It looks like you mixed use ALS in spark.ml and spark.mllib package. You can train the model by either one, meanwhile, you should use the corresponding save/load functions. You can not train/save the model by spark.mllib ALS, and then use spark.ml ALS to load the model. It will throw exceptions.
I saw you use Pipeline to train ALS under spark.ml package. Then you should use PipelineModel.load to read the model and get corresponding stage in the pipeline as the ALSModel. We strongly recommend you to use the spark.ml package which is the primary API of MLlib. The spark.mllib package is in maintenance mode. So do all your work under the same APIs. Thanks Yanbo 2016-08-17 1:30 GMT-07:00 <luohui20...@sina.com>: > Hello guys: > I have a problem in loading recommend model. I have 2 models, one is > good(able to get recommend result) and another is not working. I checked > these 2 models, both are MatrixFactorizationModel object. But in the > metadata, one is a PipelineModel and another is a MatrixFactorizationModel. > Is below exception caused by this? > > here are my stack trace: > Exception in thread "main" org.json4s.package$MappingException: Did not > find value which can be converted into java.lang.String > at org.json4s.reflect.package$.fail(package.scala:96) > at org.json4s.Extraction$.convert(Extraction.scala:554) > at org.json4s.Extraction$.extract(Extraction.scala:331) > at org.json4s.Extraction$.extract(Extraction.scala:42) > at org.json4s.ExtractableJsonAstNode.extract( > ExtractableJsonAstNode.scala:21) > at org.apache.spark.mllib.util.Loader$.loadMetadata( > modelSaveLoad.scala:131) > at org.apache.spark.mllib.recommendation. > MatrixFactorizationModel$.load(MatrixFactorizationModel.scala:330) > at org.brave.spark.ml.RecommandForMultiUsers$.main( > RecommandForMultiUsers.scala:55) > at org.brave.spark.ml.RecommandForMultiUsers.main( > RecommandForMultiUsers.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:497) > 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) > > The attached files are my codes, FYI. > RecommandForMultiUsers.scala:55 is : > val model = MatrixFactorizationModel.load(sc, modelpath) > > > -------------------------------- > > Thanks&Best regards! > San.Luo > > > --------------------------------------------------------------------- > To unsubscribe e-mail: user-unsubscr...@spark.apache.org >
