[jira] [Resolved] (SPARK-18757) Models in Pyspark support column setters

["zhengruifeng (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-18757?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

zhengruifeng resolved SPARK-18757.
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    Resolution: Not A Problem

> Models in Pyspark support column setters
> ----------------------------------------
>
>                 Key: SPARK-18757
>                 URL: https://issues.apache.org/jira/browse/SPARK-18757
>             Project: Spark
>          Issue Type: Brainstorming
>          Components: ML, PySpark
>            Reporter: zhengruifeng
>            Priority: Major
>
> Recently, I found three places in which column setters are missing: KMeansModel, BisectingKMeansModel and OneVsRestModel.
> These three models directly inherit `Model` which dont have columns setters, so I had to add the missing setters manually in [SPARK-18625] and [SPARK-18520].
> Fow now, models in pyspark still don't support column setters at all.
> I suggest that we keep the hierarchy of pyspark models in line with that in the scala side:
> For classifiation and regression algs, I‘m making a trial in [SPARK-18739]. In it, I try to copy the hierarchy from the scala side.
> For clustering algs, I think we may first create abstract classes {{ClusteringModel}} and {{ProbabilisticClusteringModel}} in the scala side, and make clustering algs inherit it. Then, in the python side, we copy the hierarchy so that we dont need to add setters manually for each alg.
> For features algs, we can also use a abstract class {{FeatureModel}} in scala side, and do the same thing.
> What's your opinions? [~yanboliang][~josephkb][~sethah][~srowen]



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