You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2016/08/30 10:37:21 UTC
[jira] [Commented] (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:comment-tabpanel&focusedCommentId=15448700#comment-15448700 ]
Apache Spark commented on SPARK-17311:
--------------------------------------
User 'srowen' has created a pull request for this issue:
https://github.com/apache/spark/pull/14826
> 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
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org