On Tue, 2013-04-23 at 23:33 -0400, [email protected] wrote: > On Tue, Apr 23, 2013 at 6:16 PM, Sebastian Berg > <[email protected]> wrote: > > On Tue, 2013-04-23 at 12:13 -0500, Jonathan Helmus wrote: > >> Back in December it was pointed out on the scipy-user list[1] that > >> numpy has a percentile function which has similar functionality to > >> scipy's stats.scoreatpercentile. I've been trying to harmonize these > >> two functions into a single version which has the features of both. > >> Scipy PR 374[2] introduced a version which look the parameters from > >> both the scipy and numpy percentile function and was accepted into Scipy > >> with the plan that it would be depreciated when a similar function was > >> introduced into Numpy. Then I moved to enhancing the Numpy version with > >> Pull Request 2970 [3]. With some input from Sebastian Berg the > >> percentile function was rewritten with further vectorization, but > >> neither of us felt fully comfortable with the final product. Can > >> someone look at implementation in the PR and suggest what should be done > >> from here? > >> > > > > Thanks! For me the main question is the vectorized usage when both > > haystack (`a`) and needle (`q`) are vectorized. What I mean is for: > > > > np.percentile(np.random.randn(n1, n2, N), [25., 50., 75.], axis=-1) > > > > I would probably expect an output shape of (n1, n2, 3), but currently > > you will get the needle dimensions first, because it is roughly the same > > as > > > > [np.percentile(np.random.randn(n1, n2, N), q, axis=-1) for q in [25., 50., > > 75.]] > > > > so for the (probably rare) vectorization of both `a` and `q`, would it > > be preferable to do some kind of long term behaviour change, or just put > > the dimensions in `q` first, which should be compatible to the current > > list? > > I don't have much of a preference either way, but I'm glad this is > going into numpy. > We can work with it either way. > > In stats, the most common case will be axis=0, and then the two are > the same, aren't they? > > What I like about the second version is unrolling (with 2 or 3 > quantiles), which I think will work > > u, l = np.random.randn(2,5) > or > res = np.percentile(...) > func(*res) > > The first case will be nicer when there are lots of percentiles, but I > guess I won't need it much except for axis=0. > > Actually, I would prefer the second version, because it might be a bit > more cumbersome to get the individual percentiles out if the axis is > somewhere in the middle, however I don't think I have a case like > that. >
I never thought about the axis being where to insert the dimensions of the quantiles. That would be a third option. It feels simpler to me to just always use the end (or the start) though. - Sebastian > The first version would be consistent with reduceat, and that would be > more numpythonic. I would go for that in numpy. > > my 2.5c > > Josef > > > > > Regards, > > > > Sebastian > > > >> Cheers, > >> > >> - Jonathan Helmus > >> > >> > >> [1] http://thread.gmane.org/gmane.comp.python.scientific.user/33331 > >> [2] https://github.com/scipy/scipy/pull/374 > >> [3] https://github.com/numpy/numpy/pull/2970 > >> _______________________________________________ > >> NumPy-Discussion mailing list > >> [email protected] > >> http://mail.scipy.org/mailman/listinfo/numpy-discussion > >> > > > > > > _______________________________________________ > > NumPy-Discussion mailing list > > [email protected] > > http://mail.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
