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https://issues.apache.org/jira/browse/SPARK-8418?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16275426#comment-16275426
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Nick Pentreath commented on SPARK-8418:
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*1 I’m ok with throwing an exception. We can update the previous and in
progress PRs accordingly.
*2 where modifying an existing API obviously we need to keep both.
But I prefer only inputCols for new Components. We can provide convenience
method to set single (or a few) input columns - I did that for
FeatureHasher.
Like setInputCol(col: String, others: String*). But the param set is
inputCols under the hood.
Java still must use setInputCols as the above only works for Scala I think.
We can also deprecate the single column variants for 3.0 if we like?
*3 yes we must thoroughly test this before 2.3 release. I think it should
be fine as it’s just adding a few new parameters which is nothing out of
the ordinary.
*4 I will create JIRAs for Python APIs - ideally we’d like them for 2.3.
Fortunately it should be pretty trivial to complete.
On Sat, 2 Dec 2017 at 00:00, Joseph K. Bradley (JIRA) <j...@apache.org>
> Add single- and multi-value support to ML Transformers
> ------------------------------------------------------
>
> Key: SPARK-8418
> URL: https://issues.apache.org/jira/browse/SPARK-8418
> Project: Spark
> Issue Type: Sub-task
> Components: ML
> Reporter: Joseph K. Bradley
>
> It would be convenient if all feature transformers supported transforming
> columns of single values and multiple values, specifically:
> * one column with one value (e.g., type {{Double}})
> * one column with multiple values (e.g., {{Array[Double]}} or {{Vector}})
> We could go as far as supporting multiple columns, but that may not be
> necessary since VectorAssembler could be used to handle that.
> Estimators under {{ml.feature}} should also support this.
> This will likely require a short design doc to describe:
> * how input and output columns will be specified
> * schema validation
> * code sharing to reduce duplication
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