Ah cool, thanks for the link! On 6 December 2016 at 12:25, Nick Pentreath <nick.pentre...@gmail.com> wrote:
> Indeed, it's being tracked here: https://issues.apache. > org/jira/browse/SPARK-18230 though no Pr has been opened yet. > > > On Tue, 6 Dec 2016 at 13:36 chris snow <chsnow...@gmail.com> wrote: > >> I'm using the MatrixFactorizationModel.predict() method and encountered >> the following exception: >> >> Name: java.util.NoSuchElementException >> Message: next on empty iterator >> StackTrace: scala.collection.Iterator$$anon$2.next(Iterator.scala:39) >> scala.collection.Iterator$$anon$2.next(Iterator.scala:37) >> scala.collection.IndexedSeqLike$Elements.next(IndexedSeqLike.scala:64) >> scala.collection.IterableLike$class.head(IterableLike.scala:91) >> scala.collection.mutable.ArrayBuffer.scala$collection$ >> IndexedSeqOptimized$$super$head(ArrayBuffer.scala:47) >> scala.collection.IndexedSeqOptimized$class.head(IndexedSeqOptimized. >> scala:120) >> scala.collection.mutable.ArrayBuffer.head(ArrayBuffer.scala:47) >> org.apache.spark.mllib.recommendation.MatrixFactorizationModel.predict( >> MatrixFactorizationModel.scala:81) >> $line78.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$ >> iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:74) >> $line78.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$ >> iwC$$iwC$$iwC$$iwC.<init>(<console>:79) >> $line78.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$ >> iwC$$iwC$$iwC.<init>(<console>:81) >> $line78.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$ >> iwC$$iwC.<init>(<console>:83) >> $line78.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$ >> iwC.<init>(<console>:85) >> $line78.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.< >> init>(<console>:87) >> $line78.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init> >> (<console>:89) >> $line78.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:91) >> $line78.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:93) >> $line78.$read$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:95) >> $line78.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:97) >> $line78.$read$$iwC$$iwC$$iwC.<init>(<console>:99) >> $line78.$read$$iwC$$iwC.<init>(<console>:101) >> $line78.$read$$iwC.<init>(<console>:103) >> $line78.$read.<init>(<console>:105) >> $line78.$read$.<init>(<console>:109) >> $line78.$read$.<clinit>(<console>) >> $line78.$eval$.<init>(<console>:7) >> $line78.$eval$.<clinit>(<console>) >> $line78.$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) >> >> This took some debugging to figure out why I received the Exception, but >> when looking at the predict() implementation, I seems to assume that there >> will always be features found for the provided user and product ids: >> >> >> /** Predict the rating of one user for one product. */ >> @Since("0.8.0") >> def predict(user: Int, product: Int): Double = { >> val userVector = userFeatures.lookup(user).head >> val productVector = productFeatures.lookup(product).head >> blas.ddot(rank, userVector, 1, productVector, 1) >> } >> >> It would be helpful if a more useful exception was raised, e.g. >> >> MissingUserFeatureException : "User ID ${user} not found in model" >> MissingProductFeatureException : "Product ID ${product} not found in >> model" >> >> WDYT? >> >> >> >>
