我正在尝试在多个列上应用字符串索引器。这是我的代码
val stringIndexers = Categorical_Model.map { colName =>new StringIndexer().setInputCol(colName).setOutputCol(colName + "_indexed")}
var dfStringIndexed = stringIndexers(0).fit(df3).transform(df3) // 'fit's a model then 'transform's data
for(x<-1 to stringIndexers.length-1)
{dfStringIndexed = stringIndexers(x).fit(dfStringIndexed).transform(dfStringIndexed)
}
dfStringIndexed = dfStringIndexed.drop(Categorical_Model: _*)
该模式显示所有具有nullable的列为false
stringIndexers数组像这样显示
stringIndexers: Array[org.apache.spark.ml.feature.StringIndexer] = Array(strIdx_c53c3bdf464c, strIdx_61e685c520f7, strIdx_d6e59b2fc69d, ......)
dfStringIndexed.show(10)
这将引发以下错误
org.apache.spark.SparkException: Failed to execute user defined function($anonfun$9: (string) => double)
为什么显示打印模式，但没有可用数据。
更新:如果我像这样手动循环字符串索引器，而不是循环。此代码有效。这很奇怪。
var dfStringIndexed = stringIndexers(0).fit(df3).transform(df3) // 'fit's a model then 'transform's data
dfStringIndexed = stringIndexers(1).fit(dfStringIndexed).transform(dfStringIndexed)
dfStringIndexed = stringIndexers(2).fit(dfStringIndexed).transform(dfStringIndexed)
dfStringIndexed = stringIndexers(3).fit(dfStringIndexed).transform(dfStringIndexed)
dfStringIndexed = stringIndexers(4).fit(dfStringIndexed).transform(dfStringIndexed)
根据要求添加Stacktrace
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1602)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1590)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1589)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1589)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:831)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1823)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1772)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1761)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2034)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2055)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2074)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:363)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3273)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2484)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2484)
at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3254)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3253)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2484)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2698)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:254)
at org.apache.spark.sql.Dataset.show(Dataset.scala:723)
at org.apache.spark.sql.Dataset.show(Dataset.scala:682)
... 63 elided
Caused by: org.apache.spark.SparkException: Failed to execute user defined function($anonfun$9: (string) => double)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.hasNext(WholeStageCodegenExec.scala:614)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:253)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:830)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:830)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
... 3 more
Caused by: org.apache.spark.SparkException: StringIndexer encountered NULL value. To handle or skip NULLS, try setting StringIndexer.handleInvalid.
at org.apache.spark.ml.feature.StringIndexerModel$$anonfun$9.apply(StringIndexer.scala:251)
at org.apache.spark.ml.feature.StringIndexerModel$$anonfun$9.apply(StringIndexer.scala:246)
... 19 more
最佳答案
我也遇到了类似的问题，即使是在50行的一小部分上，在我为字符串索引的列中都没有null。但是，即使我手动运行它也不起作用。
我可以通过包含.setHandleInvalid(“keep”)来避免该错误，并且我已经检查了输出，并且没有做任何奇怪的事情，例如将所有内容设置为0或相同的值或其他任何内容。我仍然对作为解决方案感到不满，因为它似乎很不安全。非常想知道您是否找到了更合理的答案和解决方案!
dfStringIndexed = stringIndexers(1).setHandleInvalid("keep").fit(dfStringIndexed).transform(dfStringIndexed)
我认为也可以通过更改列的可空性来修复它，即使其中不包含空值，我也按照此处所做的操作
Can I change the nullability of a column in my Spark dataframe?
关于scala - 无法执行用户定义的函数($ anonfun $ 9 : (string) => double) on using String Indexer for multiple columns，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/57143187/