[SPARK-14313][ML][SPARKR] AFTSurvivalRegression model persistence in SparkR

## What changes were proposed in this pull request?
```AFTSurvivalRegressionModel``` supports ```save/load``` in SparkR.

## How was this patch tested?
Unit tests.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #12685 from yanboliang/spark-14313.

2 files changed, 51 insertions, 3 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/AFTSurvivalRegressionWrapper.scala b/mllib/src/main/scala/org/apache/spark/ml/r/AFTSurvivalRegressionWrapper.scala
index 7835468626..a442469e4d 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/r/AFTSurvivalRegressionWrapper.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/r/AFTSurvivalRegressionWrapper.scala
@@ -17,16 +17,23 @@
 
 package org.apache.spark.ml.r
 
+import org.apache.hadoop.fs.Path
+import org.json4s._
+import org.json4s.DefaultFormats
+import org.json4s.JsonDSL._
+import org.json4s.jackson.JsonMethods._
+
 import org.apache.spark.SparkException
 import org.apache.spark.ml.{Pipeline, PipelineModel}
 import org.apache.spark.ml.attribute.AttributeGroup
 import org.apache.spark.ml.feature.RFormula
 import org.apache.spark.ml.regression.{AFTSurvivalRegression, AFTSurvivalRegressionModel}
+import org.apache.spark.ml.util._
 import org.apache.spark.sql.{DataFrame, Dataset}
 
 private[r] class AFTSurvivalRegressionWrapper private (
-    pipeline: PipelineModel,
-    features: Array[String]) {
+    val pipeline: PipelineModel,
+    val features: Array[String]) extends MLWritable {
 
   private val aftModel: AFTSurvivalRegressionModel =
     pipeline.stages(1).asInstanceOf[AFTSurvivalRegressionModel]
@@ -46,9 +53,12 @@ private[r] class AFTSurvivalRegressionWrapper private (
   def transform(dataset: Dataset[_]): DataFrame = {
     pipeline.transform(dataset).drop(aftModel.getFeaturesCol)
   }
+
+  override def write: MLWriter =
+    new AFTSurvivalRegressionWrapper.AFTSurvivalRegressionWrapperWriter(this)
 }
 
-private[r] object AFTSurvivalRegressionWrapper {
+private[r] object AFTSurvivalRegressionWrapper extends MLReadable[AFTSurvivalRegressionWrapper] {
 
   private def formulaRewrite(formula: String): (String, String) = {
     var rewritedFormula: String = null
@@ -96,4 +106,40 @@ private[r] object AFTSurvivalRegressionWrapper {
 
     new AFTSurvivalRegressionWrapper(pipeline, features)
   }
+
+  override def read: MLReader[AFTSurvivalRegressionWrapper] = new AFTSurvivalRegressionWrapperReader
+
+  override def load(path: String): AFTSurvivalRegressionWrapper = super.load(path)
+
+  class AFTSurvivalRegressionWrapperWriter(instance: AFTSurvivalRegressionWrapper)
+    extends MLWriter {
+
+    override protected def saveImpl(path: String): Unit = {
+      val rMetadataPath = new Path(path, "rMetadata").toString
+      val pipelinePath = new Path(path, "pipeline").toString
+
+      val rMetadata = ("class" -> instance.getClass.getName) ~
+        ("features" -> instance.features.toSeq)
+      val rMetadataJson: String = compact(render(rMetadata))
+      sc.parallelize(Seq(rMetadataJson), 1).saveAsTextFile(rMetadataPath)
+
+      instance.pipeline.save(pipelinePath)
+    }
+  }
+
+  class AFTSurvivalRegressionWrapperReader extends MLReader[AFTSurvivalRegressionWrapper] {
+
+    override def load(path: String): AFTSurvivalRegressionWrapper = {
+      implicit val format = DefaultFormats
+      val rMetadataPath = new Path(path, "rMetadata").toString
+      val pipelinePath = new Path(path, "pipeline").toString
+
+      val rMetadataStr = sc.textFile(rMetadataPath, 1).first()
+      val rMetadata = parse(rMetadataStr)
+      val features = (rMetadata \ "features").extract[Array[String]]
+
+      val pipeline = PipelineModel.load(pipelinePath)
+      new AFTSurvivalRegressionWrapper(pipeline, features)
+    }
+  }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/RWrappers.scala b/mllib/src/main/scala/org/apache/spark/ml/r/RWrappers.scala
index 7f6f147532..06baedf2a2 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/r/RWrappers.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/r/RWrappers.scala
@@ -38,6 +38,8 @@ private[r] object RWrappers extends MLReader[Object] {
     val className = (rMetadata \ "class").extract[String]
     className match {
       case "org.apache.spark.ml.r.NaiveBayesWrapper" => NaiveBayesWrapper.load(path)
+      case "org.apache.spark.ml.r.AFTSurvivalRegressionWrapper" =>
+        AFTSurvivalRegressionWrapper.load(path)
       case _ =>
         throw new SparkException(s"SparkR ml.load does not support load $className")
     }