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From "Barry Becker (JIRA)" <j...@apache.org>
Subject [jira] [Created] (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 Fri, 21 Oct 2016 17:22:58 GMT
Barry Becker created SPARK-18054:
------------------------------------

             Summary: 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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