[Numpy-discussion] Regression: in-place operations (possibly intentional)
Eric Firing
efiring@hawaii....
Tue Sep 18 14:55:16 CDT 2012
On 2012/09/18 9:25 AM, Charles R Harris wrote:
>
>
> On Tue, Sep 18, 2012 at 1:13 PM, Benjamin Root <ben.root@ou.edu
> <mailto:ben.root@ou.edu>> wrote:
>
>
>
> On Tue, Sep 18, 2012 at 2:47 PM, Charles R Harris
> <charlesr.harris@gmail.com <mailto:charlesr.harris@gmail.com>> wrote:
>
>
>
> On Tue, Sep 18, 2012 at 11:39 AM, Benjamin Root <ben.root@ou.edu
> <mailto:ben.root@ou.edu>> wrote:
>
>
>
> On Mon, Sep 17, 2012 at 9:33 PM, Charles R Harris
> <charlesr.harris@gmail.com
> <mailto:charlesr.harris@gmail.com>> wrote:
>
>
>
> On Mon, Sep 17, 2012 at 3:40 PM, Travis Oliphant
> <travis@continuum.io <mailto:travis@continuum.io>> wrote:
>
>
> On Sep 17, 2012, at 8:42 AM, Benjamin Root wrote:
>
> > Consider the following code:
> >
> > import numpy as np
> > a = np.array([1, 2, 3, 4, 5], dtype=np.int16)
> > a *= float(255) / 15
> >
> > In v1.6.x, this yields:
> > array([17, 34, 51, 68, 85], dtype=int16)
> >
> > But in master, this throws an exception about
> failing to cast via same_kind.
> >
> > Note that numpy was smart about this operation
> before, consider:
> > a = np.array([1, 2, 3, 4, 5], dtype=np.int16)
> > a *= float(128) / 256
>
> > yields:
> > array([0, 1, 1, 2, 2], dtype=int16)
> >
> > Of course, this is different than if one does it
> in a non-in-place manner:
> > np.array([1, 2, 3, 4, 5], dtype=np.int16) * 0.5
> >
> > which yields an array with floating point dtype
> in both versions. I can appreciate the arguments
> for preventing this kind of implicit casting between
> non-same_kind dtypes, but I argue that because the
> operation is in-place, then I (as the programmer) am
> explicitly stating that I desire to utilize the
> current array to store the results of the operation,
> dtype and all. Obviously, we can't completely turn
> off this rule (for example, an in-place addition
> between integer array and a datetime64 makes no
> sense), but surely there is some sort of happy
> medium that would allow these sort of operations to
> take place?
> >
> > Lastly, if it is determined that it is desirable
> to allow in-place operations to continue working
> like they have before, I would like to see such a
> fix in v1.7 because if it isn't in 1.7, then other
> libraries (such as matplotlib, where this issue was
> first found) would have to change their code anyway
> just to be compatible with numpy.
>
> I agree that in-place operations should allow
> different casting rules. There are different
> opinions on this, of course, but generally this is
> how NumPy has worked in the past.
>
> We did decide to change the default casting rule to
> "same_kind" but making an exception for in-place
> seems reasonable.
>
>
> I think that in these cases same_kind will flag what are
> most likely programming errors and sloppy code. It is
> easy to be explicit and doing so will make the code more
> readable because it will be immediately obvious what the
> multiplicand is without the need to recall what the
> numpy casting rules are in this exceptional case. IISTR
> several mentions of this before (Gael?), and in some of
> those cases it turned out that bugs were being turned
> up. Catching bugs with minimal effort is a good thing.
>
> Chuck
>
>
> True, it is quite likely to be a programming error, but then
> again, there are many cases where it isn't. Is the problem
> strictly that we are trying to downcast the float to an int,
> or is it that we are trying to downcast to a lower
> precision? Is there a way for one to explicitly relax the
> same_kind restriction?
>
>
> I think the problem is down casting across kinds, with the
> result that floats are truncated and the imaginary parts of
> imaginaries might be discarded. That is, the value, not just the
> precision, of the rhs changes. So I'd favor an explicit cast in
> code like this, i.e., cast the rhs to an integer.
>
> It is true that this forces downstream to code up to a higher
> standard, but I don't see that as a bad thing, especially if it
> exposes bugs. And it isn't difficult to fix.
>
> Chuck
>
>
> Mind you, in my case, casting the rhs as an integer before doing the
> multiplication would be a bug, since our value for the rhs is
> usually between zero and one. Multiplying first by the integer
> numerator before dividing by the integer denominator would likely
> cause issues with overflowing the 16 bit integer.
>
>
> For the case in point I'd do
>
> In [1]: a = np.array([1, 2, 3, 4, 5], dtype=np.int16)
>
> In [2]: a //= 2
>
> In [3]: a
> Out[3]: array([0, 1, 1, 2, 2], dtype=int16)
>
> Although I expect you would want something different in practice. But
> the current code already looks fragile to me and I think it is a good
> thing you are taking a closer look at it. If you really intend going
> through a float, then it should be something like
>
> a = (a*(float(128)/256)).astype(int16)
That's actually what we had been doing for years until a seemingly
harmless "optimization" snuck in via an unrelated PR. Fortunately, Ben
caught it after only a few days.
Eric
>
> Chuck
>
>
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