Major 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:
model <- glm(formula, data = df)
ml.save(model, path, mode = "overwrite")
model2 <- ml.load(path)
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).
- is related to
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SPARK-6725 Model export/import for Pipeline API (Scala)
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- Resolved
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- relates to
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SPARK-14831 Make ML APIs in SparkR consistent
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- Resolved
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1.
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NaiveBayes model persistence in SparkR |
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Resolved | Yanbo Liang | |
2.
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AFTSurvivalRegression model persistence in SparkR |
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Resolved | Yanbo Liang | |
3.
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K-means model persistence in SparkR |
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Resolved | Gayathri Murali | |
4.
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GLMs model persistence in SparkR |
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Resolved | Gayathri Murali |