You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Hyukjin Kwon (Jira)" <ji...@apache.org> on 2019/10/08 05:42:21 UTC
[jira] [Resolved] (SPARK-24656) SparkML Transformers and Estimators
with multiple columns
[ https://issues.apache.org/jira/browse/SPARK-24656?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-24656.
----------------------------------
Resolution: Incomplete
> SparkML Transformers and Estimators with multiple columns
> ---------------------------------------------------------
>
> Key: SPARK-24656
> URL: https://issues.apache.org/jira/browse/SPARK-24656
> Project: Spark
> Issue Type: New Feature
> Components: ML, MLlib
> Affects Versions: 2.3.1
> Reporter: Michael Dreibelbis
> Priority: Major
> Labels: bulk-closed
>
> Currently SparkML Transformers and Estimators operate on single input/output column pairs. This makes pipelines extremely cumbersome (as well as non-performant) when transformations on multiple columns needs to be made.
>
> I am proposing to implement ParallelPipelineStage/Transformer/Estimator/Model that would operate on the input columns in parallel.
>
> {code:java}
> // old way
> val pipeline = new Pipeline().setStages(Array(
> new CountVectorizer().setInputCol("_1").setOutputCol("_1_cv"),
> new CountVectorizer().setInputCol("_2").setOutputCol("_2_cv"),
> new IDF().setInputCol("_1_cv").setOutputCol("_1_idf"),
> new IDF().setInputCol("_2_cv").setOutputCol("_2_idf")
> ))
> // proposed way
> val pipeline2 = new Pipeline().setStages(Array(
> new ParallelCountVectorizer().setInputCols(Array("_1", "_2")).setOutputCols(Array("_1_cv", "_2_cv")),
> new ParallelIDF().setInputCols(Array("_1_cv", "_2_cv")).setOutputCols(Array("_1_idf", "_2_idf"))
> ))
> {code}
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org