Yeah, I already implemented these partitioners for my use case (I just pasted the classnames/docstrings for them) and I used both combiners.Top and combiners.Sample.
In fact, before writing these partitioners I had misunderstood those combiners and thought they would partition my pcollections. Not sure if that might be a common pitfall. On Thu, Oct 19, 2023 at 3:32 PM Anand Inguva via dev <dev@beam.apache.org> wrote: > FYI, there is a Top transform[1] that will fetch the greatest n elements > in Python SDK. It is not a partitioner but It may be useful for your > reference. > > [1] > https://github.com/apache/beam/blob/68e9c997a9085b0cb045238ae406d534011e7c21/sdks/python/apache_beam/transforms/combiners.py#L191 > > On Thu, Oct 19, 2023 at 3:21 PM Joey Tran <joey.t...@schrodinger.com> > wrote: > >> Yes, both need to be small enough to fit into state. >> >> Yeah a percentage sampler would also be great, we have a bunch of use >> cases for that ourselves. Not sure if it'd be too clever, but I was >> imagining three public sampling partitioners: FixedSample, >> PercentageSample, and Sample. Sample could automatically choose between >> FixedSample and PercentageSample based on whether a percentage is given or >> a large `n` is given. >> >> For `PercentageSample`, I was imagining we'd just take a count of the >> number of elements and then assign every element a `rand` and keep the ones >> that are larger than `n / Count(inputs)` (or percentage). For runners that >> have fast counting, it should perform quickly. Open to other ideas though. >> >> Cheers, >> Joey >> >> >> >> On Thu, Oct 19, 2023 at 3:10 PM Danny McCormick via dev < >> dev@beam.apache.org> wrote: >> >>> I'm interested adding something like this, I could see these being >>> generally useful for a number of cases (one that immediately comes to mind >>> is partitioning datasets into train/test/validation sets and writing each >>> to a different place). >>> >>> I'm assuming Top (or FixedSample) needs to be small enough to fit into >>> state? I would also be interested in being able to do percentages as well >>> (something like partitioners.Sample(percent=10)), though that might be much >>> more challenging for an unbounded data set (maybe we could do something as >>> simple as a probabilistic target_percentage). >>> >>> Happy to help review a design doc or PR. >>> >>> Thanks, >>> Danny >>> >>> On Thu, Oct 19, 2023 at 10:06 AM Joey Tran <joey.t...@schrodinger.com> >>> wrote: >>> >>>> Hey all, >>>> >>>> While writing a few pipelines, I was surprised by how few partitioners >>>> there were in the python SDK. I wrote a couple that are pretty generic and >>>> possibly generally useful. Just wanted to do a quick poll to see if they >>>> seem useful enough to be in the sdk's library of transforms. If so, I can >>>> put together a PTransform Design Doc[1] for them. Just wanted to confirm >>>> before spending time on the doc. >>>> >>>> Here are the two that I wrote, I'll just paste the class names and >>>> docstrings: >>>> >>>> class FixedSample(beam.PTransform): >>>> """ >>>> A PTransform that takes a PCollection and partitions it into two >>>> PCollections. >>>> The first PCollection is a random sample of the input PCollection, >>>> and the >>>> second PCollection is the remaining elements of the input >>>> PCollection. >>>> >>>> This is useful for creating holdout / test sets in machine learning. >>>> >>>> Example usage: >>>> >>>> >>> with beam.Pipeline() as p: >>>> ... sample, remaining = (p >>>> ... | beam.Create(list(range(10))) >>>> ... | partitioners.FixedSample(3)) >>>> ... # sample will contain three randomly selected elements >>>> from the >>>> ... # input PCollection >>>> ... # remaining will contain the remaining seven elements >>>> >>>> """ >>>> >>>> class Top(beam.PTransform): >>>> """ >>>> A PTransform that takes a PCollection and partitions it into two >>>> PCollections. >>>> The first PCollection contains the largest n elements of the input >>>> PCollection, >>>> and the second PCollection contains the remaining elements of the >>>> input >>>> PCollection. >>>> >>>> Parameters: >>>> n: The number of elements to take from the input PCollection. >>>> key: A function that takes an element of the input PCollection >>>> and returns >>>> a value to compare for the purpose of determining the top n >>>> elements, >>>> similar to Python's built-in sorted function. >>>> reverse: If True, the top n elements will be the n smallest >>>> elements of the >>>> input PCollection. >>>> >>>> Example usage: >>>> >>>> >>> with beam.Pipeline() as p: >>>> ... top, remaining = (p >>>> ... | beam.Create(list(range(10))) >>>> ... | partitioners.Top(3)) >>>> ... # top will contain [7, 8, 9] >>>> ... # remaining will contain [0, 1, 2, 3, 4, 5, 6] >>>> >>>> """ >>>> >>>> They're basically partitioner versions of the aggregationers Top and >>>> Sample >>>> >>>> Best, >>>> Joey >>>> >>>> >>>> [1] >>>> https://docs.google.com/document/d/1NpCipgvT6lMgf1nuuPPwZoKp5KsteplFancGqOgy8OY/edit#heading=h.x9snb54sjlu9 >>>> >>>
