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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