[jira] [Commented] (SPARK-11136) Warm-start support for ML estimator

["Xusen Yin (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-11136?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14960063#comment-14960063 ] 

Xusen Yin commented on SPARK-11136:
-----------------------------------

I have already linked all related issues. [~josephkb] Which kind of methods of supporting warm-start do you prefer? Or other feasible suggestions? In [~jayants]'s code of KMeans warm-start we can see the 3rd implementation.

> Warm-start support for ML estimator
> -----------------------------------
>
>                 Key: SPARK-11136
>                 URL: https://issues.apache.org/jira/browse/SPARK-11136
>             Project: Spark
>          Issue Type: Sub-task
>          Components: ML
>            Reporter: Xusen Yin
>            Priority: Minor
>
> The current implementation of Estimator does not support warm-start fitting, i.e. estimator.fit(data, params, partialModel). But first we need to add warm-start for all ML estimators. This is an umbrella JIRA to add support for the warm-start estimator. 
> Possible solutions:
> 1. Add warm-start fitting interface like def fit(dataset: DataFrame, initModel: M, paramMap: ParamMap): M
> 2. Treat model as a special parameter, passing it through ParamMap. e.g. val partialModel: Param[Option[M]] = new Param(...). In the case of model existing, we use it to warm-start, else we start the training process from the beginning.



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