Spark ML - KMeans - org.apache.spark.sql.AnalysisException：无法解析＆＃39;`features`＆＃39;给定输入列

时间：2017-01-20 17:48:03

标签： scala apache-spark machine-learning k-means

我正在尝试使用Spark ML KMeans分析和聚类芝加哥犯罪数据集。以下是摘录

case class ChicCase(ID: Long, Case_Number: String, Date: String, Block: String, IUCR: String, Primary_Type: String, Description: String, Location_description: String, Arrest: Boolean, Domestic: Boolean, Beat: Int, District: Int, Ward: Int, Community_Area: Int, FBI_Code: String, X_Coordinate: Int, Y_Coordinate: Int, Year: Int, Updated_On: String, Latitude: Double, Longitude: Double, Location: String)
val city = spark.read.option("header", true).option("inferSchema", true).csv("/chicago_city/Crimes_2001_to_present_2").as[ChicCase]

val data = city.drop("ID", "Case_Number", "Date", "Block", "IUCR", "Primary_Type", "Description", "Location_description", "Arrest", "Domestic", "FBI_Code", "Year", "Location", "Updated_On")

val kmeans = new KMeans
kmeans.setK(10).setSeed(1L)
val model = kmeans.fit(data)

但是这会抛出以下异常

    org.apache.spark.sql.AnalysisException: cannot resolve '`features`' given input columns: [Ward, Longitude, X_Coordinate, Beat, Latitude, District, Y_Coordinate, Community_Area];   
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:77) 
	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.plans.QueryPlan.transformExpressionUp$1(QueryPlan.scala:190) 
	at org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:200) 
at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2$1.apply(QueryPlan.scala:204) 
at scala.collection.TraversableLike$$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:204) 
	at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$5.apply(QueryPlan.scala:209) 
	at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:179) 
	at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:209) 
	at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$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:2589) 
at org.apache.spark.sql.Dataset.select(Dataset.scala:969)   
at org.apache.spark.ml.clustering.KMeans.fit(KMeans.scala:307)   ... 90 elided

数据类型为Int或Double。可能是什么问题？

1 个答案:

答案 0 :(得分：2)

在spark ml数据框架API中，应使用带有要素名称的 VectorAssembler 将所有要素列收集为一个列。当你适合模型时，它会尝试找到 features 列，在你的情况下，没有这样的列，这就是为什么Exception：无法解析＆＃39; {{1} }＆＃39;给定输入列：

features
相关问题
最新问题