[
https://issues.apache.org/jira/browse/SPARK-25782?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Erik Erlandson updated SPARK-25782:
-----------------------------------
Target Version/s: 3.0.0
Component/s: ML
Issue Type: New Feature (was: Improvement)
> 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.
--
This message was sent by Atlassian JIRA
(v7.6.3#76005)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
