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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (MAHOUT-1541) Create CLI Driver for Spark Cooccurrence Analysis
Date Sun, 20 Jul 2014 17:32:39 GMT

    [ https://issues.apache.org/jira/browse/MAHOUT-1541?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14067990#comment-14067990
] 

ASF GitHub Bot commented on MAHOUT-1541:
----------------------------------------

Github user avati commented on a diff in the pull request:

    https://github.com/apache/mahout/pull/31#discussion_r15150836
  
    --- Diff: spark/src/main/scala/org/apache/mahout/sparkbindings/drm/CheckpointedDrmSpark.scala
---
    @@ -46,6 +46,19 @@ class CheckpointedDrmSpark[K: ClassTag](
       private var cached: Boolean = false
       override val context: DistributedContext = rdd.context
     
    +  /**
    +   * Adds the equivalent of blank rows to the sparse CheckpointedDrm, which only changes
the
    +   * [[org.apache.mahout.sparkbindings.drm
    +.CheckpointedDrmSpark#nrow]] value.
    +   * No physical changes are made to the underlying rdd, now blank rows are added as
would be done with rbind(blankRows)
    +   * @param n number to increase row cardinality by
    +   * @note should be done before any BLAS optimizer actions are performed on the matrix
or you'll get unpredictable
    +   *       results.
    +   */
    +  override def addToRowCardinality(n: Int): CheckpointedDrm[K] = {
    +    assert(n > -1)
    +    new CheckpointedDrmSpark[K](rdd, nrow + n, ncol, _cacheStorageLevel )
    +  }
    --- End diff --
    
    This fixes the immutability problem, but the missing rows still create the following issues:
    
    - AewScalar: math errors
    - AewB: java exception
    - CbindAB: java exception
    
    All three are non-trivial to fix (i.e no one liner fixes).



> Create CLI Driver for Spark Cooccurrence Analysis
> -------------------------------------------------
>
>                 Key: MAHOUT-1541
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-1541
>             Project: Mahout
>          Issue Type: New Feature
>          Components: CLI
>            Reporter: Pat Ferrel
>            Assignee: Pat Ferrel
>
> Create a CLI driver to import data in a flexible manner, create an IndexedDataset with
BiMap ID translation dictionaries, call the Spark CooccurrenceAnalysis with the appropriate
params, then write output with external IDs optionally reattached.
> Ultimately it should be able to read input as the legacy mr does but will support reading
externally defined IDs and flexible formats. Output will be of the legacy format or text files
of the user's specification with reattached Item IDs. 
> Support for legacy formats is a question, users can always use the legacy code if they
want this. Internal to the IndexedDataset is a Spark DRM so pipelining can be accomplished
without any writing to an actual file so the legacy sequence file output may not be needed.
> Opinions?



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