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Posted to issues@spark.apache.org by "Yu Ishikawa (JIRA)" <ji...@apache.org> on 2015/06/11 07:27:00 UTC
[jira] [Commented] (SPARK-6725) Model export/import for Pipeline
API
[ https://issues.apache.org/jira/browse/SPARK-6725?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14581457#comment-14581457 ]
Yu Ishikawa commented on SPARK-6725:
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I'm afraid I'm not familiar with the LDA implementation in spark.mllib. Why is there any issue for importing/exporting LDA model?
> Model export/import for Pipeline API
> ------------------------------------
>
> Key: SPARK-6725
> URL: https://issues.apache.org/jira/browse/SPARK-6725
> Project: Spark
> Issue Type: Umbrella
> Components: ML
> Affects Versions: 1.3.0
> Reporter: Joseph K. Bradley
> Assignee: Joseph K. Bradley
> Priority: Critical
>
> This is an umbrella JIRA for adding model export/import to the spark.ml API. This JIRA is for adding the internal Saveable/Loadable API and Parquet-based format, not for other formats like PMML.
> This will require the following steps:
> * Add export/import for all PipelineStages supported by spark.ml
> ** This will include some Transformers which are not Models.
> ** These can use almost the same format as the spark.mllib model save/load functions, but the model metadata must store a different class name (marking the class as a spark.ml class).
> * After all PipelineStages support save/load, add an interface which forces future additions to support save/load.
> *UPDATE*: In spark.ml, we could save feature metadata using DataFrames. Other libraries and formats can support this, and it would be great if we could too. We could do either of the following:
> * save() optionally takes a dataset (or schema), and load will return a (model, schema) pair.
> * Models themselves save the input schema.
> Both options would mean inheriting from new Saveable, Loadable types.
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