On Fri, Nov 19, 2010 at 8:42 PM, Keith Goodman <kwgood...@gmail.com> wrote:
> On Fri, Nov 19, 2010 at 7:19 PM, Charles R Harris
> <charlesr.har...@gmail.com> wrote:
> >
> >
> > On Fri, Nov 19, 2010 at 1:50 PM, Keith Goodman <kwgood...@gmail.com>
> wrote:
> >>
> >> On Fri, Nov 19, 2010 at 12:29 PM, Keith Goodman <kwgood...@gmail.com>
> >> wrote:
> >> > On Fri, Nov 19, 2010 at 12:19 PM, Pauli Virtanen <p...@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.
>
> Ubuntu 10.04 64 bit, numpy 1.4.1.
>
> Difference in which times? nanny.nanmax with and wintout NaNs? The
> code doesn't explictily check for NaNs (it does check for all NaNs).
> It basically loops through the data and does:
>
> allnan = 1
> ai = ai[i,k]
> if ai > amax:
> amax = ai
> allnan = 0
>
> I should make a benchmark suite.
> _
>
This doesn't look right:
@cython.boundscheck(False)
@cython.wraparound(False)
def nanmax_2d_float64_axisNone(np.ndarray[np.float64_t, ndim=2] a):
"nanmax of 2d numpy array with dtype=np.float64 along axis=None."
cdef Py_ssize_t i, j
cdef int arow = a.shape[0], acol = a.shape[1], allnan = 1
cdef np.float64_t amax = 0, aij
for i in range(arow):
for j in range(acol):
aij = a[i,j]
if aij == aij:
amax += aij
allnan = 0
if allnan == 0:
return np.float64(amax)
else:
return NAN
It's doing a sum, not a comparison.
Chuck
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