Would it make sense to have an `na_are_zero` flag? Since null checking is not without cost, it might be helpful to some algorithms, if the content "underneath" the nulls is zero. For example in means, or scalar products and thus matrix multiplication, knowing that the array has zeros where the na's are, would allow these algorithms to pretend that there are no na's. Since setting all nulls to zero in a matrix of n columns and n rows costs O(n^2), it would make sense to set them all to zero before matrix multiplication i.e. O(n^3) and similarly expensive algorithms. If there was a `na_are_zero` flag, other algorithms could later utilize this work already being done. Algorithms which change the data and violate this contract, would only need to reset the flag. And in some use cases, it might be possible to use idle time of the computer to "clean up" the na's, preparing for the next query.
Felix ---------- Forwarded message --------- From: Wes McKinney <wesmck...@gmail.com> Date: Sun, 5 Apr 2020 at 22:31 Subject: Re: Attn: Wes, Re: Masked Arrays To: <u...@arrow.apache.org> As I recall the contents "underneath" have been discussed before and the consensus was that the contents are not specified. If you'e like to make a proposal to change something I would suggest raising it on dev@arrow.apache.org On Sun, Apr 5, 2020 at 1:56 PM Felix Benning <felix.benn...@gmail.com> wrote: > > Follow up: Do you think it would make sense to have an `na_are_zero` flag? Since it appears that the baseline (naively assuming there are no null values) is still a bit faster than equally optimized null value handling algorithms. So you might want to make the assumption, that all null values are set to zero in the array (instead of undefined). This would allow for very fast means, scalar products and thus matrix multiplication which ignore nas. And in case of matrix multiplication, you might prefer sacrificing an O(n^2) effort to set all null entries to zero before multiplying. And assuming you do not overwrite this data, you would be able to reuse that assumption in later computations with such a flag. > In some use cases, you might even be able to utilize unused computing resources for this task. I.e. clean up the nulls while the computer is not used, preparing for the next query. > > > On Sun, 5 Apr 2020 at 18:34, Felix Benning <felix.benn...@gmail.com> wrote: >> >> Awesome, that was exactly what I was looking for, thank you! >> >> On Sun, 5 Apr 2020 at 00:40, Wes McKinney <wesmck...@gmail.com> wrote: >>> >>> I wrote a blog post a couple of years about this >>> >>> https://wesmckinney.com/blog/bitmaps-vs-sentinel-values/ >>> >>> Pasha Stetsenko did a follow-up analysis that showed that my >>> "sentinel" code could be significantly improved, see: >>> >>> https://github.com/st-pasha/microbench-nas/blob/master/README.md >>> >>> Generally speaking in Apache Arrow we've been happy to have a uniform >>> representation of nullness across all types, both primitive (booleans, >>> numbers, or strings) and nested (lists, structs, unions, etc.). Many >>> computational operations (like elementwise functions) need not concern >>> themselves with the nulls at all, for example, since the bitmap from >>> the input array can be passed along (with zero copy even) to the >>> output array. >>> >>> On Sat, Apr 4, 2020 at 4:39 PM Felix Benning <felix.benn...@gmail.com> wrote: >>> > >>> > Does anyone have an opinion (or links) about Bitpattern vs Masked Arrays for NA implementations? There seems to have been a discussion about that in the numpy community in 2012 https://numpy.org/neps/nep-0026-missing-data-summary.html without an apparent result. >>> > >>> > Summary of the Summary: >>> > - The Bitpattern approach reserves one bitpattern of any type as na, the only type not having spare bitpatterns are integers which means this decreases their range by one. This approach is taken by R and was regarded as more performant in 2012. >>> > - The Mask approach was deemed more flexible, since it would allow "degrees of missingness", and also cleaner/easier implementation. >>> > >>> > Since bitpattern checks would probably disrupt SIMD, I feel like some calculations (e.g. mean) would actually benefit more, from setting na values to zero, proceeding as if they were not there, and using the number of nas in the metadata to adjust the result. This of course does not work if two columns are used (e.g. scalar product), which is probably more important. >>> > >>> > Was using Bitmasks in Arrow a conscious performance decision? Or was the decision only based on the fact, that R and Bitpattern implementations in general are a niche, which means that Bitmasks are more compatible with other languages? >>> > >>> > I am curious about this topic, since the "lack of proper na support" was cited as the reason, why Python would never replace R in statistics. >>> > >>> > Thanks, >>> > >>> > Felix >>> > >>> > >>> > On 31.03.20 14:52, Joris Van den Bossche wrote: >>> > >>> > Note that pandas is starting to use a notion of "masked arrays" as well, for example for its nullable integer data type, but also not using the np.ma masked array, but a custom implementation (for technical reasons in pandas this was easier). >>> > >>> > Also, there has been quite some discussion last year in numpy about a possible re-implementation of a MaskedArray, but using numpy's protocols (`__array_ufunc__`, `__array_function__` etc), instead of being a subclass like np.ma now is. See eg https://mail.python.org/pipermail/numpy-discussion/2019-June/079681.html. >>> > >>> > Joris >>> > >>> > On Mon, 30 Mar 2020 at 18:57, Daniel Nugent <nug...@gmail.com> wrote: >>> >> >>> >> Ok. That actually aligns closely to what I'm familiar with. Good to know. >>> >> >>> >> Thanks again for taking the time to respond, >>> >> >>> >> -Dan Nugent >>> >> >>> >> >>> >> On Mon, Mar 30, 2020 at 12:38 PM Wes McKinney <wesmck...@gmail.com> wrote: >>> >>> >>> >>> Social and technical reasons I guess. Empirically it's just not used much. >>> >>> >>> >>> You can see my comments about numpy.ma in my 2010 paper about pandas >>> >>> >>> >>> https://conference.scipy.org/proceedings/scipy2010/pdfs/mckinney.pdf >>> >>> >>> >>> At least in 2010, there were notable performance problems when using >>> >>> MaskedArray for computations >>> >>> >>> >>> "We chose to use NaN as opposed to using NumPy MaskedArrays for >>> >>> performance reasons (which are beyond the scope of this paper), as NaN >>> >>> propagates in floating-point operations in a natural way and can be >>> >>> easily detected in algorithms." >>> >>> >>> >>> On Mon, Mar 30, 2020 at 11:20 AM Daniel Nugent <nug...@gmail.com> wrote: >>> >>> > >>> >>> > Thanks! Since I'm just using it to jump to Arrow, I think I'll stick with it. >>> >>> > >>> >>> > Do you have any feelings about why Numpy's masked arrays didn't gain favor when many data representation formats explicitly support nullity (including Arrow)? Is it just that not carrying nulls in computations forward is preferable (that is, early filtering/value filling was easier)? >>> >>> > >>> >>> > -Dan Nugent >>> >>> > >>> >>> > >>> >>> > On Mon, Mar 30, 2020 at 11:40 AM Wes McKinney <wesmck...@gmail.com> wrote: >>> >>> >> >>> >>> >> On Mon, Mar 30, 2020 at 8:31 AM Daniel Nugent <nug...@gmail.com> wrote: >>> >>> >> > >>> >>> >> > Didn’t want to follow up on this on the Jira issue earlier since it's sort of tangential to that bug and more of a usage question. You said: >>> >>> >> > >>> >>> >> > > I wouldn't recommend building applications based on them nowadays since the level of support / compatibility in other projects is low. >>> >>> >> > >>> >>> >> > In my case, I am using them since it seemed like a straightforward representation of my data that has nulls, the format I’m converting from has zero cost numpy representations, and converting from an internal format into Arrow in memory structures appears zero cost (or close to it) as well. I guess I can just provide the mask as an explicit argument, but my original desire to use it came from being able to exploit numpy.ma.concatenate in a way that saved some complexity in implementation. >>> >>> >> > >>> >>> >> > Since Arrow itself supports masking values with a bitfield, is there something intrinsic to the notion of array masks that is not well supported? Or do you just mean the specific numpy MaskedArray class? >>> >>> >> > >>> >>> >> >>> >>> >> I mean just the numpy.ma module. Not many Python computing projects >>> >>> >> nowadays treat MaskedArray objects as first class citizens. Depending >>> >>> >> on what you need it may or may not be a problem. pyarrow supports >>> >>> >> ingesting from MaskedArray as a convenience, but it would not be >>> >>> >> common in my experience for a library's APIs to return MaskedArrays. >>> >>> >> >>> >>> >> > If this is too much of a numpy question rather than an arrow question, could you point me to where I can read up on masked array support or maybe what the right place to ask the numpy community about whether what I'm doing is appropriate or not. >>> >>> >> > >>> >>> >> > Thanks, >>> >>> >> > >>> >>> >> > >>> >>> >> > -Dan Nugent
