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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2018/08/13 01:13:00 UTC
[jira] [Resolved] (SPARK-25090) java.lang.ClassCastException when
using a CrossValidator
[ https://issues.apache.org/jira/browse/SPARK-25090?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-25090.
----------------------------------
Resolution: Fixed
Fix Version/s: 2.4.0
Issue resolved by pull request 22076
[https://github.com/apache/spark/pull/22076]
> java.lang.ClassCastException when using a CrossValidator
> --------------------------------------------------------
>
> Key: SPARK-25090
> URL: https://issues.apache.org/jira/browse/SPARK-25090
> Project: Spark
> Issue Type: Bug
> Components: ML
> Affects Versions: 2.3.1
> Environment: Windows 10 64-bits, pyspark 2.3.1 on Anaconda.
> Reporter: Mark Morrisson
> Assignee: Marco Gaido
> Priority: Major
> Fix For: 2.4.0
>
>
> When I fit a LogisticRegression on a dataset, everything works fine but when I fit a CrossValidator, I get this error:
> py4j.protocol.Py4JJavaError: An error occurred while calling o1187.w.
> : java.lang.ClassCastException: java.lang.Integer cannot be cast to java.lang.Double
> at scala.runtime.BoxesRunTime.unboxToDouble(BoxesRunTime.java:114)
> at org.apache.spark.ml.param.DoubleParam.w(params.scala:330)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
> at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
> at java.lang.reflect.Method.invoke(Unknown Source)
> at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
> at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
> at py4j.Gateway.invoke(Gateway.java:282)
> at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
> at py4j.commands.CallCommand.execute(CallCommand.java:79)
> at py4j.GatewayConnection.run(GatewayConnection.java:238)
> at java.lang.Thread.run(Unknown Source)
> Casting the target variable into double didn't solve the issue.
> Here is the snippet:
> lr = LogisticRegression(maxIter=10, labelCol="class", featuresCol="features", rawPredictionCol="score")
> evaluator = BinaryClassificationEvaluator(rawPredictionCol="score", labelCol="class", metricName="areaUnderROC")
> paramGrid = ParamGridBuilder().addGrid(lr.regParam, [0.01, 0.05, 0.1, 0.5, 1]).build()
> crossval = CrossValidator(estimator=lr, estimatorParamMaps=paramGrid, evaluator=evaluator, numFolds=3)
> bestModel = crossval.fit(train)
>
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