[
https://issues.apache.org/jira/browse/SPARK-14311?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15238500#comment-15238500
]
Yanbo Liang commented on SPARK-14311:
-------------------------------------
Sure, I can have a try.
Another issue is R `Object` has feature names in the model, but we did not
store them in the Scala PipelineModel. I think this should be done in further
tasks, but how did we handle this issue in the current implementation? Should
we record the feature and label names in extraMetadata of PipelineModel?
> Model persistence in SparkR
> ---------------------------
>
> 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
