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From "Joseph K. Bradley (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-6682) Deprecate static train and use builder instead for Scala/Java
Date Fri, 03 Apr 2015 17:54:54 GMT

     [ https://issues.apache.org/jira/browse/SPARK-6682?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Joseph K. Bradley updated SPARK-6682:
-------------------------------------
    Description: 
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]

  was:
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:
* 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]


> 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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