>
>> >> Anne
>> >>
>> >> > DG
>> >> >
>> >> >>
>> >> >> Anne
>> >> >> _______________________________________________
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>> >> >
>> >> >
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