[jira] [Updated] (SPARK-4285) Transpose RDD[Vector] to column store for ML

["Joseph K. Bradley (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-4285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Joseph K. Bradley updated SPARK-4285:
-------------------------------------
    Target Version/s: 1.5.0  (was: 1.4.0)

> Transpose RDD[Vector] to column store for ML
> --------------------------------------------
>
>                 Key: SPARK-4285
>                 URL: https://issues.apache.org/jira/browse/SPARK-4285
>             Project: Spark
>          Issue Type: Sub-task
>          Components: MLlib
>            Reporter: Joseph K. Bradley
>            Assignee: Joseph K. Bradley
>            Priority: Minor
>
> For certain ML algorithms, a column store is more efficient than a row store (which is currently used everywhere).  E.g., deep decision trees can be faster to train when partitioning by features.
> Proposal: Provide a method with the following API (probably in util/):
> ```
> def rowToColumnStore(data: RDD[Vector]): RDD[(Int, Vector)]
> ```
> The input Vectors will be data rows/instances, and the output Vectors will be columns/features paired with column/feature indices.
> **Question**: Is it important to maintain matrix structure?  That is, should output Vectors in the same partition be adjacent columns in the matrix?



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