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From "Ilya Matiach (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-18301) VectorAssembler does not support StructTypes
Date Tue, 20 Dec 2016 23:46:58 GMT

    [ https://issues.apache.org/jira/browse/SPARK-18301?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15765596#comment-15765596
] 

Ilya Matiach commented on SPARK-18301:
--------------------------------------

I am able to reproduce this, but I'm not sure if this is actually a bug or a feature request.
 Are any other spark transformers or estimators able to work on structured types like this?

> 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
>
> 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.



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