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.benning@gmail.com> wrote:
> Awesome, that was exactly what I was looking for, thank you!
>
> On Sun, 5 Apr 2020 at 00:40, Wes McKinney <wesmckinn@gmail.com> wrote:
>
>> I wrote a blog post a couple of years about this
>>
>> https://wesmckinney.com/blog/bitmapsvssentinelvalues/
>>
>> Pasha Stetsenko did a followup analysis that showed that my
>> "sentinel" code could be significantly improved, see:
>>
>> https://github.com/stpasha/microbenchnas/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.benning@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/nep0026missingdatasummary.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 reimplementation 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/numpydiscussion/2019June/079681.html.
>> >
>> > Joris
>> >
>> > On Mon, 30 Mar 2020 at 18:57, Daniel Nugent <nugend@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 <wesmckinn@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 floatingpoint operations in a natural way and can be
>> >>> easily detected in algorithms."
>> >>>
>> >>> On Mon, Mar 30, 2020 at 11:20 AM Daniel Nugent <nugend@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 <wesmckinn@gmail.com>
>> wrote:
>> >>> >>
>> >>> >> On Mon, Mar 30, 2020 at 8:31 AM Daniel Nugent <nugend@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
>>
>
