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Posted to issues@spark.apache.org by "Anca Sarb (JIRA)" <ji...@apache.org> on 2019/05/02 09:21:00 UTC
[jira] [Commented] (SPARK-27621) Calling transform() method on a
LinearRegressionModel throws NoSuchElementException
[ https://issues.apache.org/jira/browse/SPARK-27621?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16831499#comment-16831499 ]
Anca Sarb commented on SPARK-27621:
-----------------------------------
I've created a PR with the fix here [https://github.com/apache/spark/pull/24509]
> Calling transform() method on a LinearRegressionModel throws NoSuchElementException
> -----------------------------------------------------------------------------------
>
> Key: SPARK-27621
> URL: https://issues.apache.org/jira/browse/SPARK-27621
> Project: Spark
> Issue Type: Bug
> Components: ML
> Affects Versions: 2.3.0, 2.3.1, 2.3.2, 2.3.3, 2.3.4, 2.4.0, 2.4.1, 2.4.2
> Reporter: Anca Sarb
> Priority: Minor
> Original Estimate: 2h
> Remaining Estimate: 2h
>
> When transform(...) method is called on a LinearRegressionModel created directly with the coefficients and intercepts, the following exception is encountered.
> {code:java}
> java.util.NoSuchElementException: Failed to find a default value for loss at org.apache.spark.ml.param.Params$$anonfun$getOrDefault$2.apply(params.scala:780) at org.apache.spark.ml.param.Params$$anonfun$getOrDefault$2.apply(params.scala:780) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.ml.param.Params$class.getOrDefault(params.scala:779) at org.apache.spark.ml.PipelineStage.getOrDefault(Pipeline.scala:42) at org.apache.spark.ml.param.Params$class.$(params.scala:786) at org.apache.spark.ml.PipelineStage.$(Pipeline.scala:42) at org.apache.spark.ml.regression.LinearRegressionParams$class.validateAndTransformSchema(LinearRegression.scala:111) at org.apache.spark.ml.regression.LinearRegressionModel.validateAndTransformSchema(LinearRegression.scala:637) at org.apache.spark.ml.PredictionModel.transformSchema(Predictor.scala:192) at org.apache.spark.ml.PipelineModel$$anonfun$transformSchema$5.apply(Pipeline.scala:311) at org.apache.spark.ml.PipelineModel$$anonfun$transformSchema$5.apply(Pipeline.scala:311) at scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:57) at scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:66) at scala.collection.mutable.ArrayOps$ofRef.foldLeft(ArrayOps.scala:186) at org.apache.spark.ml.PipelineModel.transformSchema(Pipeline.scala:311) at org.apache.spark.ml.PipelineStage.transformSchema(Pipeline.scala:74) at org.apache.spark.ml.PipelineModel.transform(Pipeline.scala:305)
> {code}
> This is because validateAndTransformSchema() is called both during training and scoring phases, but the checks against the training related params like loss should really be performed during training phase only, I think, please correct me if I'm missing anything :)
> This issue was first reported for mleap ([combust/mleap#455|https://github.com/combust/mleap/issues/455]) because basically when we serialize the Spark transformers for mleap, we only serialize the params that are relevant for scoring. We do have the option to de-serialize the serialized transformers back into Spark for scoring again, but in that case, we no longer have all the training params.
> Test to reproduce in PR: [https://github.com/apache/spark/pull/24509]
>
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