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Posted to issues@spark.apache.org by "Joseph K. Bradley (JIRA)" <ji...@apache.org> on 2016/03/21 19:42:25 UTC
[jira] [Commented] (SPARK-14023) Make exceptions consistent
regarding fields and columns
[ https://issues.apache.org/jira/browse/SPARK-14023?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15204858#comment-15204858 ]
Joseph K. Bradley commented on SPARK-14023:
-------------------------------------------
+1 It'd also be nice to create some Exception types for the different types of errors:
* missing input column
* input column has bad type
* any others?
> Make exceptions consistent regarding fields and columns
> -------------------------------------------------------
>
> Key: SPARK-14023
> URL: https://issues.apache.org/jira/browse/SPARK-14023
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 2.0.0
> Reporter: Jacek Laskowski
> Priority: Trivial
>
> As you can see below, a column is called a field depending on where an exception is thrown. I think it should be "column" everywhere (since that's what has a type from a schema).
> {code}
> scala> lr
> res32: org.apache.spark.ml.regression.LinearRegression = linReg_d9bfe808e743
> scala> lr.fit(ds)
> java.lang.IllegalArgumentException: Field "features" does not exist.
> at org.apache.spark.sql.types.StructType$$anonfun$apply$1.apply(StructType.scala:214)
> at org.apache.spark.sql.types.StructType$$anonfun$apply$1.apply(StructType.scala:214)
> at scala.collection.MapLike$class.getOrElse(MapLike.scala:128)
> at scala.collection.AbstractMap.getOrElse(Map.scala:59)
> at org.apache.spark.sql.types.StructType.apply(StructType.scala:213)
> at org.apache.spark.ml.util.SchemaUtils$.checkColumnType(SchemaUtils.scala:40)
> at org.apache.spark.ml.PredictorParams$class.validateAndTransformSchema(Predictor.scala:50)
> at org.apache.spark.ml.Predictor.validateAndTransformSchema(Predictor.scala:71)
> at org.apache.spark.ml.Predictor.transformSchema(Predictor.scala:116)
> at org.apache.spark.ml.PipelineStage.transformSchema(Pipeline.scala:67)
> at org.apache.spark.ml.Predictor.fit(Predictor.scala:89)
> ... 51 elided
> scala> lr.fit(ds)
> java.lang.IllegalArgumentException: requirement failed: Column label must be of type DoubleType but was actually StringType.
> at scala.Predef$.require(Predef.scala:219)
> at org.apache.spark.ml.util.SchemaUtils$.checkColumnType(SchemaUtils.scala:42)
> at org.apache.spark.ml.PredictorParams$class.validateAndTransformSchema(Predictor.scala:53)
> at org.apache.spark.ml.Predictor.validateAndTransformSchema(Predictor.scala:71)
> at org.apache.spark.ml.Predictor.transformSchema(Predictor.scala:116)
> at org.apache.spark.ml.PipelineStage.transformSchema(Pipeline.scala:67)
> at org.apache.spark.ml.Predictor.fit(Predictor.scala:89)
> ... 51 elided
> {code}
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