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https://issues.apache.org/jira/browse/SPARK-15509?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15475758#comment-15475758
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WangJianfei commented on SPARK-15509:
-------------------------------------
please check my issue https://issues.apache.org/jira/browse/SPARK-17447
thinks
> R MLlib algorithms should support input columns "features" and "label"
> ----------------------------------------------------------------------
>
> Key: SPARK-15509
> URL: https://issues.apache.org/jira/browse/SPARK-15509
> Project: Spark
> Issue Type: Improvement
> Components: ML, SparkR
> Reporter: Joseph K. Bradley
> Assignee: Xin Ren
> Fix For: 2.1.0
>
>
> Currently in SparkR, when you load a LibSVM dataset using the sqlContext and
> then pass it to an MLlib algorithm, the ML wrappers will fail since they will
> try to create a "features" column, which conflicts with the existing
> "features" column from the LibSVM loader. E.g., using the "mnist" dataset
> from LibSVM:
> {code}
> training <- loadDF(sqlContext, ".../mnist", "libsvm")
> model <- naiveBayes(label ~ features, training)
> {code}
> This fails with:
> {code}
> 16/05/24 11:52:41 ERROR RBackendHandler: fit on
> org.apache.spark.ml.r.NaiveBayesWrapper failed
> Error in invokeJava(isStatic = TRUE, className, methodName, ...) :
> java.lang.IllegalArgumentException: Output column features already exists.
> at
> org.apache.spark.ml.feature.VectorAssembler.transformSchema(VectorAssembler.scala:120)
> at
> org.apache.spark.ml.Pipeline$$anonfun$transformSchema$4.apply(Pipeline.scala:179)
> at
> org.apache.spark.ml.Pipeline$$anonfun$transformSchema$4.apply(Pipeline.scala:179)
> at
> scala.collection.IndexedSeqOptimized$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.Pipeline.transformSchema(Pipeline.scala:179)
> at org.apache.spark.ml.PipelineStage.transformSchema(Pipeline.scala:67)
> at org.apache.spark.ml.Pipeline.fit(Pipeline.scala:131)
> at org.apache.spark.ml.feature.RFormula.fit(RFormula.scala:169)
> at
> org.apache.spark.ml.r.NaiveBayesWrapper$.fit(NaiveBayesWrapper.scala:62)
> at org.apache.spark.ml.r.NaiveBayesWrapper.fit(NaiveBayesWrapper.sca
> {code}
> The same issue appears for the "label" column once you rename the "features"
> column.
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