PR to add an initializer kwarg to ufunc.reduce (and similar functions)
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 identityless 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 https://www.helloastro.com for Mac
To reiterate my comments in the issue  I'm in favor of this.
It seems seem especially valuable for identityless 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@gmail.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 identityless 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 https://www.helloastro.com for Mac
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
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+numpy@gmail.com wrote:
To reiterate my comments in the issue  I'm in favor of this.
It seems seem especially valuable for identityless 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@gmail.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 identityless 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 https://www.helloastro.com for Mac
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
It turns out I mispoke  functools.reduce calls the argument `initial`
On Mon, 26 Mar 2018 at 00:17 Stephan Hoyer shoyer@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+numpy@gmail.com wrote:
To reiterate my comments in the issue  I'm in favor of this.
It seems seem especially valuable for identityless 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@gmail.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 identityless 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 https://www.helloastro.com for Mac
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
It calls it `initializer`  See https://docs.python.org/3.5/library/functools.html#functools.reduce
Sent from Astro https://www.helloastro.com 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 shoyer@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+numpy@gmail.com wrote:
To reiterate my comments in the issue  I'm in favor of this.
It seems seem especially valuable for identityless 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@gmail.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 identityless 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 https://www.helloastro.com for Mac
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
_______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
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, 20180326 at 05:57 0400, Hameer Abbasi wrote:
It calls it `initializer`  See https://docs.python.org/3.5/library/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 shoyer@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+numpy@gm ail.com> wrote:
To reiterate my comments in the issue  I'm in favor of this.
It seems seem especially valuable for identityless 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 identityless 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
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
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 https://www.helloastro.com for Mac
On Mar 26, 2018 at 12:06, Sebastian Berg sebastian@sipsolutions.net 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, 20180326 at 05:57 0400, Hameer Abbasi wrote:
It calls it `initializer`  See https://docs.python.org/3.5/library/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 shoyer@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+numpy@gm ail.com> wrote:
To reiterate my comments in the issue  I'm in favor of this.
It seems seem especially valuable for identityless 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 identityless 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
_______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
_______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
_______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
_______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
_______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
_______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
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, 20180326 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 <sebastian@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, 20180326 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 shoyer@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 identityless 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 > > _______________________________________________ > NumPyDiscussion mailing list > NumPyDiscussion@python.org > https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
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 <sebastian@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:
 initializer/start (always used)
 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, 20180326 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 <sebastian@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, 20180326 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 shoyer@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 identityless > 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 > > > > _______________________________________________ > > NumPyDiscussion mailing list > > NumPyDiscussion@python.org > > https://mail.python.org/mailman/listinfo/numpydiscussion > > _______________________________________________ > NumPyDiscussion mailing list > NumPyDiscussion@python.org > https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
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. 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 sebastian@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, 20180326 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 <sebastian@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, 20180326 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 shoyer@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 identityless > > > > > > 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 > > > > > > > > > > > > > >
_______________________________________________ > > > > > > > NumPyDiscussion mailing list > > > > > > > NumPyDiscussion@python.org > > > > > > > https://mail.python.org/mailman/listinfo/numpydiscussion > > > > > >
_______________________________________________ > > > > >
NumPyDiscussion mailing list > > > > > >
NumPyDiscussion@python.org > > > > > > https://mail.python.org/mailman/listinfo/numpydiscussion > > > > > >
_______________________________________________ > > > > >
NumPyDiscussion mailing list > > > > > NumPyDiscussion@python.org >
https://mail.python.org/mailman/listinfo/numpydiscussion > >
_______________________________________________ > > > >
NumPyDiscussion mailing list > > > > NumPyDiscussion@python.org > >
https://mail.python.org/mailman/listinfo/numpydiscussion > > > > _______________________________________________ > > >
NumPyDiscussion mailing list > > > NumPyDiscussion@python.org > > > https://mail.python.org/mailman/listinfo/numpydiscussion > > > > _______________________________________________ > > NumPyDiscussion mailing list > > NumPyDiscussion@python.org > > https://mail.python.org/mailman/listinfo/numpydiscussion > > _______________________________________________ > NumPyDiscussion mailing list > NumPyDiscussion@python.org > https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
On Mon, 20180326 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 sebastian@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, 20180326 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 <sebastian@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, 20180326 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 shoyer@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 identityless > > > > > > 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 > > > > > > > > > > > > > >
_______________________________________________ > > > > > > > NumPyDiscussion mailing list > > > > > > > NumPyDiscussion@python.org > > > > > > > https://mail.python.org/mailman/listinfo/numpydiscussion > > > > > >
> _______________________________________________ > > > > >
NumPyDiscussion mailing list > > > > > >
NumPyDiscussion@python.org > > > > > > https://mail.python.org/mailman/listinfo/numpydiscussion > > > > > >
_______________________________________________ > > > > >
NumPyDiscussion mailing list > > > > > NumPyDiscussion@python.org >
https://mail.python.org/mailman/listinfo/numpydiscussion > >
> _______________________________________________ > > > >
NumPyDiscussion mailing list > > > > NumPyDiscussion@python.org > >
https://mail.python.org/mailman/listinfo/numpydiscussion > > > > _______________________________________________ > > >
NumPyDiscussion mailing list > > > NumPyDiscussion@python.org > > > https://mail.python.org/mailman/listinfo/numpydiscussion > > > > _______________________________________________ > > NumPyDiscussion mailing list > > NumPyDiscussion@python.org > > https://mail.python.org/mailman/listinfo/numpydiscussion > > _______________________________________________ > NumPyDiscussion mailing list > NumPyDiscussion@python.org > https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
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. On 26/03/2018 at 17:45, Sebastian wrote: On Mon, 20180326 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 sebastian@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, 20180326 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 <sebastian@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, 20180326 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 shoyer@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 identityless > > > > > > 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 > > > > > > > > _______________________________________________ > > > >
NumPyDiscussion mailing list > > > > > > >
NumPyDiscussion@python.org > > > > > > > https://mail.python.org/mailman/listinfo/numpydiscussion > > > > > > _______________________________________________ > > > > > NumPyDiscussion mailing list > > > > > > NumPyDiscussion@python.org
https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ > > > > >
NumPyDiscussion mailing list > > > > > NumPyDiscussion@python.org > https://mail.python.org/mailman/listinfo/numpydiscussion > > _______________________________________________ > > > > NumPyDiscussion mailing list > > > > NumPyDiscussion@python.org > > https://mail.python.org/mailman/listinfo/numpydiscussion > > > > _______________________________________________ > > > NumPyDiscussion mailing list > > > NumPyDiscussion@python.org > > > https://mail.python.org/mailman/listinfo/numpydiscussion > > > > _______________________________________________ > > NumPyDiscussion mailing list > > NumPyDiscussion@python.org > > https://mail.python.org/mailman/listinfo/numpydiscussion > > _______________________________________________ > NumPyDiscussion mailing list > NumPyDiscussion@python.org > https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
On Mon, 20180326 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, 20180326 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 sebastian@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, 20180326 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 <sebastian@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, 20180326 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 shoyer@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 identityless > > > > > > 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 > > > > > > > > > > > > > > > _______________________________________________ > > > >
NumPyDiscussion mailing list > > > > > > >
NumPyDiscussion@python.org > > > > > > > https://mail.python.org/mailman/listinfo/numpydiscussion > > > > > > _______________________________________________ > > > > > NumPyDiscussion mailing list > > > > > > NumPyDiscussion@python.org
> https://mail.python.org/mailman/listinfo/numpydiscussion > _______________________________________________ > > > > >
NumPyDiscussion mailing list > > > > > NumPyDiscussion@python.org > https://mail.python.org/mailman/listinfo/numpydiscussion > > _______________________________________________ > > > > NumPyDiscussion mailing list > > > > NumPyDiscussion@python.org > > https://mail.python.org/mailman/listinfo/numpydiscussion > > > > _______________________________________________ > > > NumPy Discussion mailing list > > > NumPyDiscussion@python.org > > > https://mail.python.org/mailman/listinfo/numpydiscussion > > > > _______________________________________________ > > NumPyDiscussion mailing list > > NumPyDiscussion@python.org > > https://mail.python.org/mailman/listinfo/numpydiscussion > > _______________________________________________ > NumPyDiscussion mailing list > NumPyDiscussion@python.org > https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
On Mon, 20180326 at 18:48 +0200, Sebastian Berg wrote:
On Mon, 20180326 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.
In other words: I am very very much in favor if you get rid that object dtype special case. I frankly not see why not (except that it needs a bit more code change). If given explicitly, we might as well force the use and not do the funny stuff which is designed to be more type agnostic! If it happens to fail due to not being type agnostic, it will at least fail loudly.
If you leave that object special case I am *very* hesitant about it.
That I think I would like a `default` argument as well, is another issue and it can wait to another day.
 Sebastian
 Sebastian
On 26/03/2018 at 17:45, Sebastian wrote: On Mon, 20180326 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 sebastian@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, 20180326 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 <sebastian@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, 20180326 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 shoyer@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 identityless > > > > > > 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 > > > > > > > > > > > > > > > > > > > > > > > _______________________________________________ > > > > > > >
NumPyDiscussion mailing list > > > > > > >
NumPyDiscussion@python.org > > > > > > > https://mail.python.org/mailman/listinfo/numpydiscussion > > > > >
_______________________________________________ > > > > > NumPyDiscussion mailing list > > > > > > NumPyDiscussion@python.o rg
> > https://mail.python.org/mailman/listinfo/numpydiscussi > > on > > _______________________________________________ > > > > > > >
NumPyDiscussion mailing list > > > > > NumPyDiscussion@python.org
https://mail.python.org/mailman/listinfo/numpydiscussion > > _______________________________________________ > > > > NumPyDiscussion mailing list > > > > NumPyDiscussion@python.org >
https://mail.python.org/mailman/listinfo/numpydiscussion > > > > _______________________________________________ > > > NumPy Discussion mailing list > > > NumPyDiscussion@python.org > > > https://mail.python.org/mailman/listinfo/numpydiscussion > > > > _______________________________________________ > > NumPy Discussion mailing list > > NumPyDiscussion@python.org > > https://mail.python.org/mailman/listinfo/numpydiscussion > > _______________________________________________ > NumPyDiscussion mailing list > NumPyDiscussion@python.org > https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
That may be complicated. Currently, the identity isn't used in object dtype reductions. We may need to change that, which could cause a whole lot of other backwards incompatible changes. For example, sum actually including zero in object reductions. Or we could pass in a flag saying an initializer was passed in to change that behaviour. If this is agreed upon and someone is kind enough to point me to the code, I'd be willing to make this change. On 26/03/2018 at 18:54, Sebastian wrote: On Mon, 20180326 at 18:48 +0200, Sebastian Berg wrote: On Mon, 20180326 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. In other words: I am very very much in favor if you get rid that object dtype special case. I frankly not see why not (except that it needs a bit more code change). If given explicitly, we might as well force the use and not do the funny stuff which is designed to be more type agnostic! If it happens to fail due to not being type agnostic, it will at least fail loudly. If you leave that object special case I am *very* hesitant about it. That I think I would like a `default` argument as well, is another issue and it can wait to another day.  Sebastian  Sebastian On 26/03/2018 at 17:45, Sebastian wrote: On Mon, 20180326 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 sebastian@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, 20180326 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 <sebastian@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, 20180326 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 shoyer@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 identityless 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 > > > > > _______________________________________________ > > > NumPyDiscussion mailing list > > > > > > > NumPyDiscussion@python.org > > > > > > > https://mail.python.org/mailman/listinfo/numpydiscussion > > > > > _______________________________________________ > > > > > NumPyDiscussion mailing list > > > > > > NumPyDiscussion@python.o rg https://mail.python.org/mailman/listinfo/numpydiscussi on _______________________________________________ > > > > NumPyDiscussion mailing list > > > > > NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion > > _______________________________________________ > > > > NumPyDiscussion mailing list > > > > NumPyDiscussion@python.org > https://mail.python.org/mailman/listinfo/numpydiscussion > > > > _______________________________________________ > > > NumPy Discussion mailing list > > > NumPyDiscussion@python.org > > > https://mail.python.org/mailman/listinfo/numpydiscussion > > > > _______________________________________________ > > NumPy Discussion mailing list > > NumPyDiscussion@python.org > > https://mail.python.org/mailman/listinfo/numpydiscussion > > _______________________________________________ > NumPyDiscussion mailing list > NumPyDiscussion@python.org > https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
On Mon, 20180326 at 12:59 0400, Hameer Abbasi wrote:
That may be complicated. Currently, the identity isn't used in object dtype reductions. We may need to change that, which could cause a whole lot of other backwards incompatible changes. For example, sum actually including zero in object reductions. Or we could pass in a flag saying an initializer was passed in to change that behaviour. If this is agreed upon and someone is kind enough to point me to the code, I'd be willing to make this change.
I realize the implication, I am not suggesting to change the default behaviour (when no initial=... is passed), I would think about deprecating it, but probably only if we also have the `default` argument, since otherwise you cannot replicate the old behaviour.
What I think I would like to see is to change how it works if (and only if) the initializer is passed in. Yes, this will require holding on to some extra information since you will have to know/remember whether the "identity" was passed in or defined otherwise.
I did not check the code, but I would hope that it is not awfully tricky to do that.
 Sebastian
PS: A side note, but I see your emails as a single block of text with no/broken newlines.
On 26/03/2018 at 18:54, Sebastian wrote: On Mon, 20180326 at 18:48 +0200, Sebastian Berg wrote: On Mon, 20180326 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. In other words: I am very very much in favor if you get rid that object dtype special case. I frankly not see why not (except that it needs a bit more code change). If given explicitly, we might as well force the use and not do the funny stuff which is designed to be more type agnostic! If it happens to fail due to not being type agnostic, it will at least fail loudly. If you leave that object special case I am *very* hesitant about it. That I think I would like a `default` argument as well, is another issue and it can wait to another day.  Sebastian  Sebastian On 26/03/2018 at 17:45, Sebastian wrote: On Mon, 20180326 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 sebastian@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, 20180326 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 <sebastian@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, 20180326 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 shoyer@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 identityless > > 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 > > > > > > > NumPyDiscussion@python.org > > > > > > > https://mail.python.org/mailman/listinfo/numpydiscussion > > > > > _______________________________________________ > > > > > NumPyDiscussion mailing list > > > > > > NumPyDiscussion@python.o rg https://mail.python.org/mailman/listinfo/numpydiscussi on _______________________________________________ > > > > NumPyDiscussion mailing list > > > > > NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion > > _______________________________________________ > > > > NumPyDiscussion mailing list > > > > NumPyDiscussion@python.org > https://mail.python.org/mailman/listinfo/numpydiscussion > > > > _______________________________________________ > > > NumPy Discussion mailing list > > > NumPyDiscussion@python.org > > > https://mail.python.org/mailman/listinfo/numpydiscussion > > > > _______________________________________________ > > NumPy Discussion mailing list > > NumPyDiscussion@python.org > > https://mail.python.org/mailman/listinfo/numpydiscussion > > _______________________________________________ > NumPyDiscussion mailing list > NumPyDiscussion@python.org > https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
The difficulty in supporting object arrays is that func.reduce(arr, initial=func.identity) and func.reduce(arr) have different meanings  whereas with the current patch, they are equivalent.
On Mon, 26 Mar 2018 at 10:10 Sebastian Berg sebastian@sipsolutions.net wrote:
On Mon, 20180326 at 12:59 0400, Hameer Abbasi wrote:
That may be complicated. Currently, the identity isn't used in object dtype reductions. We may need to change that, which could cause a whole lot of other backwards incompatible changes. For example, sum actually including zero in object reductions. Or we could pass in a flag saying an initializer was passed in to change that behaviour. If this is agreed upon and someone is kind enough to point me to the code, I'd be willing to make this change.
I realize the implication, I am not suggesting to change the default behaviour (when no initial=... is passed), I would think about deprecating it, but probably only if we also have the `default` argument, since otherwise you cannot replicate the old behaviour.
What I think I would like to see is to change how it works if (and only if) the initializer is passed in. Yes, this will require holding on to some extra information since you will have to know/remember whether the "identity" was passed in or defined otherwise.
I did not check the code, but I would hope that it is not awfully tricky to do that.
 Sebastian
PS: A side note, but I see your emails as a single block of text with no/broken newlines.
On 26/03/2018 at 18:54, Sebastian wrote: On Mon, 20180326 at 18:48 +0200, Sebastian Berg wrote: On Mon, 20180326 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. In other words: I am very very much in favor if you get rid that object dtype special case. I frankly not see why not (except that it needs a bit more code change). If given explicitly, we might as well force the use and not do the funny stuff which is designed to be more type agnostic! If it happens to fail due to not being type agnostic, it will at least fail loudly. If you leave that object special case I am *very* hesitant about it. That I think I would like a `default` argument as well, is another issue and it can wait to another day.  Sebastian  Sebastian On 26/03/2018 at 17:45, Sebastian wrote: On Mon, 20180326 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 sebastian@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, 20180326 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 <sebastian@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, 20180326 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 shoyer@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 identityless > > > > 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 > > > > > > > NumPyDiscussion@python.org > > > > > > > https://mail.python.org/mailman/listinfo/numpydiscussion > > > > > _______________________________________________ > > > > > NumPyDiscussion mailing list > > > > > > NumPyDiscussion@python.o rg https://mail.python.org/mailman/listinfo/numpydiscussi on _______________________________________________ > > > > NumPyDiscussion mailing list > > > > > NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion > > _______________________________________________ > > > > NumPyDiscussion mailing list > > > > NumPyDiscussion@python.org > https://mail.python.org/mailman/listinfo/numpydiscussion > > > > _______________________________________________ > > > NumPy Discussion mailing list > > > NumPyDiscussion@python.org > > > https://mail.python.org/mailman/listinfo/numpydiscussion > > > > _______________________________________________ > > NumPy Discussion mailing list > > NumPyDiscussion@python.org > > https://mail.python.org/mailman/listinfo/numpydiscussion > > _______________________________________________ > NumPyDiscussion mailing list > NumPyDiscussion@python.org > https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
On Mon, 20180326 at 17:40 +0000, Eric Wieser wrote:
The difficulty in supporting object arrays is that func.reduce(arr, initial=func.identity) and func.reduce(arr) have different meanings  whereas with the current patch, they are equivalent.
True, but the current meaning is:
func.reduce(arr, intial=<NoValue>, default=func.identity)
in the case for object dtype. Luckily for normal dtypes, func.identity is both the correct default "default" and a noop for initial. Thus the name "identity" kinda works there. I am also not really sure that both kwargs would make real sense (plus initial probably disallows default...), but I got some feeling that the "default" meaning may be even more useful to simplify special casing the empty case.
Anyway, still just pointing out that I it gives me some headaches to see such a special case for objects :(.
 Sebastian
On Mon, 26 Mar 2018 at 10:10 Sebastian Berg <sebastian@sipsolutions.n et> wrote:
On Mon, 20180326 at 12:59 0400, Hameer Abbasi wrote:
That may be complicated. Currently, the identity isn't used in
object
dtype reductions. We may need to change that, which could cause a whole lot of other backwards incompatible changes. For example,
sum
actually including zero in object reductions. Or we could pass in
a
flag saying an initializer was passed in to change that
behaviour. If
this is agreed upon and someone is kind enough to point me to the code, I'd be willing to make this change.
I realize the implication, I am not suggesting to change the default behaviour (when no initial=... is passed), I would think about deprecating it, but probably only if we also have the `default` argument, since otherwise you cannot replicate the old behaviour.
What I think I would like to see is to change how it works if (and only if) the initializer is passed in. Yes, this will require holding on to some extra information since you will have to know/remember whether the "identity" was passed in or defined otherwise.
I did not check the code, but I would hope that it is not awfully tricky to do that.
 Sebastian
PS: A side note, but I see your emails as a single block of text with no/broken newlines.
On 26/03/2018 at 18:54, Sebastian wrote: On Mon, 20180326 at 18:48 +0200, Sebastian
Berg
wrote: On Mon, 20180326 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. In other words: I am very very
much
in favor if you get rid that object dtype special case. I frankly
not
see why not (except that it needs a bit more code change). If
given
explicitly, we might as well force the use and not do the funny
stuff
which is designed to be more type agnostic! If it happens to fail
due
to not being type agnostic, it will at least fail loudly. If you leave that object special case I am *very* hesitant about it. That I
think
I would like a `default` argument as well, is another issue and it
can
wait to another day.  Sebastian  Sebastian On 26/03/2018 at
17:45,
Sebastian wrote: On Mon, 20180326 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 sebastian@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, 20180326 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 <sebastian@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, 20180326 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 shoyer@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 > > > > > > > NumPyDiscussion@python.org > > > > >
https://mail.python.org/mailman/listinfo/numpydiscussion > > > >
_______________________________________________ > > > > > NumPyDiscussion mailing list > > > > > > NumPyDiscussion@python
.o
rg https://mail.python.org/mailman/listinfo/numpydiscussi on _______________________________________________ > > > > NumPyDiscussion mailing list > > > > > NumPyDiscussion@python.o
rg
https://mail.python.org/mailman/listinfo/numpydiscussion > > _______________________________________________ > > > > NumPyDiscussion mailing list > > > > NumPyDiscussion@python.org
https://mail.python.org/mailman/listinfo/numpydiscussion > > > > _______________________________________________ > > > NumPy Discussion mailing list > > > NumPyDiscussion@python.org > > > https://mail.python.org/mailman/listinfo/numpydiscussion > > > > _______________________________________________ > > NumPy
Discussion
mailing list > > NumPyDiscussion@python.org > > https://mail.python.org/mailman/listinfo/numpydiscussion > > _______________________________________________ > NumPy
Discussion
mailing list > NumPyDiscussion@python.org > https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion________
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
I've renamed the kwarg to `initial`. I'm willing to make the object dtype changes as well, if someone pointed me to relevant bits of code.
Unfortunately, currently, the identity is only used for object dtypes if the reduction is empty. I think this is to prevent things like `0` being passed in the sum of objects (and similar cases), which makes sense.
However, with the kwarg, it makes sense to include it in the reduction. I think the change will be somewhere along the lines of: Detect if `initial` was passed, if so, include for object, otherwise exclude.
I personally feel `initial` renders `default` redundant. It can be used for both purposes. I can't think of a reasonable use case where you would want the default to be different from the initial value. However, I do agree that fixing the object case is important, we don't want users to get used to this behaviour and then rely on it later.
Hameer
On Mon, Mar 26, 2018 at 8:09 PM, Sebastian Berg sebastian@sipsolutions.net wrote:
On Mon, 20180326 at 17:40 +0000, Eric Wieser wrote:
The difficulty in supporting object arrays is that func.reduce(arr, initial=func.identity) and func.reduce(arr) have different meanings  whereas with the current patch, they are equivalent.
True, but the current meaning is:
func.reduce(arr, intial=<NoValue>, default=func.identity)
in the case for object dtype. Luckily for normal dtypes, func.identity is both the correct default "default" and a noop for initial. Thus the name "identity" kinda works there. I am also not really sure that both kwargs would make real sense (plus initial probably disallows default...), but I got some feeling that the "default" meaning may be even more useful to simplify special casing the empty case.
Anyway, still just pointing out that I it gives me some headaches to see such a special case for objects :(.
 Sebastian
On Mon, 26 Mar 2018 at 10:10 Sebastian Berg <sebastian@sipsolutions.n et> wrote:
On Mon, 20180326 at 12:59 0400, Hameer Abbasi wrote:
That may be complicated. Currently, the identity isn't used in
object
dtype reductions. We may need to change that, which could cause a whole lot of other backwards incompatible changes. For example,
sum
actually including zero in object reductions. Or we could pass in
a
flag saying an initializer was passed in to change that
behaviour. If
this is agreed upon and someone is kind enough to point me to the code, I'd be willing to make this change.
I realize the implication, I am not suggesting to change the default behaviour (when no initial=... is passed), I would think about deprecating it, but probably only if we also have the `default` argument, since otherwise you cannot replicate the old behaviour.
What I think I would like to see is to change how it works if (and only if) the initializer is passed in. Yes, this will require holding on to some extra information since you will have to know/remember whether the "identity" was passed in or defined otherwise.
I did not check the code, but I would hope that it is not awfully tricky to do that.
 Sebastian
PS: A side note, but I see your emails as a single block of text with no/broken newlines.
On 26/03/2018 at 18:54, Sebastian wrote: On Mon, 20180326 at 18:48 +0200, Sebastian
Berg
wrote: On Mon, 20180326 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. In other words: I am very very
much
in favor if you get rid that object dtype special case. I frankly
not
see why not (except that it needs a bit more code change). If
given
explicitly, we might as well force the use and not do the funny
stuff
which is designed to be more type agnostic! If it happens to fail
due
to not being type agnostic, it will at least fail loudly. If you leave that object special case I am *very* hesitant about it. That I
think
I would like a `default` argument as well, is another issue and it
can
wait to another day.  Sebastian  Sebastian On 26/03/2018 at
17:45,
Sebastian wrote: On Mon, 20180326 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 sebastian@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, 20180326 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 <sebastian@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, 20180326 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 shoyer@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 > > > > > > > NumPyDiscussion@python.org > > > > >
https://mail.python.org/mailman/listinfo/numpydiscussion > > > >
_______________________________________________ > > > > > NumPyDiscussion mailing list > > > > > > NumPyDiscussion@python
.o
rg https://mail.python.org/mailman/listinfo/numpydiscussi on _______________________________________________ > > > > NumPyDiscussion mailing list > > > > > NumPyDiscussion@python.o
rg
https://mail.python.org/mailman/listinfo/numpydiscussion > > _______________________________________________ > > > > NumPyDiscussion mailing list > > > > NumPyDiscussion@python.org
https://mail.python.org/mailman/listinfo/numpydiscussion > > > > _______________________________________________ > > > NumPy Discussion mailing list > > > NumPyDiscussion@python.org > > > https://mail.python.org/mailman/listinfo/numpydiscussion > > > > _______________________________________________ > > NumPy
Discussion
mailing list > > NumPyDiscussion@python.org > > https://mail.python.org/mailman/listinfo/numpydiscussion > > _______________________________________________ > NumPy
Discussion
mailing list > NumPyDiscussion@python.org > https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion________
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
On Mon, 20180409 at 13:37 +0200, Hameer Abbasi wrote:
I've renamed the kwarg to `initial`. I'm willing to make the object dtype changes as well, if someone pointed me to relevant bits of code.
Unfortunately, currently, the identity is only used for object dtypes if the reduction is empty. I think this is to prevent things like `0` being passed in the sum of objects (and similar cases), which makes sense.
However, with the kwarg, it makes sense to include it in the reduction. I think the change will be somewhere along the lines of: Detect if `initial` was passed, if so, include for object, otherwise exclude.
I personally feel `initial` renders `default` redundant. It can be used for both purposes. I can't think of a reasonable use case where you would want the default to be different from the initial value. However, I do agree that fixing the object case is important, we don't want users to get used to this behaviour and then rely on it later.
The reason would be the case of NaN which is not a possible initial value for the reduction. I personally find the object case important, if someone seriously argues the opposite I might be swayed possibly.
 Sebastian
Hameer
On Mon, Mar 26, 2018 at 8:09 PM, Sebastian Berg <sebastian@sipsolutio ns.net> wrote:
On Mon, 20180326 at 17:40 +0000, Eric Wieser wrote:
The difficulty in supporting object arrays is that
func.reduce(arr,
initial=func.identity) and func.reduce(arr) have different
meanings 
whereas with the current patch, they are equivalent.
True, but the current meaning is:
func.reduce(arr, intial=<NoValue>, default=func.identity)
in the case for object dtype. Luckily for normal dtypes, func.identity is both the correct default "default" and a noop for initial. Thus the name "identity" kinda works there. I am also not really sure that both kwargs would make real sense (plus initial probably disallows default...), but I got some feeling that the "default" meaning may be even more useful to simplify special casing the empty case.
Anyway, still just pointing out that I it gives me some headaches to see such a special case for objects :(.
 Sebastian
On Mon, 26 Mar 2018 at 10:10 Sebastian Berg <sebastian@sipsolutio
ns.n
et> wrote:
On Mon, 20180326 at 12:59 0400, Hameer Abbasi wrote:
That may be complicated. Currently, the identity isn't used
in
object
dtype reductions. We may need to change that, which could
cause a
whole lot of other backwards incompatible changes. For
example,
sum
actually including zero in object reductions. Or we could
pass in
a
flag saying an initializer was passed in to change that
behaviour. If
this is agreed upon and someone is kind enough to point me to
the
code, I'd be willing to make this change.
I realize the implication, I am not suggesting to change the default behaviour (when no initial=... is passed), I would think about deprecating it, but probably only if we also have the `default` argument, since otherwise you cannot replicate the old
behaviour.
What I think I would like to see is to change how it works if
(and
only if) the initializer is passed in. Yes, this will require
holding on
to some extra information since you will have to know/remember
whether
the "identity" was passed in or defined otherwise.
I did not check the code, but I would hope that it is not
awfully
tricky to do that.
 Sebastian
PS: A side note, but I see your emails as a single block of
text
with no/broken newlines.
On 26/03/2018 at 18:54, Sebastian wrote: On Mon, 20180326 at 18:48 +0200, Sebastian
Berg
wrote: On Mon, 20180326 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. In other words: I am very
very
much
in favor if you get rid that object dtype special case. I
frankly
not
see why not (except that it needs a bit more code change). If
given
explicitly, we might as well force the use and not do the
funny
stuff
which is designed to be more type agnostic! If it happens to
fail
due
to not being type agnostic, it will at least fail loudly. If
you
leave that object special case I am *very* hesitant about it. That
I
think
I would like a `default` argument as well, is another issue and
it
can
wait to another day.  Sebastian  Sebastian On 26/03/2018 at
17:45,
Sebastian wrote: On Mon, 20180326 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 <sebastian@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, 20180326 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 <sebastian@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, 20180326 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 shoyer@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 > > > > > > > NumPyDiscussion@python.org > > >
https://mail.python.org/mailman/listinfo/numpydiscussion > >
_______________________________________________ > > > > > NumPyDiscussion mailing list > > > > > > NumPyDiscussion@py
thon
.o
rg https://mail.python.org/mailman/listinfo/numpydiscussi on _______________________________________________ > > > > NumPyDiscussion mailing list > > > > > NumPyDiscussion@pyth
on.o
rg
https://mail.python.org/mailman/listinfo/numpydiscussion > > _______________________________________________ > > > > NumPyDiscussion mailing list > > > > NumPyDiscussion@python
.org
https://mail.python.org/mailman/listinfo/numpydiscussion > >
_______________________________________________ > > > NumPy Discussion mailing list > > > NumPyDiscussion@python.org > >
https://mail.python.org/mailman/listinfo/numpydiscussion > >
_______________________________________________ > > NumPy
Discussion
mailing list > > NumPyDiscussion@python.org > > https://mail.python.org/mailman/listinfo/numpydiscussion > > _______________________________________________ > NumPy
Discussion
mailing list > NumPyDiscussion@python.org > https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPy
Discussion
mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPy
Discussion
mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPy
Discussion
mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPy
Discussion
mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion____
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
Huh, looks like it has different names in different places. `help(functools.reduce)` shows "initial".
On Mon, Mar 26, 2018, 02:57 Hameer Abbasi einstein.edison@gmail.com wrote:
It calls it `initializer`  See https://docs.python.org/3.5/library/functools.html#functools.reduce
Sent from Astro https://www.helloastro.com 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 shoyer@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+numpy@gmail.com wrote:
To reiterate my comments in the issue  I'm in favor of this.
It seems seem especially valuable for identityless 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@gmail.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 identityless 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 https://www.helloastro.com for Mac
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
participants (5)

Benjamin Root

Eric Wieser

Hameer Abbasi

Sebastian Berg

Stephan Hoyer