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https://issues.apache.org/jira/browse/SPARK-11605?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15030494#comment-15030494
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yuhao yang edited comment on SPARK-11605 at 11/28/15 1:16 PM:
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Return type of org.apache.spark.ml.clustering.LDA.getOldDataset becomes
scala.Tuple2<java.lang.Object, org.apache.spark.mllib.linalg.Vector>, while it
should be (long, vector). Although this is private[clustering] method
Some other private[ml] functions have similar issue. Let me know if we should
list them.
was (Author: yuhaoyan):
return type of org.apache.spark.ml.clustering.LDA.getOldDataset becomes
scala.Tuple2<java.lang.Object, org.apache.spark.mllib.linalg.Vector>, while it
should be (long, vector). Although this is private[clustering] method
> ML 1.6 QA: API: Java compatibility, docs
> ----------------------------------------
>
> Key: SPARK-11605
> URL: https://issues.apache.org/jira/browse/SPARK-11605
> Project: Spark
> Issue Type: Sub-task
> Components: Documentation, Java API, ML, MLlib
> Reporter: Joseph K. Bradley
> Assignee: yuhao yang
>
> Check Java compatibility for MLlib for this release.
> Checking compatibility means:
> * comparing with the Scala doc
> * verifying that Java docs are not messed up by Scala type incompatibilities.
> Some items to look out for are:
> ** Check for generic "Object" types where Java cannot understand complex
> Scala types.
> *** *Note*: The Java docs do not always match the bytecode. If you find a
> problem, please verify it using {{javap}}.
> ** Check Scala objects (especially with nesting!) carefully.
> ** Check for uses of Scala and Java enumerations, which can show up oddly in
> the other language's doc.
> * If needed for complex issues, create small Java unit tests which execute
> each method. (The correctness can be checked in Scala.)
> If you find issues, please comment here, or for larger items, create separate
> JIRAs and link here.
> Note that we should not break APIs from previous releases. So if you find a
> problem, check if it was introduced in this Spark release (in which case we
> can fix it) or in a previous one (in which case we can create a java-friendly
> version of the API).
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