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https://issues.apache.org/jira/browse/SPARK-18054?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15770972#comment-15770972
]
Ilya Matiach commented on SPARK-18054:
--------------------------------------
It looks like this is already fixed in the latest version. The error message I
got from Spark was:
java.lang.IllegalArgumentException: requirement failed: Column features must be
of type org.apache.spark.ml.linalg.VectorUDT@3bfc3ba7 but was actually
org.apache.spark.mllib.linalg.VectorUDT@f71b0bce.
My test code was in NaiveBayesSuite, the code was:
import org.apache.spark.sql.functions._
test("SPARK-18054: throw nice error message when using mllib vector instead of
ml vector") {
// Example taken from bug submitter
// scalastyle:off println
dataset.columns.foreach(println(_))
// scalastyle:on println
val nbModel = new NaiveBayes()
.setLabelCol(dataset.columns(0))
.setFeaturesCol(dataset.columns(1))
.setPredictionCol("_prediction_column_")
.setProbabilityCol("_probability_column_")
.setModelType("multinomial")
.fit(dataset)
val data = sc.parallelize(Seq((1.0,
org.apache.spark.mllib.linalg.Vectors.dense(Array.empty[Double])))).toDF("label",
"features")
var newDf = nbModel.transform(data)
val extractProbability = udf((vector: DenseVector) => vector(1))
val dfWithProbability = newDf.withColumn("foo",
extractProbability(col("_probability_column_")))
}
> 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: Improvement
> Components: Documentation, ML
> Affects Versions: 2.0.1
> Reporter: Barry Becker
> Priority: Minor
>
> 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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