[
https://issues.apache.org/jira/browse/SPARK-8418?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16292320#comment-16292320
]
Nick Pentreath edited comment on SPARK-8418 at 12/15/17 10:40 AM:
------------------------------------------------------------------
Created SPARK-22796, SPARK-22797 and SPARK-22798 to track PySpark support for
{{QuantileDiscretizer}}, {{Bucketizer}} and {{StringIndexer}}, respectively.
The in-progress PR for QD changed to throwing exception as per above
discussion. I created SPARK-22799 to track that for {{Bucketizer}}
was (Author: mlnick):
Created SPARK-22796, SPARK-22797 and SPARK-22798 to track PySpark support for
{{QuantileDiscretizer}}, {{Bucketizer}} and {{StringIndexer}}, respectively.
The in-progress PR for QD changed to throwing exception as per above
discussion. I created SPARK-22799 to track that.
> 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
--
This message was sent by Atlassian JIRA
(v6.4.14#64029)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
