[jira] [Assigned] (SPARK-6345) Model update propagation during prediction in Streaming Regression

["Apache Spark (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-6345?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-6345:
-----------------------------------

    Assignee: Apache Spark  (was: Jeremy Freeman)

> Model update propagation during prediction in Streaming Regression
> ------------------------------------------------------------------
>
>                 Key: SPARK-6345
>                 URL: https://issues.apache.org/jira/browse/SPARK-6345
>             Project: Spark
>          Issue Type: Bug
>          Components: MLlib, Streaming
>            Reporter: Jeremy Freeman
>            Assignee: Apache Spark
>
> During streaming regression analyses (Streaming Linear Regression and Streaming Logistic Regression), model updates based on training data are not being reflected in subsequent calls to predictOn or predictOnValues, despite updates themselves occurring successfully. It may be due to recent changes to model declaration, and I have a working fix prepared to be submitted ASAP (alongside expanded test coverage).
> A temporary workaround is to retrieve and use the updated model within a foreachRDD, as in:
> {code}
> model.trainOn(trainingData)
> testingData.foreachRDD{ rdd =>
>     val latest = model.latestModel()
>     val predictions = rdd.map(lp => latest.predict(lp.features))
>     ...print or other side effects...
> }
> {code}
> Or within a transform, as in:
> {code}
> model.trainOn(trainingData)
> val predictions = testingData.transform { rdd =>
>       val latest = model.latestModel()
>       rdd.map(lp => (lp.label, latest.predict(lp.features)))
> }
> {code}
> Note that this does not affect Streaming KMeans, which works as expected for combinations of training and prediction.



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