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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2017/10/18 07:02:00 UTC
[jira] [Assigned] (SPARK-22289) Cannot save
LogisticRegressionClassificationModel with bounds on coefficients
[ https://issues.apache.org/jira/browse/SPARK-22289?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-22289:
------------------------------------
Assignee: Apache Spark
> Cannot save LogisticRegressionClassificationModel with bounds on coefficients
> -----------------------------------------------------------------------------
>
> Key: SPARK-22289
> URL: https://issues.apache.org/jira/browse/SPARK-22289
> Project: Spark
> Issue Type: Bug
> Components: ML
> Affects Versions: 2.2.0
> Reporter: Nic Eggert
> Assignee: Apache Spark
>
> I think this was introduced in SPARK-20047.
> Trying to call save on a logistic regression model trained with bounds on its parameters throws an error. This seems to be because Spark doesn't know how to serialize the Matrix parameter.
> Model is set up like this:
> {code}
> val calibrator = new LogisticRegression()
> .setFeaturesCol("uncalibrated_probability")
> .setLabelCol("label")
> .setWeightCol("weight")
> .setStandardization(false)
> .setLowerBoundsOnCoefficients(new DenseMatrix(1, 1, Array(0.0)))
> .setFamily("binomial")
> .setProbabilityCol("probability")
> .setPredictionCol("logistic_prediction")
> .setRawPredictionCol("logistic_raw_prediction")
> {code}
> {code}
> 17/10/16 15:36:59 ERROR ApplicationMaster: User class threw exception: scala.NotImplementedError: The default jsonEncode only supports string and vector. org.apache.spark.ml.param.Param must override jsonEncode for org.apache.spark.ml.linalg.DenseMatrix.
> scala.NotImplementedError: The default jsonEncode only supports string and vector. org.apache.spark.ml.param.Param must override jsonEncode for org.apache.spark.ml.linalg.DenseMatrix.
> at org.apache.spark.ml.param.Param.jsonEncode(params.scala:98)
> at org.apache.spark.ml.util.DefaultParamsWriter$$anonfun$1$$anonfun$2.apply(ReadWrite.scala:296)
> at org.apache.spark.ml.util.DefaultParamsWriter$$anonfun$1$$anonfun$2.apply(ReadWrite.scala:295)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
> at scala.collection.AbstractTraversable.map(Traversable.scala:104)
> at org.apache.spark.ml.util.DefaultParamsWriter$$anonfun$1.apply(ReadWrite.scala:295)
> at org.apache.spark.ml.util.DefaultParamsWriter$$anonfun$1.apply(ReadWrite.scala:295)
> at scala.Option.getOrElse(Option.scala:121)
> at org.apache.spark.ml.util.DefaultParamsWriter$.getMetadataToSave(ReadWrite.scala:295)
> at org.apache.spark.ml.util.DefaultParamsWriter$.saveMetadata(ReadWrite.scala:277)
> at org.apache.spark.ml.classification.LogisticRegressionModel$LogisticRegressionModelWriter.saveImpl(LogisticRegression.scala:1182)
> at org.apache.spark.ml.util.MLWriter.save(ReadWrite.scala:114)
> at org.apache.spark.ml.Pipeline$SharedReadWrite$$anonfun$saveImpl$1.apply(Pipeline.scala:254)
> at org.apache.spark.ml.Pipeline$SharedReadWrite$$anonfun$saveImpl$1.apply(Pipeline.scala:253)
> at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
> at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
> at org.apache.spark.ml.Pipeline$SharedReadWrite$.saveImpl(Pipeline.scala:253)
> at org.apache.spark.ml.PipelineModel$PipelineModelWriter.saveImpl(Pipeline.scala:337)
> at org.apache.spark.ml.util.MLWriter.save(ReadWrite.scala:114)
> -snip-
> {code}
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