[jira] [Updated] (SPARK-18301) VectorAssembler does not support StructTypes

["Hyukjin Kwon (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-18301?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon updated SPARK-18301:
---------------------------------
    Labels: bulk-closed  (was: )

> VectorAssembler does not support StructTypes
> --------------------------------------------
>
>                 Key: SPARK-18301
>                 URL: https://issues.apache.org/jira/browse/SPARK-18301
>             Project: Spark
>          Issue Type: Bug
>          Components: MLlib
>    Affects Versions: 2.0.1
>         Environment: Windows Standalone Mode, Java
>            Reporter: Steffen Herbold
>            Priority: Minor
>              Labels: bulk-closed
>
> I tried to transform a structured type using the VectorAssembler as follows:
> {code:java}
> VectorAssembler va = new VectorAssembler().setInputCols(new String[]
>             { "metrics.Line", "metrics.McCC" }).setOutputCol("features");
>         dataframe= va.transform(dataframe);
> {code}
> This yields the following exception:
> {code:java}
> Exception in thread "main" java.lang.IllegalArgumentException: Field "metrics.McCC" does not exist.
> 	at org.apache.spark.sql.types.StructType$$anonfun$apply$1.apply(StructType.scala:228)
> 	at org.apache.spark.sql.types.StructType$$anonfun$apply$1.apply(StructType.scala:228)
> 	at scala.collection.MapLike$class.getOrElse(MapLike.scala:128)
> 	at scala.collection.AbstractMap.getOrElse(Map.scala:59)
> 	at org.apache.spark.sql.types.StructType.apply(StructType.scala:227)
> 	at org.apache.spark.ml.feature.VectorAssembler$$anonfun$5.apply(VectorAssembler.scala:116)
> 	at org.apache.spark.ml.feature.VectorAssembler$$anonfun$5.apply(VectorAssembler.scala:116)
> 	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> 	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> 	at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
> 	at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
> 	at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
> 	at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:186)
> 	at org.apache.spark.ml.feature.VectorAssembler.transformSchema(VectorAssembler.scala:116)
> 	at org.apache.spark.ml.PipelineStage.transformSchema(Pipeline.scala:70)
> 	at org.apache.spark.ml.feature.VectorAssembler.transform(VectorAssembler.scala:54)
> 	at de.ugoe.cs.smartshark.jobs.DefectPredictionExample.main(DefectPredictionExample.java:53)
> {code}
> The schema of the dataframe is:
> {noformat}
>  |-- metrics: struct (nullable = true)
>  |    |-- Line: double (nullable = true)
>  |    |-- McCC: double (nullable = true)
> ...
> {noformat}
> The transfomation works, if I first use withColumn to make "metrics.Line" and "metrics.McCC" into columns of the dataframe:
> {code:java}
> dataframe.withColumn("Line", data.col("metrics.Line")
> dataframe.withColumn("McCC", data.col("metrics.McCC")
> VectorAssembler va = new VectorAssembler().setInputCols(new String[]
>             { "metrics.McCC", "metrics.NL" }).setOutputCol("features");
>         fileState = va.transform(dataframe);
> {code}
> However, this workaround is quite costly and direct support to access the nested values would be very helpful.



--
This message was sent by Atlassian JIRA
(v7.6.3#76005)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org