address comments

mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala

@@ -133,9 +133,7 @@ class IsotonicRegressionModel (
   }
 
   override def save(sc: SparkContext, path: String): Unit = {
-    val intervals = boundaries.toList.zip(predictions.toList).toArray
-    val data = IsotonicRegressionModel.SaveLoadV1_0.Data(intervals)
-    IsotonicRegressionModel.SaveLoadV1_0.save(sc, path, data, isotonic)
+    IsotonicRegressionModel.SaveLoadV1_0.save(sc, path, boundaries, predictions, isotonic)
   }
 
   override protected def formatVersion: String = "1.0"
@@ -153,9 +151,14 @@ object IsotonicRegressionModel extends Loader[IsotonicRegressionModel] {
     def thisClassName: String = "org.apache.spark.mllib.regression.IsotonicRegressionModel"
 
     /** Model data for model import/export */
-    case class Data(intervals: Array[(Double, Double)])
-
-    def save(sc: SparkContext, path: String, data: Data, isotonic: Boolean): Unit = {
+    case class Data(boundary: Double, prediction: Double)
+
+    def save(
+        sc: SparkContext, 
+        path: String, 
+        boundaries: Array[Double], 
+        predictions: Array[Double], 
+        isotonic: Boolean): Unit = {
       val sqlContext = new SQLContext(sc)
       import sqlContext.implicits._
 
@@ -164,21 +167,18 @@ object IsotonicRegressionModel extends Loader[IsotonicRegressionModel] {
           ("isotonic" -> isotonic)))
       sc.parallelize(Seq(metadata), 1).saveAsTextFile(metadataPath(path))
 
-      val dataRDD: DataFrame = sc.parallelize(Seq(data), 1).toDF()
-      dataRDD.saveAsParquetFile(dataPath(path))
+      sqlContext.createDataFrame(boundaries.toList.zip(predictions.toList)
+        .map { case (b, p) => Data(b, p) }).saveAsParquetFile(dataPath(path))
     }
 
     def load(sc: SparkContext, path: String): (Array[Double], Array[Double]) = {
       val sqlContext = new SQLContext(sc)
       val dataRDD = sqlContext.parquetFile(dataPath(path))
 
       checkSchema[Data](dataRDD.schema)
-      val dataArray = dataRDD.select("intervals").take(1)
-      assert(dataArray.size == 1,
-        s"Unable to load IsotonicRegressionModel data from: ${dataPath(path)}")
-      val data = dataArray(0)
-      val intervals = data.getAs[Seq[(Double, Double)]](0)
-      val (boundaries, predictions) = intervals.unzip
+      val dataArray = dataRDD.select("boundary", "prediction").collect()
+      val (boundaries, predictions) = dataArray.map { 
+        x => (x.getAs[Double](0), x.getAs[Double](1)) }.toList.unzip
       (boundaries.toArray, predictions.toArray)
     }
   }