[SPARK-14312][ML][SPARKR] NaiveBayes model persistence in SparkR

## What changes were proposed in this pull request?
SparkR ```NaiveBayesModel``` supports ```save/load``` by the following API:
```
df <- createDataFrame(sqlContext, infert)
model <- naiveBayes(education ~ ., df, laplace = 0)
ml.save(model, path)
model2 <- ml.load(path)
```

## How was this patch tested?
Add unit tests.

cc mengxr

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #12573 from yanboliang/spark-14312.

2 files changed, 94 insertions, 3 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/NaiveBayesWrapper.scala b/mllib/src/main/scala/org/apache/spark/ml/r/NaiveBayesWrapper.scala
index b17207e99b..27c7e72881 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/r/NaiveBayesWrapper.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/r/NaiveBayesWrapper.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.ml.{Pipeline, PipelineModel}
 import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, NominalAttribute}
 import org.apache.spark.ml.classification.{NaiveBayes, NaiveBayesModel}
 import org.apache.spark.ml.feature.{IndexToString, RFormula}
+import org.apache.spark.ml.util._
 import org.apache.spark.sql.{DataFrame, Dataset}
 
 private[r] class NaiveBayesWrapper private (
-    pipeline: PipelineModel,
+    val pipeline: PipelineModel,
     val labels: Array[String],
-    val features: Array[String]) {
+    val features: Array[String]) extends MLWritable {
 
   import NaiveBayesWrapper._
 
@@ -41,9 +48,11 @@ private[r] class NaiveBayesWrapper private (
       .drop(PREDICTED_LABEL_INDEX_COL)
       .drop(naiveBayesModel.getFeaturesCol)
   }
+
+  override def write: MLWriter = new NaiveBayesWrapper.NaiveBayesWrapperWriter(this)
 }
 
-private[r] object NaiveBayesWrapper {
+private[r] object NaiveBayesWrapper extends MLReadable[NaiveBayesWrapper] {
 
   val PREDICTED_LABEL_INDEX_COL = "pred_label_idx"
   val PREDICTED_LABEL_COL = "prediction"
@@ -74,4 +83,41 @@ private[r] object NaiveBayesWrapper {
       .fit(data)
     new NaiveBayesWrapper(pipeline, labels, features)
   }
+
+  override def read: MLReader[NaiveBayesWrapper] = new NaiveBayesWrapperReader
+
+  override def load(path: String): NaiveBayesWrapper = super.load(path)
+
+  class NaiveBayesWrapperWriter(instance: NaiveBayesWrapper) 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) ~
+        ("labels" -> instance.labels.toSeq) ~
+        ("features" -> instance.features.toSeq)
+      val rMetadataJson: String = compact(render(rMetadata))
+      sc.parallelize(Seq(rMetadataJson), 1).saveAsTextFile(rMetadataPath)
+
+      instance.pipeline.save(pipelinePath)
+    }
+  }
+
+  class NaiveBayesWrapperReader extends MLReader[NaiveBayesWrapper] {
+
+    override def load(path: String): NaiveBayesWrapper = {
+      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 labels = (rMetadata \ "labels").extract[Array[String]]
+      val features = (rMetadata \ "features").extract[Array[String]]
+
+      val pipeline = PipelineModel.load(pipelinePath)
+      new NaiveBayesWrapper(pipeline, labels, 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
new file mode 100644
index 0000000000..7f6f147532
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/r/RWrappers.scala
@@ -0,0 +1,45 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.r
+
+import org.apache.hadoop.fs.Path
+import org.json4s.DefaultFormats
+import org.json4s.jackson.JsonMethods._
+
+import org.apache.spark.SparkException
+import org.apache.spark.ml.util.MLReader
+
+/**
+ * This is the Scala stub of SparkR ml.load. It will dispatch the call to corresponding
+ * model wrapper loading function according the class name extracted from rMetadata of the path.
+ */
+private[r] object RWrappers extends MLReader[Object] {
+
+  override def load(path: String): Object = {
+    implicit val format = DefaultFormats
+    val rMetadataPath = new Path(path, "rMetadata").toString
+    val rMetadataStr = sc.textFile(rMetadataPath, 1).first()
+    val rMetadata = parse(rMetadataStr)
+    val className = (rMetadata \ "class").extract[String]
+    className match {
+      case "org.apache.spark.ml.r.NaiveBayesWrapper" => NaiveBayesWrapper.load(path)
+      case _ =>
+        throw new SparkException(s"SparkR ml.load does not support load $className")
+    }
+  }
+}