[jira] [Assigned] (SPARK-17835) Optimize NaiveBayes mllib wrapper to eliminate extra pass on data

["Apache Spark (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-17835?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-17835:
------------------------------------

    Assignee:     (was: Apache Spark)

> Optimize NaiveBayes mllib wrapper to eliminate extra pass on data
> -----------------------------------------------------------------
>
>                 Key: SPARK-17835
>                 URL: https://issues.apache.org/jira/browse/SPARK-17835
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML, MLlib
>            Reporter: Yanbo Liang
>
> SPARK-14077 copied the {{NaiveBayes}} implementation from mllib to ml and left mllib as a wrapper. However, there are some difference between mllib and ml to handle {{labels}}:
> * mllib allow input labels as {-1, +1}, however, ml assumes the input labels in range [0, numClasses).
> * mllib {{NaiveBayesModel}} expose {{labels}} but ml did not due to the assumption mention above.
> During the copy in SPARK-14077, we use {{val labels = data.map(_.label).distinct().collect().sorted}} to get the distinct labels firstly, and then feed to training. It inovlves another extra Spark job compared with the original implementation. Since {{NaiveBayes}} only do one aggregation during training, add another one seems not efficient. We can get the labels in a single pass along with {{NaiveBayes}} training and send them to MLlib side.



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