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https://issues.apache.org/jira/browse/SPARK-2372?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14094668#comment-14094668
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Kyle Ellrott edited comment on SPARK-2372 at 8/13/14 6:06 AM:
--------------------------------------------------------------
GroupedBinaryClassificationMetrics has been added to the pull request connected
to this issue.
GroupedBinaryClassificationMetrics is an re-write of the
BinaryClassificationMetrics methods, but it work on a RDD[KEY,(Double,Double)]
structure (rather then the RDD[(Double,Double)] that
BinaryClassificationMetrics takes), where KEY is a generic that will be the
type of the key used to identified the data set.
A unit test is included do validate these function work in the same way as the
BinaryClassificationMetrics implementations.
https://github.com/kellrott/spark/commit/dcabb2f6a39c0940afc39e809a50601f46e50162
was (Author: kellrott):
GroupedBinaryClassificationMetrics has been added to the pull request connected
to this issue.
GroupedBinaryClassificationMetrics is an re-write of the
BinaryClassificationMetrics methods, but it work on a RDD[KEY,(Double,Double)]
structure (rather then the RDD[(Double,Double)] that
BinaryClassificationMetrics takes), where KEY is a generic that will be the
type of the key used to identified the data set. Now methods return
Map[KEY,Double], with a different score for each data set, rather then a single
'Double'
A unit test is included do validate these function work in the same way as the
BinaryClassificationMetrics implementations.
https://github.com/kellrott/spark/commit/dcabb2f6a39c0940afc39e809a50601f46e50162
> Grouped Optimization/Learning
> -----------------------------
>
> Key: SPARK-2372
> URL: https://issues.apache.org/jira/browse/SPARK-2372
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Affects Versions: 1.0.1, 1.1.0, 1.0.2
> Reporter: Kyle Ellrott
>
> The purpose of this patch is the enable MLLib to better handle scenarios
> where the user would want to do learning on multiple feature/label sets at
> the same time. Rather then schedule each learning task separately, this patch
> lets the user create a single RDD with an Int key to represent the 'group'
> sets of entries belong to.
> This patch establishing the GroupedOptimizer trait, for which
> GroupedGradientDescent has been implemented. This systems differs from the
> original Optimizer trait in that the original optimize method accepted
> RDD[(Int, Vector)] the new GroupedOptimizer accepts RDD[(Int, (Double,
> Vector))].
> The difference is that the GroupedOptimizer uses a 'group' ID key in the RDD
> to multiplex multiple optimization operations in the same RDD.
> This patch also establishes the GroupedGeneralizedLinearAlgorithm trait, for
> which the 'run' method has had the RDD[LabeledPoint] input replaced with
> RDD[(Int,LabeledPoint)].
> This patch also provides a unit test and utility to take the results of
> MLUtils.kFold and turn it into a single grouped RDD, ready for simultaneous
> learning.
> https://github.com/apache/spark/pull/1292
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