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From "Barry Becker (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-18054) Unexpected error from UDF that gets an element of a vector: argument 1 requires vector type, however, '`_column_`' is of vector type
Date Sat, 22 Oct 2016 12:58:58 GMT

    [ https://issues.apache.org/jira/browse/SPARK-18054?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15597816#comment-15597816
] 

Barry Becker commented on SPARK-18054:
--------------------------------------

Ah. That is quite likely the problem. I will verify next week. A simple fix for this then,
would be to include the package name of the 2 classes in the error message.

> Unexpected error from UDF that gets an element of a vector: argument 1 requires vector
type, however, '`_column_`' is of vector type
> ------------------------------------------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-18054
>                 URL: https://issues.apache.org/jira/browse/SPARK-18054
>             Project: Spark
>          Issue Type: Bug
>          Components: ML
>    Affects Versions: 2.0.1
>            Reporter: Barry Becker
>
> Not sure if this is a bug in ML or a more core part of spark.
> It used to work in spark 1.6.2, but now gives me an error.
> I have a pipeline that contains a NaiveBayesModel which I created like this
> {code}
> val nbModel = new NaiveBayes()
>       .setLabelCol(target)
>       .setFeaturesCol(FEATURES_COL)
>       .setPredictionCol(PREDICTION_COLUMN)
>       .setProbabilityCol("_probability_column_")
>       .setModelType("multinomial")
> {code}
> When I apply that pipeline to some data there will be a "_probability_column_" of type
vector. I want to extract a probability for a specific class label using the following, but
it no longer works.
> {code}
> var newDf = pipeline.transform(df)
> val extractProbability = udf((vector: DenseVector) => vector(1))
> val dfWithProbability = newDf.withColumn("foo", extractProbability(col("_probability_column_")))
> {code}
> The error I get now that I have upgraded to 2.0.1 from 1.6.2 is shnown below. I consider
this a strange error because its basically saying "argument 1 requires a vector, but we got
a vector instead". That does not make any sense to me. It wants a vector, and a vector was
given. Why does it fail?
> {code}
> org.apache.spark.sql.AnalysisException: cannot resolve 'UDF(_class_probability_column__)'
due to data type mismatch: argument 1 requires vector type, however, '`_class_probability_column__`'
is of vector type.;
> 	at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
> 	at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:82)
> 	at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:74)
> 	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:301)
> 	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:301)
> 	at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)
> 	at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:300)
> 	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:298)
> 	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:298)
> 	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:321)
> 	at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:179)
> 	at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:319)
> 	at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:298)
> 	at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionUp$1(QueryPlan.scala:191)
> 	at org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:201)
> 	at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2$1.apply(QueryPlan.scala:205)
> 	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$class.map(TraversableLike.scala:234)
> 	at scala.collection.AbstractTraversable.map(Traversable.scala:104)
> 	at org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:205)
> 	at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$5.apply(QueryPlan.scala:210)
> 	at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:179)
> 	at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:210)
> 	at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:74)
> 	at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:67)
> 	at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:126)
> 	at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:67)
> 	at org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:58)
> 	at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:49)
> 	at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:64)
> 	at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withPlan(Dataset.scala:2603)
> 	at org.apache.spark.sql.Dataset.select(Dataset.scala:969)
> 	at org.apache.spark.sql.Dataset.withColumn(Dataset.scala:1697)
> 	at com.mineset.spark.transformations.ApplyModel.addProbabilityColumn(ApplyModel.scala:120)
> {code}



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