Anca Sarb created SPARK-27621:
---------------------------------
Summary: 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: 2.4.2, 2.4.1, 2.4.0, 2.3.3, 2.3.2, 2.3.1, 2.3.0, 2.3.4
Reporter: Anca Sarb
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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