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Posted to issues@spark.apache.org by "Joseph K. Bradley (JIRA)" <ji...@apache.org> on 2017/10/20 17:56:00 UTC
[jira] [Updated] (SPARK-21926) Compatibility between ML
Transformers and Structured Streaming
[ https://issues.apache.org/jira/browse/SPARK-21926?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Joseph K. Bradley updated SPARK-21926:
--------------------------------------
Issue Type: Umbrella (was: Bug)
> Compatibility between ML Transformers and Structured Streaming
> --------------------------------------------------------------
>
> Key: SPARK-21926
> URL: https://issues.apache.org/jira/browse/SPARK-21926
> Project: Spark
> Issue Type: Umbrella
> Components: ML, Structured Streaming
> Affects Versions: 2.2.0
> Reporter: Bago Amirbekian
>
> We've run into a few cases where ML components don't play nice with streaming dataframes (for prediction). This ticket is meant to help aggregate these known cases in one place and provide a place to discuss possible fixes.
> Failing cases:
> 1) VectorAssembler where one of the inputs is a VectorUDT column with no metadata.
> Possible fixes:
> a) Re-design vectorUDT metadata to support missing metadata for some elements. (This might be a good thing to do anyways SPARK-19141)
> b) drop metadata in streaming context.
> 2) OneHotEncoder where the input is a column with no metadata.
> Possible fixes:
> a) Make OneHotEncoder an estimator (SPARK-13030).
> b) Allow user to set the cardinality of OneHotEncoder.
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