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https://issues.apache.org/jira/browse/SPARK-17835?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15557405#comment-15557405
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Apache Spark commented on SPARK-17835:
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User 'yanboliang' has created a pull request for this issue:
https://github.com/apache/spark/pull/15402
> 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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