[Numpy-discussion] Do we want scalar casting to behave as it does at the moment?
Benjamin Root
ben.root@ou....
Mon Nov 12 23:13:17 CST 2012
On Monday, November 12, 2012, Matthew Brett wrote:
> Hi,
>
> On Mon, Nov 12, 2012 at 8:15 PM, Benjamin Root <ben.root@ou.edu> wrote:
> >
> >
> > On Monday, November 12, 2012, Olivier Delalleau wrote:
> >>
> >> 2012/11/12 Nathaniel Smith <njs@pobox.com>
> >>>
> >>> On Mon, Nov 12, 2012 at 8:54 PM, Matthew Brett <
> matthew.brett@gmail.com>
> >>> wrote:
> >>> > Hi,
> >>> >
> >>> > I wanted to check that everyone knows about and is happy with the
> >>> > scalar casting changes from 1.6.0.
> >>> >
> >>> > Specifically, the rules for (array, scalar) casting have changed such
> >>> > that the resulting dtype depends on the _value_ of the scalar.
> >>> >
> >>> > Mark W has documented these changes here:
> >>> >
> >>> > http://docs.scipy.org/doc/numpy/reference/ufuncs.html#casting-rules
> >>> >
> >>> >
> http://docs.scipy.org/doc/numpy/reference/generated/numpy.result_type.html
> >>> >
> >>> >
> http://docs.scipy.org/doc/numpy/reference/generated/numpy.promote_types.html
> >>> >
> >>> > Specifically, as of 1.6.0:
> >>> >
> >>> > In [19]: arr = np.array([1.], dtype=np.float32)
> >>> >
> >>> > In [20]: (arr + (2**16-1)).dtype
> >>> > Out[20]: dtype('float32')
> >>> >
> >>> > In [21]: (arr + (2**16)).dtype
> >>> > Out[21]: dtype('float64')
> >>> >
> >>> > In [25]: arr = np.array([1.], dtype=np.int8)
> >>> >
> >>> > In [26]: (arr + 127).dtype
> >>> > Out[26]: dtype('int8')
> >>> >
> >>> > In [27]: (arr + 128).dtype
> >>> > Out[27]: dtype('int16')
> >>> >
> >>> > There's discussion about the changes here:
> >>> >
> >>> >
> >>> >
> http://mail.scipy.org/pipermail/numpy-discussion/2011-September/058563.html
> >>> >
> http://mail.scipy.org/pipermail/numpy-discussion/2011-March/055156.html
> >>> >
> >>> >
> http://mail.scipy.org/pipermail/numpy-discussion/2012-February/060381.html
> >>> >
> >>> > It seems to me that this change is hard to explain, and does what you
> >>> > want only some of the time, making it a false friend.
> >>>
> >>> The old behaviour was that in these cases, the scalar was always cast
> >>> to the type of the array, right? So
> >>> np.array([1], dtype=np.int8) + 256
> >>> returned 1? Is that the behaviour you prefer?
> >>>
> >>> I agree that the 1.6 behaviour is surprising and somewhat
> >>> inconsistent. There are many places where you can get an overflow in
> >>> numpy, and in all the other cases we just let the overflow happen. And
> >>> in fact you can still get an overflow with arr + scalar operations, so
> >>> this doesn't really fix anything.
> >>>
> >>> I find the specific handling of unsigned -> signed and float32 ->
> >>> float64 upcasting confusing as well. (Sure, 2**16 isn't exactly
> >>> representable as a float32, but it doesn't *overflow*, it just gives
> >>> you 2.0**16... if I'm using float32 then I presumably don't care that
> >>> much about exact representability, so it's surprising that numpy is
> >>> working to enforce it, and definitely a separate decision from what to
> >>> do about overflow.)
> >>>
> >>> None of those threads seem to really get into the question of what the
> >>> best behaviour here *is*, though.
> >>>
> >>> Possibly the moWell, hold on though, I was asking earlier in the
> thread what we
> thought the behavior should be in 2.0 or maybe better put, sometime in
> the future.
>
> If we know what we think the best answer is, and we think the best
> answer is worth shooting for, then we can try to think of sensible
> ways of getting there.
>
> I guess that's what Nathaniel and Olivier were thinking of but they
> can correct me if I'm wrong...
>
> Cheers,
>
> Matthew
I am fine with migrating to better solutions (I have yet to decide on this
current situation, though), but whatever change is adopted must go through
a deprecation process, which was my point. Outright breaking of code as a
first step is the wrong choice, and I was merely nipping it in the bud.
Cheers!
Ben Root
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