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https://issues.apache.org/jira/browse/SPARK-14812?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15371841#comment-15371841
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Joseph K. Bradley edited comment on SPARK-14812 at 7/12/16 1:03 AM:
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[~thunterdb] has reviewed the public API for things which should be private.
Issues noted in [SPARK-16485]
General decisions to follow, except where noted:
* spark.mllib, pyspark.mllib: Remove all Experimental annotations. Leave
DeveloperApi annotations alone.
* spark.ml, pyspark.ml
** Treat Estimator-Model pairs of classes and companion objects the same way.
** For all algorithms marked Experimental with Since tag <= 1.6, remove
Experimental annotation.
** For all algorithms marked Experimental with Since tag = 2.0, leave
Experimental annotation.
* While writing and reviewing a PR, we should be careful to check for cases
where a class should no longer be Experimental but has an Experimental method,
val, or other feature.
* DeveloperApi annotations are left alone, except where noted.
* No changes to which types are sealed.
spark.ml, pyspark.ml
* Model Summary classes remain Experimental
* MLWriter, MLReader, MLWritable, MLReadable remain Experimental
* ml.tree.Node, ml.tree.Split, and subclasses should no longer be DeveloperApi
How does this sound [~mlnick]?
was (Author: josephkb):
[~thunterdb] has reviewed the public API for things which should be private.
Issues noted in [SPARK-16485]
General decisions to follow, except where noted:
* spark.mllib, pyspark.mllib: Remove all Experimental annotations. Leave
DeveloperApi annotations alone.
* spark.ml, pyspark.ml
** Treat Estimator-Model pairs of classes and companion objects the same way.
** For all algorithms marked Experimental with Since tag <= 1.6, remove
Experimental annotation.
** For all algorithms marked Experimental with Since tag = 2.0, leave
Experimental annotation.
* While writing and reviewing a PR, we should be careful to check for cases
where a class should no longer be Experimental but has an Experimental method,
val, or other feature.
* DeveloperApi annotations are left alone, except where noted.
* No changes to which types are sealed.
spark.ml, pyspark.ml
* classification
** LogisticRegression*Summary classes remain Experimental
* MLWriter, MLReader, MLWritable, MLReadable remain Experimental
* ml.tree.Node, ml.tree.Split, and subclasses should no longer be DeveloperApi
How does this sound [~mlnick]?
> ML, Graph 2.0 QA: API: Experimental, DeveloperApi, final, sealed audit
> ----------------------------------------------------------------------
>
> Key: SPARK-14812
> URL: https://issues.apache.org/jira/browse/SPARK-14812
> Project: Spark
> Issue Type: Sub-task
> Components: Documentation, GraphX, ML, MLlib
> Reporter: Joseph K. Bradley
> Assignee: DB Tsai
> Priority: Blocker
>
> We should make a pass through the items marked as Experimental or
> DeveloperApi and see if any are stable enough to be unmarked.
> We should also check for items marked final or sealed to see if they are
> stable enough to be opened up as APIs.
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