The problem might be that you are evaluating with "predictionLabel" instead of "prediction", where predictionLabel is the prediction index mapped to the original label strings - at least according to the RandomForestClassifierExample, not sure if your code is exactly the same.
On Tue, Jun 28, 2016 at 1:21 PM, Rich Tarro <richta...@gmail.com> wrote: > I created a ML pipeline using the Random Forest Classifier - similar to > what is described here except in my case the source data is in csv format > rather than libsvm. > > > https://spark.apache.org/docs/latest/ml-classification-regression.html#random-forest-classifier > > I am able to successfully train the model and make predictions (on test > data not used to train the model) as shown here. > > +------------+--------------+-----+----------+--------------------+ > |indexedLabel|predictedLabel|label|prediction| features| > +------------+--------------+-----+----------+--------------------+ > | 4.0| 4.0| 0| 0|(784,[124,125,126...| > | 2.0| 2.0| 3| 3|(784,[119,120,121...| > | 8.0| 8.0| 8| 8|(784,[180,181,182...| > | 0.0| 0.0| 1| 1|(784,[154,155,156...| > | 3.0| 8.0| 2| 8|(784,[148,149,150...| > +------------+--------------+-----+----------+--------------------+ > only showing top 5 rows > > However, when I attempt to calculate the error between the indexedLabel and > the precictedLabel using the MulticlassClassificationEvaluator, I get the > NoSuchElementException error attached below. > > val evaluator = new > MulticlassClassificationEvaluator().setLabelCol("indexedLabel").setPredictionCol("predictedLabel").setMetricName("precision") > val accuracy = evaluator.evaluate(predictions) > println("Test Error = " + (1.0 - accuracy)) > > What could be the issue? > > > > Name: org.apache.spark.SparkException > Message: Job aborted due to stage failure: Task 2 in stage 49.0 failed 10 > times, most recent failure: Lost task 2.9 in stage 49.0 (TID 162, > yp-spark-dal09-env5-0024): java.util.NoSuchElementException: key not found: > 132.0 > at scala.collection.MapLike$class.default(MapLike.scala:228) > at scala.collection.AbstractMap.default(Map.scala:58) > at scala.collection.MapLike$class.apply(MapLike.scala:141) > at scala.collection.AbstractMap.apply(Map.scala:58) > at > org.apache.spark.ml.feature.VectorIndexerModel$$anonfun$10.apply(VectorIndexer.scala:331) > at > org.apache.spark.ml.feature.VectorIndexerModel$$anonfun$10.apply(VectorIndexer.scala:309) > at > org.apache.spark.ml.feature.VectorIndexerModel$$anonfun$11.apply(VectorIndexer.scala:351) > at > org.apache.spark.ml.feature.VectorIndexerModel$$anonfun$11.apply(VectorIndexer.scala:351) > at > org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificPredicate.eval(Unknown > Source) > at > org.apache.spark.sql.catalyst.expressions.codegen.GeneratePredicate$$anonfun$create$2.apply(GeneratePredicate.scala:67) > at > org.apache.spark.sql.catalyst.expressions.codegen.GeneratePredicate$$anonfun$create$2.apply(GeneratePredicate.scala:67) > at > org.apache.spark.sql.execution.Filter$$anonfun$2$$anonfun$apply$2.apply(basicOperators.scala:74) > at > org.apache.spark.sql.execution.Filter$$anonfun$2$$anonfun$apply$2.apply(basicOperators.scala:72) > at scala.collection.Iterator$$anon$14.hasNext(Iterator.scala:390) > at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327) > at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327) > at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327) > at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327) > at > org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:189) > at > org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:64) > at > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73) > at > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41) > at org.apache.spark.scheduler.Task.run(Task.scala:89) > at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213) > at > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1153) > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628) > at java.lang.Thread.run(Thread.java:785) > > Driver stacktrace: > StackTrace: > org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431) > org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419) > org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418) > scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) > scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) > org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418) > org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799) > org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799) > scala.Option.foreach(Option.scala:236) > org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799) > org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640) > org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599) > org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588) > org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48) > java.lang.Thread.getStackTrace(Thread.java:1117) > org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620) > org.apache.spark.SparkContext.runJob(SparkContext.scala:1837) > org.apache.spark.SparkContext.runJob(SparkContext.scala:1850) > org.apache.spark.SparkContext.runJob(SparkContext.scala:1863) > org.apache.spark.SparkContext.runJob(SparkContext.scala:1934) > org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:927) > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150) > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111) > org.apache.spark.rdd.RDD.withScope(RDD.scala:316) > org.apache.spark.rdd.RDD.collect(RDD.scala:926) > org.apache.spark.rdd.PairRDDFunctions$$anonfun$collectAsMap$1.apply(PairRDDFunctions.scala:741) > org.apache.spark.rdd.PairRDDFunctions$$anonfun$collectAsMap$1.apply(PairRDDFunctions.scala:740) > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150) > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111) > org.apache.spark.rdd.RDD.withScope(RDD.scala:316) > org.apache.spark.rdd.PairRDDFunctions.collectAsMap(PairRDDFunctions.scala:740) > org.apache.spark.mllib.evaluation.MulticlassMetrics.tpByClass$lzycompute(MulticlassMetrics.scala:49) > org.apache.spark.mllib.evaluation.MulticlassMetrics.tpByClass(MulticlassMetrics.scala:45) > org.apache.spark.mllib.evaluation.MulticlassMetrics.precision$lzycompute(MulticlassMetrics.scala:142) > org.apache.spark.mllib.evaluation.MulticlassMetrics.precision(MulticlassMetrics.scala:142) > org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator.evaluate(MulticlassClassificationEvaluator.scala:84) > $line110.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:59) > $line110.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:64) > $line110.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:66) > $line110.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:68) > $line110.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:70) > $line110.$read$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:72) > $line110.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:74) > $line110.$read$$iwC$$iwC$$iwC.<init>(<console>:76) > $line110.$read$$iwC$$iwC.<init>(<console>:78) > $line110.$read$$iwC.<init>(<console>:80) > $line110.$read.<init>(<console>:82) > $line110.$read$.<init>(<console>:86) > $line110.$read$.<clinit>(<console>) > $line110.$eval$.<init>(<console>:7) > $line110.$eval$.<clinit>(<console>) > $line110.$eval.$print(<console>) > sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) > sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:95) > sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:55) > java.lang.reflect.Method.invoke(Method.java:507) > org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065) > org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1346) > org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:840) > org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:871) > org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:819) > com.ibm.spark.interpreter.ScalaInterpreter$$anonfun$interpretAddTask$1$$anonfun$apply$3.apply(ScalaInterpreter.scala:296) > com.ibm.spark.interpreter.ScalaInterpreter$$anonfun$interpretAddTask$1$$anonfun$apply$3.apply(ScalaInterpreter.scala:291) > com.ibm.spark.global.StreamState$.withStreams(StreamState.scala:80) > com.ibm.spark.interpreter.ScalaInterpreter$$anonfun$interpretAddTask$1.apply(ScalaInterpreter.scala:290) > com.ibm.spark.interpreter.ScalaInterpreter$$anonfun$interpretAddTask$1.apply(ScalaInterpreter.scala:290) > com.ibm.spark.utils.TaskManager$$anonfun$add$2$$anon$1.run(TaskManager.scala:123) > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1153) > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628) > java.lang.Thread.run(Thread.java:785) > > > > > >
