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