collaborative filtering - Spark ALS with Sparse Implicit Dataset -


i trying run movieals example spark implicit dataset , receiving error:

got 3856988 ratings 144250 users on 378937 movies. training: 3085522, test: 771466. 15/07/13 10:43:07 warn blas: failed load implementation from: com.github.fommil.netlib.nativesystemblas 15/07/13 10:43:07 warn blas: failed load implementation from: com.github.fommil.netlib.nativerefblas 15/07/13 10:43:10 warn tasksetmanager: lost task 3.0 in stage 29.0 (tid 192, 10.162.45.33): java.lang.assertionerror: assertion failed: lapack.dppsv returned 1. @ scala.predef$.assert(predef.scala:179) @ org.apache.spark.ml.recommendation.als$choleskysolver.solve(als.scala:386) @ org.apache.spark.ml.recommendation.als$$anonfun$org$apache$spark$ml$recommendation$als$$computefactors$1.apply(als.scala:1163) @ org.apache.spark.ml.recommendation.als$$anonfun$org$apache$spark$ml$recommendation$als$$computefactors$1.apply(als.scala:1124) @ org.apache.spark.rdd.pairrddfunctions$$anonfun$mapvalues$1$$anonfun$apply$41$$anonfun$apply$42.apply(pairrddfunctions.scala:700) @ org.apache.spark.rdd.pairrddfunctions$$anonfun$mapvalues$1$$anonfun$apply$41$$anonfun$apply$42.apply(pairrddfunctions.scala:700) @ scala.collection.iterator$$anon$11.next(iterator.scala:328) @ org.apache.spark.storage.memorystore.unrollsafely(memorystore.scala:277) @ org.apache.spark.cachemanager.putinblockmanager(cachemanager.scala:171) @ org.apache.spark.cachemanager.getorcompute(cachemanager.scala:78) @ org.apache.spark.rdd.rdd.iterator(rdd.scala:242) @ org.apache.spark.rdd.mappartitionsrdd.compute(mappartitionsrdd.scala:35) @ org.apache.spark.rdd.rdd.computeorreadcheckpoint(rdd.scala:277) @ org.apache.spark.rdd.rdd.iterator(rdd.scala:244) @ org.apache.spark.scheduler.resulttask.runtask(resulttask.scala:63) @ org.apache.spark.scheduler.task.run(task.scala:70) @ org.apache.spark.executor.executor$taskrunner.run(executor.scala:213) @ java.util.concurrent.threadpoolexecutor.runworker(threadpoolexecutor.java:1145) @ java.util.concurrent.threadpoolexecutor$worker.run(threadpoolexecutor.java:615) @ java.lang.thread.run(thread.java:745)  15/07/13 10:43:10 error tasksetmanager: task 12 in stage 29.0 failed 4 times; aborting job exception in thread "main" org.apache.spark.sparkexception: job aborted due stage failure: task 12 in stage 29.0 failed 4 times, recent failure: lost task 12.3 in stage 29.0 (tid 249, 10.162.45.33): java.lang.assertionerror: assertion failed: lapack.dppsv returned 1. @ scala.predef$.assert(predef.scala:179) @ org.apache.spark.ml.recommendation.als$choleskysolver.solve(als.scala:386) @ org.apache.spark.ml.recommendation.als$$anonfun$org$apache$spark$ml$recommendation$als$$computefactors$1.apply(als.scala:1163) @ org.apache.spark.ml.recommendation.als$$anonfun$org$apache$spark$ml$recommendation$als$$computefactors$1.apply(als.scala:1124) @ org.apache.spark.rdd.pairrddfunctions$$anonfun$mapvalues$1$$anonfun$apply$41$$anonfun$apply$42.apply(pairrddfunctions.scala:700) @ org.apache.spark.rdd.pairrddfunctions$$anonfun$mapvalues$1$$anonfun$apply$41$$anonfun$apply$42.apply(pairrddfunctions.scala:700) @ scala.collection.iterator$$anon$11.next(iterator.scala:328) @ org.apache.spark.storage.memorystore.unrollsafely(memorystore.scala:277) @ org.apache.spark.cachemanager.putinblockmanager(cachemanager.scala:171) @ org.apache.spark.cachemanager.getorcompute(cachemanager.scala:78) @ org.apache.spark.rdd.rdd.iterator(rdd.scala:242) @ org.apache.spark.rdd.mappartitionsrdd.compute(mappartitionsrdd.scala:35) @ org.apache.spark.rdd.rdd.computeorreadcheckpoint(rdd.scala:277) @ org.apache.spark.rdd.rdd.iterator(rdd.scala:244) @ org.apache.spark.scheduler.resulttask.runtask(resulttask.scala:63) @ org.apache.spark.scheduler.task.run(task.scala:70) @ org.apache.spark.executor.executor$taskrunner.run(executor.scala:213) @ java.util.concurrent.threadpoolexecutor.runworker(threadpoolexecutor.java:1145) @ java.util.concurrent.threadpoolexecutor$worker.run(threadpoolexecutor.java:615) @ java.lang.thread.run(thread.java:745)  driver stacktrace: @ org.apache.spark.scheduler.dagscheduler.org$apache$spark$scheduler$dagscheduler$$failjobandindependentstages(dagscheduler.scala:1266) @ org.apache.spark.scheduler.dagscheduler$$anonfun$abortstage$1.apply(dagscheduler.scala:1257) @ org.apache.spark.scheduler.dagscheduler$$anonfun$abortstage$1.apply(dagscheduler.scala:1256) @ scala.collection.mutable.resizablearray$class.foreach(resizablearray.scala:59) @ scala.collection.mutable.arraybuffer.foreach(arraybuffer.scala:47) @ org.apache.spark.scheduler.dagscheduler.abortstage(dagscheduler.scala:1256) @ org.apache.spark.scheduler.dagscheduler$$anonfun$handletasksetfailed$1.apply(dagscheduler.scala:730) @ org.apache.spark.scheduler.dagscheduler$$anonfun$handletasksetfailed$1.apply(dagscheduler.scala:730) @ scala.option.foreach(option.scala:236) @ org.apache.spark.scheduler.dagscheduler.handletasksetfailed(dagscheduler.scala:730) @ org.apache.spark.scheduler.dagschedulereventprocessloop.onreceive(dagscheduler.scala:1450) @ org.apache.spark.scheduler.dagschedulereventprocessloop.onreceive(dagscheduler.scala:1411) @ org.apache.spark.util.eventloop$$anon$1.run(eventloop.scala:48)

i unsure if due sparsity of dataset. works fine when trained explicitly. however, since dataset binary, error rate obtained explicit als model not accurate.

would possible me out? thank you, ben


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