[ https://issues.apache.org/jira/browse/SPARK-14311?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Xiangrui Meng updated SPARK-14311: ---------------------------------- Summary: Model persistence in SparkR 2.0 (was: Model persistence in SparkR) > Model persistence in SparkR 2.0 > ------------------------------- > > Key: SPARK-14311 > URL: https://issues.apache.org/jira/browse/SPARK-14311 > Project: Spark > Issue Type: Umbrella > Components: ML, SparkR > Reporter: Xiangrui Meng > Assignee: Xiangrui Meng > > In Spark 2.0, we are going to have 4 ML models in SparkR: GLMs, k-means, > naive Bayes, and AFT survival regression. Users can fit models, get summary, > and make predictions. However, they cannot save/load the models yet. > ML models in SparkR are wrappers around ML pipelines. So it should be > straightforward to implement model persistence. We need to think more about > the API. R uses save/load for objects and datasets (also objects). It is > possible to overload save for ML models, e.g., save.NaiveBayesWrapper. But > I'm not sure whether load can be overloaded easily. I propose the following > API: > {code} > model <- glm(formula, data = df) > ml.save(model, path, mode = "overwrite") > model2 <- ml.load(path) > {code} > We defined wrappers as S4 classes. So `ml.save` is an S4 method and ml.load > is a S3 method (correct me if I'm wrong). -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org