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https://issues.apache.org/jira/browse/SPARK-6682?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Joseph K. Bradley updated SPARK-6682:
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Target Version/s: 1.6.0 (was: 1.5.0)
> Deprecate static train and use builder instead for Scala/Java
> -------------------------------------------------------------
>
> Key: SPARK-6682
> URL: https://issues.apache.org/jira/browse/SPARK-6682
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.3.0
> Reporter: Joseph K. Bradley
>
> In MLlib, we have for some time been unofficially moving away from the old
> static train() methods and moving towards builder patterns. This JIRA is to
> discuss this move and (hopefully) make it official.
> "Old static train()" API:
> {code}
> val myModel = NaiveBayes.train(myData, ...)
> {code}
> "New builder pattern" API:
> {code}
> val nb = new NaiveBayes().setLambda(0.1)
> val myModel = nb.train(myData)
> {code}
> Pros of the builder pattern:
> * Much less code when algorithms have many parameters. Since Java does not
> support default arguments, we required *many* duplicated static train()
> methods (for each prefix set of arguments).
> * Helps to enforce default parameters. Users should ideally not have to even
> think about setting parameters if they just want to try an algorithm quickly.
> * Matches spark.ml API
> Cons of the builder pattern:
> * In Python APIs, static train methods are more "Pythonic."
> Proposal:
> * Scala/Java: We should start deprecating the old static train() methods. We
> must keep them for API stability, but deprecating will help with API
> consistency, making it clear that everyone should use the builder pattern.
> As we deprecate them, we should make sure that the builder pattern supports
> all parameters.
> * Python: Keep static train methods.
> CC: [~mengxr]
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