[Numpy-discussion] [ANN] Nanny, faster NaN functions

Charles R Harris charlesr.harris@gmail....
Fri Nov 19 21:37:17 CST 2010


On Fri, Nov 19, 2010 at 8:19 PM, Charles R Harris <charlesr.harris@gmail.com
> wrote:

>
>
> On Fri, Nov 19, 2010 at 1:50 PM, Keith Goodman <kwgoodman@gmail.com>wrote:
>
>> On Fri, Nov 19, 2010 at 12:29 PM, Keith Goodman <kwgoodman@gmail.com>
>> wrote:
>> > On Fri, Nov 19, 2010 at 12:19 PM, Pauli Virtanen <pav@iki.fi> wrote:
>> >> Fri, 19 Nov 2010 11:19:57 -0800, Keith Goodman wrote:
>> >> [clip]
>> >>> My guess is that having separate underlying functions for each dtype,
>> >>> ndim, and axis would be a nightmare for a large project like Numpy.
>> But
>> >>> manageable for a focused project like nanny.
>> >>
>> >> Might be easier to migrate the nan* functions to using Ufuncs.
>> >>
>> >> Unless I'm missing something,
>> >>
>> >>        np.nanmax -> np.fmax.reduce
>> >>        np.nanmin -> np.fmin.reduce
>> >>
>> >> For `nansum`, we'd need to add an ufunc `nanadd`, and for
>> >> `nanargmax/min`, we'd need `argfmin/fmax'.
>> >
>> > How about that! I wasn't aware of fmax/fmin. Yes, I'd like a nanadd,
>> please.
>> >
>> >>> arr = np.random.rand(1000, 1000)
>> >>> arr[arr > 0.5] = np.nan
>> >>> np.nanmax(arr)
>> >   0.49999625409581072
>> >>> np.fmax.reduce(arr, axis=None)
>> > <snip>
>> > TypeError: an integer is required
>> >>> np.fmax.reduce(np.fmax.reduce(arr, axis=0), axis=0)
>> >   0.49999625409581072
>> >
>> >>> timeit np.fmax.reduce(np.fmax.reduce(arr, axis=0), axis=0)
>> > 100 loops, best of 3: 12.7 ms per loop
>> >>> timeit np.nanmax(arr)
>> > 10 loops, best of 3: 39.6 ms per loop
>> >
>> >>> timeit np.nanmax(arr, axis=0)
>> > 10 loops, best of 3: 46.5 ms per loop
>> >>> timeit np.fmax.reduce(arr, axis=0)
>> > 100 loops, best of 3: 12.7 ms per loop
>>
>> Cython is faster than np.fmax.reduce.
>>
>> I wrote a cython version of np.nanmax, called nanmax below. (It only
>> handles the 2d, float64, axis=None case, but since the array is large
>> I don't think that explains the time difference).
>>
>> Note that fmax.reduce is slower than np.nanmax when there are no NaNs:
>>
>> >> arr = np.random.rand(1000, 1000)
>> >> timeit np.nanmax(arr)
>> 100 loops, best of 3: 5.82 ms per loop
>> >> timeit np.fmax.reduce(np.fmax.reduce(arr))
>> 100 loops, best of 3: 9.14 ms per loop
>> >> timeit nanmax(arr)
>> 1000 loops, best of 3: 1.17 ms per loop
>>
>> >> arr[arr > 0.5] = np.nan
>>
>> >> timeit np.nanmax(arr)
>> 10 loops, best of 3: 45.5 ms per loop
>> >> timeit np.fmax.reduce(np.fmax.reduce(arr))
>> 100 loops, best of 3: 12.7 ms per loop
>> >> timeit nanmax(arr)
>> 1000 loops, best of 3: 1.17 ms per loop
>>
>
> There seem to be some odd hardware/compiler dependencies. I get quite a
> different pattern of times:
>
> In [1]: arr = np.random.rand(1000, 1000)
>
> In [2]: timeit np.nanmax(arr)
> 100 loops, best of 3: 10.4 ms per loop
>
> In [3]: timeit np.fmax.reduce(arr.flat)
> 100 loops, best of 3: 2.09 ms per loop
>
> In [4]: arr[arr > 0.5] = np.nan
>
> In [5]: timeit np.nanmax(arr)
> 100 loops, best of 3: 12.9 ms per loop
>
> In [6]: timeit np.fmax.reduce(arr.flat)
> 100 loops, best of 3: 7.09 ms per loop
>
>
> I've tweaked fmax with the reduce loop option but the nanmax times don't
> look like yours at all. I'm also a bit surprised that
> you don't see any difference in times when the array contains a lot of
> nans. I'm running on AMD Phenom, gcc 4.4.5.
>
>
However, I noticed that the build wants to be -O1 by default. I have my own
CFLAGS that make it -O2, but It looks like ubuntu's python might be built
with -O1. Hmm. That could certainly cause some odd timings.

Chuck
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