Re: MLLIB model export: PMML vs MLLIB serialization
Hi Sourabh, I came across same problem as you. One workable solution for me was to serialize the parts of model that can be used again to recreate it. I serialize RDD's in my model using saveAsObjectFile with a time stamp attached to it in HDFS. My other spark application read from the latest stored dir from HDFS using sc.ObjectFile and recreate the recently trained model for prediction. I think this is not the best solution but it worked for me. I am also looking for other efficient approaches for such problem where exporting of model to some other application is required. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/MLLIB-model-export-PMML-vs-MLLIB-serialization-tp20324p20348.html Sent from the Apache Spark User List mailing list archive at Nabble.com. - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
Re: MLLib: loading saved model
Hi Sameer, Your model recreation should be: val model = new LinearRegressionModel(weights, intercept) As you have already got weights for linear regression model using stochastic gradient descent, you just have to use LinearRegressionModel to construct new model. Other points to notice is that weights should be in vector format so you have to convert weights to vector after reading from file and your intercept will be 0.0 as you mentioned. Regards, Manish -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/MLLib-loading-saved-model-tp20281p20354.html Sent from the Apache Spark User List mailing list archive at Nabble.com. - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
