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From "Yanbo Liang (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-17835) Optimize NaiveBayes mllib wrapper to eliminate extra pass on data
Date Sat, 08 Oct 2016 06:45:20 GMT

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

Yanbo Liang updated SPARK-17835:
--------------------------------
    Issue Type: Improvement  (was: Bug)

> 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 ml 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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