[jira] [Commented] (SPARK-4722) StreamingLinearRegression should return a DStream of weights when calling trainOn

["Xiangrui Meng (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-4722?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14232920#comment-14232920 ] 

Xiangrui Meng commented on SPARK-4722:
--------------------------------------

[~Arthur][ You can use `StreamingLinearRegression.model` to get the latest model. It may be expensive and unnecessary to make predictOn return a DStream of model weights. If you want to re-use the previously trained model, you save the last model coefficients in the first run and then set initial weights in the second run.

> StreamingLinearRegression should return a DStream of weights when calling trainOn
> ---------------------------------------------------------------------------------
>
>                 Key: SPARK-4722
>                 URL: https://issues.apache.org/jira/browse/SPARK-4722
>             Project: Spark
>          Issue Type: Improvement
>          Components: MLlib, Streaming
>            Reporter: Arthur Andres
>            Priority: Minor
>              Labels: mllib, regression, streaming
>
> When training a model with a stream of new data (Spark Streaming + Spark Mlllib),  the weights (and the other part of the regression model) update at every iterations.
> At the moment the only output we can get is the prediction when calling predictOn (class StreamingLinearRegression)
> It would be a nice improvement if trainOn would return a Dstream of weights (and any other underlying model data) so we can access it and see it evolve. At the moment they are only outputted in the log
> For example this could then be saved so when reloading the application we can access this information without having to train the model again.



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