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https://issues.apache.org/jira/browse/SPARK-7146?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15216769#comment-15216769
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Michael Zieliński commented on SPARK-7146:
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We ended up copying significant portion of Params (HasInputCol(s),
HasOutputCol(s), HasProbabilityCol) for our custom Transformers/Estimators.
This creates a problem if you want to abstract Transformer, e.g. have a piece
of code that accepts any transformer with "setInputCol" method. Because our
HasInputCol and native Spark HasInputCol are different traits, that results in
very clunky code (structural typing + few ugly type aliases).
Opening up at least some traits would result in cleaner code and more reuse.
This document
(https://docs.google.com/document/d/1plFBPJY_PriPTuMiFYLSm7fQgD1FieP4wt3oMVKMGcc/edit#)
separates traits in a few groups, like I/O, hyper-parameters and optimization.
I/O would be a very good place to start, since those are re-used most
frequently across algorithms.
> Should ML sharedParams be a public API?
> ---------------------------------------
>
> Key: SPARK-7146
> URL: https://issues.apache.org/jira/browse/SPARK-7146
> Project: Spark
> Issue Type: Brainstorming
> Components: ML
> Reporter: Joseph K. Bradley
>
> Discussion: Should the Param traits in sharedParams.scala be public?
> Pros:
> * Sharing the Param traits helps to encourage standardized Param names and
> documentation.
> Cons:
> * Users have to be careful since parameters can have different meanings for
> different algorithms.
> * If the shared Params are public, then implementations could test for the
> traits. It is unclear if we want users to rely on these traits, which are
> somewhat experimental.
> Currently, the shared params are private.
> Proposal: Either
> (a) make the shared params private to encourage users to write specialized
> documentation and value checks for parameters, or
> (b) design a better way to encourage overriding documentation and parameter
> value checks
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