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https://issues.apache.org/jira/browse/SPARK-4285?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14209605#comment-14209605
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SUMANTH B B N commented on SPARK-4285:
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[~josephkb]
i have tried to implement this method,u can have a look at it
https://github.com/bbnsumanth/transposing/blob/master/src/main/scala/Transpose.scala
> 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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