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https://issues.apache.org/jira/browse/MAHOUT-1541?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14068936#comment-14068936
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ASF GitHub Bot commented on MAHOUT-1541:
----------------------------------------
Github user pferrel commented on a diff in the pull request:
https://github.com/apache/mahout/pull/31#discussion_r15184775
--- 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 --
I see no fundamental reason for these not to work but it may not be part of
the DRM contract. So maybe I'll make a feature request Jira to support this.
In the meantime rbind will not solve this because A will have missing rows
at the end but B may have them throughout--let alone some future C. So I think
reading in all data into one drm with one row and column id space then chopping
into two or more drms based on column ranges should give us empty rows where
they are needed (I certainly hope so or I'm in trouble). Will have to keep
track of which column ids go in which slice but that's doable.
> 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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