On Mon, 2018-03-26 at 11:53 -0400, Hameer Abbasi wrote: > It'll need to be thought out for object arrays and subclasses. But > for > Regular numeric stuff, Numpy uses fmin and this would have the > desired > effect.
I do not want to block this, but I would like a clearer opinion about this issue, `np.nansum` as Benjamin noted would require something like: np.nansum([np.nan], default=np.nan) because np.sum([1], initializer=np.nan) np.nansum([1], initializer=np.nan) would both give NaN if the logic is the same as the current `np.sum`. And yes, I guess for fmin/fmax NaN happens to work. And then there are many nonsense reduces which could make sense with `initializer`. Now nansum is not implemented in a way that could make use of the new kwarg anyway, so maybe it does not matter in some sense. We can in principle use `default` in nansum and at some point possibly add `default` to the normal ufuncs. If we argue like that, the only annoying thing is the `object` dtype which confuses the two use cases currently. This confusion IMO is not harmless, because I might want to use it (e.g. sum with initializer=5), and I would expect things like dropping in `decimal.Decimal` to work most of the time, while here it would give silently bad results. - Sebastian > On 26/03/2018 at 17:45, Sebastian wrote: On Mon, 2018-03-26 at > 11:39 -0400, Hameer Abbasi wrote: That is the idea, but NaN functions > are in a separate branch for another PR to be discussed later. You > can > see it on my fork, if you're interested. Except that as far as I > understand I am not sure it will help much with it, since it is not a > default, but an initializer. Initializing to NaN would just make all > results NaN. - Sebastian On 26/03/2018 at 17:35, Benjamin wrote: Hmm, > this is neat. I imagine it would finally give some people a choice on > what np.nansum([np.nan]) should return? It caused a huge hullabeloo a > few years ago when we changed it from returning NaN to returning > zero. > Ben Root On Mon, Mar 26, 2018 at 11:16 AM, Sebastian Berg > <sebast...@sipsolutions.net> wrote: OK, the new documentation is > actually clear: initializer : scalar, optional The value with which > to > start the reduction. Defaults to the `~numpy.ufunc.identity` of the > ufunc. If ``None`` is given, the first element of the reduction is > used, and an error is thrown if the reduction is empty. If > ``a.dtype`` > is ``object``, then the initializer is _only_ used if reduction is > empty. I would actually like to say that I do not like the object > special case much (and it is probably the reason why I was confused), > nor am I quite sure this is what helps a lot? Logically, I would > argue > there are two things: 1. initializer/start (always used) 2. default > (oly used for empty reductions) For example, I might like to give > `np.nan` as the default for some empty reductions, this will not > work. > I understand that this is a minimal invasive PR and I am not sure I > find the solution bad enough to really dislike it, but what do other > think? My first expectation was the default behaviour (in all cases, > not just object case) for some reason. To be honest, for now I just > wonder a bit: How hard would it be to do both, or is that too > annoying? It would at least get rid of that annoying thing with > object > ufuncs (which currently have a default, but not really an > identity/initializer). Best, Sebastian On Mon, 2018-03-26 at 08:20 > -0400, Hameer Abbasi wrote: > Actually, the behavior right now isn’t > that of `default` but that of > `initializer` or `start`. > > This > was > discussed further down in the PR but to reiterate: > `np.sum([10], > initializer=5)` becomes `15`. > > Also, `np.min([5], initializer=0)` > becomes `0`, so it isn’t really > the default value, it’s the initial > value among which the reduction > is performed. > > This was the > reason to call it initializer in the first place. I like > `initial` > and `initial_value` as well, and `start` also makes sense > but isn’t > descriptive enough. > > Hameer > Sent from Astro for Mac > > > On Mar > 26, 2018 at 12:06, Sebastian Berg <sebast...@sipsolutions.ne > > t> > wrote: > > > > Initializer or this sounds fine to me. As an other > data > point which > > I > > think has been mentioned before, `sum` uses > start and min/max use > > default. `start` does not work, unless we > also change the code to > > always use the identity if given > (currently that is not the case), > > in > > which case it might be > nice. However, "start" seems a bit like > > solving > > a different > issue in any case. > > > > Anyway, mostly noise. I really like adding > this, the only thing > > worth > > discussing a bit is the name :). > > - Sebastian > > > > > > On Mon, 2018-03-26 at 05:57 -0400, Hameer > Abbasi wrote: > > > It calls it `initializer` - See > https://docs.python.org/3.5/libra > > > ry/f > > > > unctools.html#functools.reduce > > > > > > Sent from Astro for Mac > > On Mar 26, 2018 at 09:54, Eric Wieser <wieser.eric+numpy@gmail. > > > > > com> > > > > wrote: > > > > > > > > It turns out I mispoke - > > functools.reduce calls the argument > > > > `initial` > > > > > > > > > On Mon, 26 Mar 2018 at 00:17 Stephan Hoyer <sho...@gmail.com> > > > > > wrote: > > > > > This looks like a very logical addition to the > reduce > > > > > > interface. > > > > > It has my support! > > > > > > > > > > > > I would have preferred the more descriptive name > > > > > > "initial_value", > > > > > but consistency with functools.reduce > makes > a compelling case > > > > > for > > > > > "initializer". > > > > > > > > > > > > On Sun, Mar 25, 2018 at 1:15 PM Eric Wieser <wieser.eric+nump > > > > > > y@gm > > > > > ail.com> wrote: To reiterate my comments in > > > > > the > > issue - I'm in favor of > this. > > > > > > > > > > > > It seems seem > especially valuable for identity-less > > > > > > functions > > > > > > > (`min`, `max`, `lcm`), and the argument name is consistent > > > > > > > > > with > `functools.reduce`. too. > > > > > > > > > > > > The only > > argument I can see against merging this would be > > > > > > > `kwarg`-creep of `reduce`, and I think this has enough use > > > > > > cases to justify that. > > > > > > > > > > > > I'd like to merge in a > few days, if no one else has any > > > > > > opinions. > > > > > > > > Eric > > > > > > > > > > > > On Fri, 16 Mar 2018 at 10:13 Hameer > Abbasi <einstein.edison > > > > > > @gma > > > > > > il.com> wrote: > > > > > > > > Hello, everyone. I’ve submitted a PR to add a initializer > > > > > > > > kwarg to ufunc.reduce. This is useful in a few cases, > > > > > > > > > > > > > > > e.g., > > > > > > > it allows one to supply a “default” > > value for identity- > > > > > > > less > > > > > > > ufunc > reductions, > and specify an initial value for > > > > > > > reductions such as sum > (other than zero.) > > > > > > > > > > > > Please feel free to review > or leave feedback, (although I > > > > > think Eric and Marten have > picked it apart pretty well). > > > > > https://github.com/numpy/numpy/pull/10635 > > > > > Thanks, > > > > > > > > > > > > > > > Hameer > > > > Sent from Astro for Mac > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > _______________________________________________ > > > > > > > > > > > > NumPy-Discussion mailing list > > > > > > > > > NumPy-Discussion@python.org > > > > > > > > https://mail.python.org/mailman/listinfo/numpy-discussion > > > > > > > _______________________________________________ > > > > > > NumPy-Discussion mailing list > > > > > > NumPy-Discussion@python.org > > > > > > > https://mail.python.org/mailman/listinfo/numpy-discussion > > > > > > > _______________________________________________ > > > > > > > NumPy-Discussion mailing list > > > > > NumPy-Discussion@python.org > > https://mail.python.org/mailman/listinfo/numpy-discussion > > > _______________________________________________ > > > > > NumPy-Discussion mailing list > > > > NumPy-Discussion@python.org > > > https://mail.python.org/mailman/listinfo/numpy-discussion > > > > > _______________________________________________ > > > NumPy- > Discussion > mailing list > > > NumPy-Discussion@python.org > > > > https://mail.python.org/mailman/listinfo/numpy-discussion > > > > > _______________________________________________ > > NumPy-Discussion > mailing list > > NumPy-Discussion@python.org > > > https://mail.python.org/mailman/listinfo/numpy-discussion > > > _______________________________________________ > NumPy-Discussion > mailing list > NumPy-Discussion@python.org > > https://mail.python.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion > mailing list NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion > mailing list NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion
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