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Posted to issues@spark.apache.org by "zhengruifeng (JIRA)" <ji...@apache.org> on 2019/06/11 11:00:00 UTC
[jira] [Comment Edited] (SPARK-24875) MulticlassMetrics should
offer a more efficient way to compute count by label
[ https://issues.apache.org/jira/browse/SPARK-24875?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16860922#comment-16860922 ]
zhengruifeng edited comment on SPARK-24875 at 6/11/19 10:59 AM:
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The <label, score> dataset is usually much smaller than the training dataset containing <features>,
if the score data is to huge to perform a simple op like countByValue, how could you train/evaluate the model?
I doubt whether it is worth to apply a approximation.
was (Author: podongfeng):
The <label, score> dataset is usually much smaller than the training dataset containing <features>,
if the score data is to huge to perform a simple op like countByValue, how could you train the model?
I doubt whether it is worth to apply a approximation.
> MulticlassMetrics should offer a more efficient way to compute count by label
> -----------------------------------------------------------------------------
>
> Key: SPARK-24875
> URL: https://issues.apache.org/jira/browse/SPARK-24875
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 2.3.1
> Reporter: Antoine Galataud
> Priority: Minor
>
> Currently _MulticlassMetrics_ calls _countByValue_() to get count by class/label
> {code:java}
> private lazy val labelCountByClass: Map[Double, Long] = predictionAndLabels.values.countByValue()
> {code}
> If input _RDD[(Double, Double)]_ is huge (which can be the case with a large test dataset), it will lead to poor execution performance.
> One option could be to allow using _countByValueApprox_ (could require adding an extra configuration param for MulticlassMetrics).
> Note: since there is no equivalent of _MulticlassMetrics_ in new ML library, I don't know how this could be ported there.
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