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https://issues.apache.org/jira/browse/SPARK-11136?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xusen Yin updated SPARK-11136:
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Description:
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.
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.
was:
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.
> 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.
> 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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