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https://issues.apache.org/jira/browse/MAHOUT-1896?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15762885#comment-15762885
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ASF GitHub Bot commented on MAHOUT-1896:
----------------------------------------
Github user andrewpalumbo commented on a diff in the pull request:
https://github.com/apache/mahout/pull/263#discussion_r93155654
--- Diff:
spark/src/main/scala/org/apache/mahout/sparkbindings/package.scala ---
@@ -141,6 +144,42 @@ package object sparkbindings {
new CheckpointedDrmSpark[K](rddInput = rdd, _nrow = nrow, _ncol =
ncol, cacheHint = cacheHint,
_canHaveMissingRows = canHaveMissingRows)
+ /** A drmWrap version that takes an
RDD[org.apache.spark.mllib.regression.LabeledPoint]
+ * returns a DRM where column 0 is the label */
+ def drmWrapMLLibLabeledPoint(rdd: RDD[LabeledPoint],
+ nrow: Long = -1,
+ ncol: Int = -1,
+ cacheHint: CacheHint.CacheHint = CacheHint.NONE,
+ canHaveMissingRows: Boolean = false):
CheckpointedDrm[Int] = {
+ val drmRDD: DrmRdd[Int] = rdd.zipWithIndex.map( lv => (lv._2.toInt,
+ new org.apache.mahout.math.DenseVector( Array(lv._1.label) ++
lv._1.features.toArray)) )
--- End diff --
I don't think that this should always be dense
> Add convenience methods for interacting with Spark ML
> -----------------------------------------------------
>
> Key: MAHOUT-1896
> URL: https://issues.apache.org/jira/browse/MAHOUT-1896
> Project: Mahout
> Issue Type: Bug
> Affects Versions: 0.12.2
> Reporter: Trevor Grant
> Assignee: Trevor Grant
> Priority: Minor
> Fix For: 0.13.0
>
>
> Currently the method for ingesting RDDs to DRM is `drmWrap`. This is a
> flexible method, however there are many cases when the RDD to be wrapped is
> either RDD[org.apache.spark.mllib.lingalg.Vector],
> RDD[org.apache.spark.mllib.regression.LabeledPoint], or DataFrame[Row] (as is
> the case when working with SparkML. It makes sense to create convenience
> methods for converting these types to DRM.
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