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https://issues.apache.org/jira/browse/SPARK-10931?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15190093#comment-15190093
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Timothy Hunter commented on SPARK-10931:
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Using python decorators, it is fairly easy to autogenerate at runtime all the
param wrappers, getters and setters and extract the documentation from the
scala side so that the documentation of the parameter is included in the
docstring of the getters and setters.
There are two issues with that:
- do we need to specialize the documentation or some of the conversions
between java and python? In both cases, it is possible to "subclass" and make
sure that the methods do not get overwritten by some autogenerated stubs
- the documentation of a class (which relies on the bytecode, not on runtime
instances) would miss all the params, because they are only generated in
runtime objects. I believe there are some ways around it, such as inserting
such methods at import time, but that would require more investigation.
> PySpark ML Models should contain Param values
> ---------------------------------------------
>
> Key: SPARK-10931
> URL: https://issues.apache.org/jira/browse/SPARK-10931
> Project: Spark
> Issue Type: Improvement
> Components: ML, PySpark
> Reporter: Joseph K. Bradley
>
> PySpark spark.ml Models are generally wrappers around Java objects and do not
> even contain Param values. This JIRA is for copying the Param values from
> the Estimator to the model.
> This can likely be solved by modifying Estimator.fit to copy Param values,
> but should also include proper unit tests.
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