[SPARK-14324] Refactor GLMs code in SparkRWrappers

• Type: Improvement
• Status: Resolved
• Priority: Major
• Resolution: Fixed
• Fix Version/s: 2.0.0
• Component/s: ML, SparkR
• Target Version/s: 2.0.0
• Assignee: Yanbo Liang
• Reporter: Xiangrui Meng
• Shepherd: Xiangrui Meng
• Created: 01/Apr/16 06:51
• Updated: 12/Apr/16 17:52
• Resolved: 12/Apr/16 17:52

Description

We use a single object `SparkRWrappers` (https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/r/SparkRWrappers.scala) to wrap method calls to glm and kmeans in SparkR. This is quite hard to maintain. We should refactor them into separate wrappers, like `AFTSurvivalRegressionWrapper` and `NaiveBayesWrapper`.

The package name should be `spakr.ml.r` instead of `spark.ml.api.r`.

Issue Links

• blocks: SPARK-14315 GLMs model persistence in SparkR
• is a clone of: SPARK-14303 Refactor k-means code in SparkRWrappers
• is blocked by: SPARK-12566 GLM model family, link function support in SparkR:::glm
• links to: [Github] Pull Request #12294 (yanboliang)