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https://issues.apache.org/jira/browse/SPARK-18757?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15793971#comment-15793971
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zhengruifeng commented on SPARK-18757:
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OK, I will follow your guides.
> 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
>
> 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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