Yes the three event types that I defined in the engine.json exist in my dataset, facet is my primary, I checked that it exists.
I think it is not needed to build again when changing something in the engine.json, as the file is read in the process but I built it and tried again and I still have the same error. What is this example-intrigration? I dont know about this. Where can I find this script? 2017-06-07 11:11 GMT+02:00 Vaghawan Ojha <[email protected]>: > Hi, > > For me this problem had happened when I had mistaken my primary events. > The first eventName in the eventName array "eventNames": > ["facet","view","search"] is primary. There is that event in your data. > > Did you make sure, you built the app again when you changed the eventName > in engine.json? > > Also you could varify everything's fine with UR with > ./example-intrigration. > > Thanks > > On Wed, Jun 7, 2017 at 2:49 PM, Bruno LEBON <[email protected]> wrote: > >> Thanks for your answer. >> >> *You could explicitly do * >> >> >> *pio train -- --master spark://localhost:7077 --driver-memory 16G >> --executor-memory 24G * >> >> *and change the spark master url and the memories configuration. And see >> if that works. * >> >> Yes that is the command I use to launch the train, except I am on a >> cluster, so Spark is not local. Here is mine: >> pio train -- --master spark://master:7077 --driver-memory 4g >> --executor-memory 10g >> >> The train works with different datasets, it also works with this dataset >> when I skip the event type *view*. So my guess is that there is >> something about this event type, either in the data but the data looks fine >> to me, or maybe there is a problem when I use more than two types of event >> (this is the first time I have more than two, however I can't believe that >> the problem is related the a number of event types). >> >> The spelling is the same in the event sent to the eventserver ( *view *) >> and in the engine.json ( *view *). >> >> I am reading the code to figure out where this error comes from. >> >> >> >> 2017-06-07 10:17 GMT+02:00 Vaghawan Ojha <[email protected]>: >> >>> You could explicitly do >>> >>> pio train -- --master spark://localhost:7077 --driver-memory 16G >>> --executor-memory 24G >>> >>> and change the spark master url and the memories configuration. And see >>> if that works. >>> >>> Thanks >>> >>> On Wed, Jun 7, 2017 at 1:55 PM, Bruno LEBON <[email protected]> wrote: >>> >>>> 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.com >>>> <http://ip-172-31-40-139.eu-west-1.com>pute.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$$anonfun$reduce$1.apply(RDD.scala:1025)* >>>> * at >>>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)* >>>> * at >>>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)* >>>> * at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)* >>>> * at org.apache.spark.rdd.RDD.reduce(RDD.scala:1007)* >>>> * at >>>> org.apache.mahout.sparkbindings.SparkEngine$.numNonZeroElementsPerColumn(SparkEngine.scala:81)* >>>> * at >>>> org.apache.mahout.math.drm.CheckpointedOps.numNonZeroElementsPerColumn(CheckpointedOps.scala:36)* >>>> * at org.apache.mahout.math.cf >>>> <http://org.apache.mahout.math.cf>.SimilarityAnalysis$.sampleDownAndBinarize(SimilarityAnalysis.scala:397)* >>>> * at org.apache.mahout.math.cf >>>> <http://org.apache.mahout.math.cf>.SimilarityAnalysis$$anonfun$cooccurrences$1.apply(SimilarityAnalysis.scala:101)* >>>> * at org.apache.mahout.math.cf >>>> <http://org.apache.mahout.math.cf>.SimilarityAnalysis$$anonfun$cooccurrences$1.apply(SimilarityAnalysis.scala:95)* >>>> * at >>>> scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)* >>>> * at >>>> scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)* >>>> * at org.apache.mahout.math.cf >>>> <http://org.apache.mahout.math.cf>.SimilarityAnalysis$.cooccurrences(SimilarityAnalysis.scala:95)* >>>> * at org.apache.mahout.math.cf >>>> <http://org.apache.mahout.math.cf>.SimilarityAnalysis$.cooccurrencesIDSs(SimilarityAnalysis.scala:147)* >>>> * at org.template.URAlgorithm.calcAll(URAlgorithm.scala:280)* >>>> * at org.template.URAlgorithm.train(URAlgorithm.scala:251)* >>>> * at org.template.URAlgorithm.train(URAlgorithm.scala:169)* >>>> * at >>>> org.apache.predictionio.controller.P2LAlgorithm.trainBase(P2LAlgorithm.scala:49)* >>>> * at >>>> org.apache.predictionio.controller.Engine$$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 >>>> <http://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 >>>> >>>> >>>> >>>> >>>> >>>> >>> >> >
