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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2016/08/30 10:37:21 UTC
[jira] [Assigned] (SPARK-17311) Standardize Python-Java MLlib API
to accept optional long seeds in all cases
[ https://issues.apache.org/jira/browse/SPARK-17311?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-17311:
------------------------------------
Assignee: Sean Owen (was: Apache Spark)
> Standardize Python-Java MLlib API to accept optional long seeds in all cases
> ----------------------------------------------------------------------------
>
> Key: SPARK-17311
> URL: https://issues.apache.org/jira/browse/SPARK-17311
> Project: Spark
> Issue Type: Bug
> Components: MLlib, PySpark
> Affects Versions: 2.0.0
> Reporter: Sean Owen
> Assignee: Sean Owen
> Priority: Minor
>
> (Note this follows on https://issues.apache.org/jira/browse/SPARK-16832 )
> There are a few seed-related issues in the Pyspark-MLLib bridge:
> - {{PythonMLlibAPI}} methods that take a seed don't always take a {{java.lang.Long}} consistently, allowing the Python API to specify "no seed"
> - .mllib's {{Word2VecModel}} seems to be an odd man out in .mllib in that it picks its own random seed. Instead it should default to None, meaning, letting the Scala implementation pick a seed
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