[jira] [Commented] (SPARK-25782) Add PCA Aggregator to support grouping

["Erik Erlandson (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-25782?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16657394#comment-16657394 ] 

Erik Erlandson commented on SPARK-25782:
----------------------------------------

Thanks [~mttsndrs]!

I agree it makes sense to support full Dataset aggregation functionality via a UDAF.

> Add PCA Aggregator to support grouping
> --------------------------------------
>
>                 Key: SPARK-25782
>                 URL: https://issues.apache.org/jira/browse/SPARK-25782
>             Project: Spark
>          Issue Type: New Feature
>          Components: ML, MLlib
>    Affects Versions: 2.3.2
>            Reporter: Matt Saunders
>            Priority: Minor
>
> I built an Aggregator that computes PCA on grouped datasets. I wanted to use the PCA functions provided by MLlib, but they only work on a full dataset, and I needed to do it on a grouped dataset (like a RelationalGroupedDataset). 
> So I built a little Aggregator that can do that, here's an example of how it's called:
> {noformat}
> val pcaAggregation = new PCAAggregator(vectorColumnName).toColumn
> // For each grouping, compute a PCA matrix/vector
> val pcaModels = inputData
>   .groupBy(keys:_*)
>   .agg(pcaAggregation.as(pcaOutput)){noformat}
> I used the same algorithms under the hood as RowMatrix.computePrincipalComponentsAndExplainedVariance, though this works directly on Datasets without converting to RDD first.
> I've seen others who wanted this ability (for example on Stack Overflow) so I'd like to contribute it if it would be a benefit to the larger community. If there is interest, I will prepare the code for a pull request.



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