[jira] [Created] (SPARK-14311) Model persistence in SparkR

["Xiangrui Meng (JIRA)" <ji...@apache.org>]

Xiangrui Meng created SPARK-14311:
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             Summary: 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}



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