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https://issues.apache.org/jira/browse/SPARK-8418?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16275049#comment-16275049
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Joseph K. Bradley commented on SPARK-8418:
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I just glanced through the various PRs adding multi-column support and wanted
to get consensus about a few items to make sure we have consistent APIs. CC
[~mlnick], [~yuhaoyan], [~yanboliang], [~WeichenXu123], [~huaxing], [~viirya]
Let me know what you think!
*1. When both inputCol and inputCols are specified, what should we do?*
* [SPARK-20542]: Bucketizer: logWarning
* [SPARK-13030]: OneHotEncoder: n/a (no single-column support)
* [SPARK-11215]: StringIndexer: throw exception
* [SPARK-22397]: QuantileDiscretizer: logWarning
* my vote: throw exception (safer since it's easier for users to recognize
their error)
*2. Should we have single- and multi-column support or just multi-column?
E.g., should we have (a) inputCol and inputCols or (b) only inputCols?*
Currently, [SPARK-13030] only has multi-column support for the new
OneHotEncoderEstimator. The other PRs have both single- and multi-column
support since they are modifying existing APIs.
*Q*: Should we add single-column to OneHotEncoderEstimator for consistency or
not bother? I'm ambivalent.
*3. Backwards compatibility for ML persistence*
We'll have to be aware of whether we're breaking compatibility. I don't see
problems in most PRs but have not tested it manually. The only PR with an
issue is [SPARK-13030] for OneHotEncoder; however, that's pretty reasonable to
break compatibility for persistence there.
*4. Python APIs*
I don't see follow-ups for Python APIs yet. Are those planned for 2.3?
> 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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