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From "zhengruifeng (Jira)" <j...@apache.org>
Subject [jira] [Created] (SPARK-28958) pyspark.ml function parity
Date Tue, 03 Sep 2019 11:37:00 GMT
zhengruifeng created SPARK-28958:
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             Summary: pyspark.ml function parity
                 Key: SPARK-28958
                 URL: https://issues.apache.org/jira/browse/SPARK-28958
             Project: Spark
          Issue Type: Improvement
          Components: ML, PySpark
    Affects Versions: 3.0.0
            Reporter: zhengruifeng


I looked into the hierarchy of both py and scala sides, and found that they are quite different,
which damage the parity and make the codebase hard to maintain.

The main inconvenience is that most models in pyspark do not support any param getters and
setters.


In the py side, I think we need to do:

1, remove setters generated by _shared_params_code_gen.py;

2, add common abstract classes like the side side, such as JavaPredictor/JavaClassificationModel/JavaProbabilisticClassifier;

3, for each alg, add its param trait, such as LinearSVCParams;

4, since sharedParam do not have setters, we need to add them in right places;


Unfortunately, I notice that if we do 1 (remove setters generated by _shared_params_code_gen.py),
all algs (classification/regression/clustering/features/fpm/recommendation) need to be modified
in one batch.


The scala side also need some small improvements, but I think they can be leave alone at first,
to avoid a lot of MiMa Failures.



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