Hi, Using UR with PIO 0.10 I am trying to train my dataset. In return I get the following error:
*...* *[INFO] [DataSource] Received events List(facet, view, search)* *[INFO] [DataSource] Number of events List(5, 4, 6)* *[INFO] [Engine$] org.template.TrainingData does not support data sanity check. Skipping check.* *[INFO] [Engine$] org.template.PreparedData does not support data sanity check. Skipping check.* *[INFO] [URAlgorithm] Actions read now creating correlators* *[WARN] [TaskSetManager] Lost task 0.0 in stage 56.0 (TID 50, ip-172-31-40-139.eu-west-1.compute.internal): java.lang.NegativeArraySizeException* * at org.apache.mahout.math.DenseVector.<init>(DenseVector.java:57)* * at org.apache.mahout.sparkbindings.SparkEngine$$anonfun$5.apply(SparkEngine.scala:73)* * at org.apache.mahout.sparkbindings.SparkEngine$$anonfun$5.apply(SparkEngine.scala:72)* * at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)* * at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)* * at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)* * at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)* * at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)* * at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)* * at org.apache.spark.scheduler.Task.run(Task.scala:89)* * at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227)* * at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)* * at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)* * at java.lang.Thread.run(Thread.java:748)* *[ERROR] [TaskSetManager] Task 0 in stage 56.0 failed 4 times; aborting job* *Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 56.0 failed 4 times, most recent failure: Lost task 0.3 in stage 56.0 (TID 56, ip-172-1-1-1.eu-west-1.compute.internal): java.lang.NegativeArraySizeException* * at org.apache.mahout.math.DenseVector.<init>(DenseVector.java:57)* * at org.apache.mahout.sparkbindings.SparkEngine$$anonfun$5.apply(SparkEngine.scala:73)* * at org.apache.mahout.sparkbindings.SparkEngine$$anonfun$5.apply(SparkEngine.scala:72)* * at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)* * at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)* * at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)* * at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)* * at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)* * at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)* * at org.apache.spark.scheduler.Task.run(Task.scala:89)* * at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227)* * at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)* * at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)* * at java.lang.Thread.run(Thread.java:748)* *Driver stacktrace:* * at org.apache.spark.scheduler.DAGScheduler.org <http://org.apache.spark.scheduler.DAGScheduler.org>$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)* * at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)* * at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)* * at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)* * at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)* * at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)* * at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)* * at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)* * at scala.Option.foreach(Option.scala:236)* * at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)* * at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)* * at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)* * at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)* * at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)* * at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)* * at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)* * at org.apache.spark.SparkContext.runJob(SparkContext.scala:1952)* * at org.apache.spark.rdd.RDD$$anonfun$reduce$1.apply(RDD.scala:1025)* * at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)* * at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)* * at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)* * at org.apache.spark.rdd.RDD.reduce(RDD.scala:1007)* * at org.apache.mahout.sparkbindings.SparkEngine$.numNonZeroElementsPerColumn(SparkEngine.scala:81)* * at org.apache.mahout.math.drm.CheckpointedOps.numNonZeroElementsPerColumn(CheckpointedOps.scala:36)* * at org.apache.mahout.math.cf.SimilarityAnalysis$.sampleDownAndBinarize(SimilarityAnalysis.scala:397)* * at org.apache.mahout.math.cf.SimilarityAnalysis$$anonfun$cooccurrences$1.apply(SimilarityAnalysis.scala:101)* * at org.apache.mahout.math.cf.SimilarityAnalysis$$anonfun$cooccurrences$1.apply(SimilarityAnalysis.scala:95)* * at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)* * at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)* * at org.apache.mahout.math.cf.SimilarityAnalysis$.cooccurrences(SimilarityAnalysis.scala:95)* * at org.apache.mahout.math.cf.SimilarityAnalysis$.cooccurrencesIDSs(SimilarityAnalysis.scala:147)* * at org.template.URAlgorithm.calcAll(URAlgorithm.scala:280)* * at org.template.URAlgorithm.train(URAlgorithm.scala:251)* * at org.template.URAlgorithm.train(URAlgorithm.scala:169)* * at org.apache.predictionio.controller.P2LAlgorithm.trainBase(P2LAlgorithm.scala:49)* * at org.apache.predictionio.controller.Engine$$anonfun$18.apply(Engine.scala:692)* * at org.apache.predictionio.controller.Engine$$anonfun$18.apply(Engine.scala:692)* * at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)* * at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)* * at scala.collection.immutable.List.foreach(List.scala:318)* * at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)* * at scala.collection.AbstractTraversable.map(Traversable.scala:105)* * at org.apache.predictionio.controller.Engine$.train(Engine.scala:692)* * at org.apache.predictionio.controller.Engine.train(Engine.scala:177)* * at org.apache.predictionio.workflow.CoreWorkflow$.runTrain(CoreWorkflow.scala:67)* * at org.apache.predictionio.workflow.CreateWorkflow$.main(CreateWorkflow.scala:250)* * at org.apache.predictionio.workflow.CreateWorkflow.main(CreateWorkflow.scala)* * at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)* * at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)* * at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)* * at java.lang.reflect.Method.invoke(Method.java:498)* * at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)* * at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)* * at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)* * at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)* * at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)* *Caused by: java.lang.NegativeArraySizeException* * at org.apache.mahout.math.DenseVector.<init>(DenseVector.java:57)* * at org.apache.mahout.sparkbindings.SparkEngine$$anonfun$5.apply(SparkEngine.scala:73)* * at org.apache.mahout.sparkbindings.SparkEngine$$anonfun$5.apply(SparkEngine.scala:72)* * at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)* * at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)* * at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)* * at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)* * at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)* * at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)* * at org.apache.spark.scheduler.Task.run(Task.scala:89)* * at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227)* * at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)* * at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)* * at java.lang.Thread.run(Thread.java:748)* Now usually this message NegativeArraySizeException tells me that one of the events defined in engine.json doesn't exist in my dataset. However this is not the case here, my three events are present in my dataset. Here the proves: http://x.x.x.x:7070/events.json?accessKey=df8ef7dd-0165-4b6f-a008-d1550adbb3df&startTime=2017-06-2T0:0:00.321Z&limit=1&event=facet [{"eventId":"AYDE4TYMjU2dFGWVAYyUYwAAAVx5_afdpSyQHw_eNT0","event":"facet","entityType":"user","entityId":"92ec6a38-9fee-4c99-92a5-46677ad9ca48","targetEntityType":"item","targetEntityId":"alfa-romeo-marque","properties":{},"eventTime":"2017-06-05T20:41:25.725Z","creationTime":"2017-06-05T20:41:25.725Z"}] http://x.x.x.x:7070/events.json?accessKey=df8ef7dd-0165-4b6f-a008-d1550adbb3df&startTime=2017-06-2T0:0:00.321Z&limit=1&event=view [{"eventId":"IjuMNR7h40l_sylo-uqEsAAAAVxoIcPqnumP2B_qWAk","event":"view","entityType":"user","entityId":"bbc5bd25-b1ac-41e0-b771-43fe65a8827e","targetEntityType":"item","targetEntityId":"citroen-marque","properties":{},"eventTime":"2017-06-02T09:27:42.314Z","creationTime":"2017-06-02T09:27:42.314Z"}] http://x.x.x.x:7070/events.json?accessKey=df8ef7dd-0165-4b6f-a008-d1550adbb3df&startTime=2017-06-2T0:0:00.321Z&limit=1&event=search [{"eventId":"AI6NF05NJa3fP2bRpKUxAwAAAVxymnYYjm6nNt3TsGY","event":"search","entityType":"user","entityId":"b2c77901-0824-4583-9999-3cd56c1f34c9","targetEntityType":"item","targetEntityId":"peugeot-marque","properties":{},"eventTime":"2017-06-04T10:15:44.408Z","creationTime":"2017-06-04T10:15:44.408Z"}] I selected only one event per type but there are more. If I keep only the event types *facet *and *search*, then it works, the train succeeds and I have my model. However as soon as I add the event type *view*, it fails. I tried putting *view *as a primary event and it doesnt change anything. Not sure why it would change anything but I tried anyway. Here is my engine.json: *{ "comment":"", "id": "car", "description": "settings", "engineFactory": "org.template.RecommendationEngine", "datasource": { "params" : { "name": "sample-handmade-data.txt", "appName": "piourcar", "eventNames": ["facet","view","search"] } }, "sparkConf": { "spark.serializer": "org.apache.spark.serializer.KryoSerializer", "spark.kryo.registrator": "org.apache.mahout.sparkbindings.io.MahoutKryoRegistrator", "spark.kryo.referenceTracking": "false", "spark.kryoserializer.buffer": "300m", "es.index.auto.create": "true", "es.nodes":"espionode1:9200,espionode2:9200,espionode3:9200" }, "algorithms": [ { "name": "ur", "params": { "appName": "piourcar", "indexName": "urindex_car", "typeName": "items", "eventNames": ["facet","view","search"], "blacklistEvents": [], "maxEventsPerEventType": 50000, "maxCorrelatorsPerEventType": 100, "maxQueryEvents": 10, "num": 5, "userBias": 2, "returnSelf": true } } ] }* Thanks in advance for your help, regards, Bruno
