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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-9245) DistributedLDAModel predict top topic per doc-term instance
Date Thu, 20 Aug 2015 03:50:45 GMT

    [ https://issues.apache.org/jira/browse/SPARK-9245?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14704249#comment-14704249
] 

Apache Spark commented on SPARK-9245:
-------------------------------------

User 'jkbradley' has created a pull request for this issue:
https://github.com/apache/spark/pull/8329

> DistributedLDAModel predict top topic per doc-term instance
> -----------------------------------------------------------
>
>                 Key: SPARK-9245
>                 URL: https://issues.apache.org/jira/browse/SPARK-9245
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>            Reporter: Joseph K. Bradley
>            Assignee: Joseph K. Bradley
>   Original Estimate: 48h
>  Remaining Estimate: 48h
>
> For each (document, term) pair, return top topic.  Note that instances of (doc, term)
pairs within a document (a.k.a. "tokens") are exchangeable, so we should provide an estimate
per document-term, rather than per token.
> Synopsis for DistributedLDAModel:
> {code}
> /** @return RDD of (doc ID, vector of top topic index for each term) */
> def topTopicAssignments: RDD[(Long, Vector)]
> {code}
> Note that using Vector will let us have a sparse encoding which is Java-friendly.



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