[
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.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
