[SPARK-7654][MLlib] Migrate MLlib to the DataFrame reader/writer API.

Author: Reynold Xin <rxin@databricks.com>

Closes #6211 from rxin/mllib-reader and squashes the following commits:

79a2cb9 [Reynold Xin] [SPARK-7654][MLlib] Migrate MLlib to the DataFrame reader/writer API.

examples/src/main/scala/org/apache/spark/examples/mllib/DatasetExample.scala

@@ -103,7 +103,7 @@ object DatasetExample {
     tmpDir.deleteOnExit()
     val outputDir = new File(tmpDir, "dataset").toString
     println(s"Saving to $outputDir as Parquet file.")
-    df.saveAsParquetFile(outputDir)
+    df.write.parquet(outputDir)
 
     println(s"Loading Parquet file with UDT from $outputDir.")
     val newDataset = sqlContext.read.parquet(outputDir)

examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala

@@ -58,7 +58,7 @@ object RDDRelation {
     df.where($"key" === 1).orderBy($"value".asc).select($"key").collect().foreach(println)
 
     // Write out an RDD as a parquet file.
-    df.saveAsParquetFile("pair.parquet")
+    df.write.parquet("pair.parquet")
 
     // Read in parquet file.  Parquet files are self-describing so the schmema is preserved.
     val parquetFile = sqlContext.read.parquet("pair.parquet")

mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala

@@ -140,7 +140,7 @@ object NaiveBayesModel extends Loader[NaiveBayesModel] {
 
       // Create Parquet data.
       val dataRDD: DataFrame = sc.parallelize(Seq(data), 1).toDF()
-      dataRDD.saveAsParquetFile(dataPath(path))
+      dataRDD.write.parquet(dataPath(path))
     }
 
     def load(sc: SparkContext, path: String): NaiveBayesModel = {
@@ -186,7 +186,7 @@ object NaiveBayesModel extends Loader[NaiveBayesModel] {
 
       // Create Parquet data.
       val dataRDD: DataFrame = sc.parallelize(Seq(data), 1).toDF()
-      dataRDD.saveAsParquetFile(dataPath(path))
+      dataRDD.write.parquet(dataPath(path))
     }
 
     def load(sc: SparkContext, path: String): NaiveBayesModel = {

mllib/src/main/scala/org/apache/spark/mllib/classification/impl/GLMClassificationModel.scala

@@ -62,7 +62,7 @@ private[classification] object GLMClassificationModel {
 
       // Create Parquet data.
       val data = Data(weights, intercept, threshold)
-      sc.parallelize(Seq(data), 1).toDF().saveAsParquetFile(Loader.dataPath(path))
+      sc.parallelize(Seq(data), 1).toDF().write.parquet(Loader.dataPath(path))
     }
 
     /**

mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala

@@ -126,7 +126,7 @@ object GaussianMixtureModel extends Loader[GaussianMixtureModel] {
       val dataArray = Array.tabulate(weights.length) { i =>
         Data(weights(i), gaussians(i).mu, gaussians(i).sigma)
       }
-      sc.parallelize(dataArray, 1).toDF().saveAsParquetFile(Loader.dataPath(path))
+      sc.parallelize(dataArray, 1).toDF().write.parquet(Loader.dataPath(path))
     }
 
     def load(sc: SparkContext, path: String): GaussianMixtureModel = {

mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala

@@ -110,7 +110,7 @@ object KMeansModel extends Loader[KMeansModel] {
       val dataRDD = sc.parallelize(model.clusterCenters.zipWithIndex).map { case (point, id) =>
         Cluster(id, point)
       }.toDF()
-      dataRDD.saveAsParquetFile(Loader.dataPath(path))
+      dataRDD.write.parquet(Loader.dataPath(path))
     }
 
     def load(sc: SparkContext, path: String): KMeansModel = {

mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala

@@ -74,7 +74,7 @@ object PowerIterationClusteringModel extends Loader[PowerIterationClusteringMode
       sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path))
 
       val dataRDD = model.assignments.toDF()
-      dataRDD.saveAsParquetFile(Loader.dataPath(path))
+      dataRDD.write.parquet(Loader.dataPath(path))
     }
 
     def load(sc: SparkContext, path: String): PowerIterationClusteringModel = {
@@ -86,7 +86,7 @@ object PowerIterationClusteringModel extends Loader[PowerIterationClusteringMode
       assert(formatVersion == thisFormatVersion)
 
       val k = (metadata \ "k").extract[Int]
-      val assignments = sqlContext.parquetFile(Loader.dataPath(path))
+      val assignments = sqlContext.read.parquet(Loader.dataPath(path))
       Loader.checkSchema[PowerIterationClustering.Assignment](assignments.schema)
 
       val assignmentsRDD = assignments.map {

mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala

@@ -580,7 +580,7 @@ object Word2VecModel extends Loader[Word2VecModel] {
       sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path))
 
       val dataArray = model.toSeq.map { case (w, v) => Data(w, v) }
-      sc.parallelize(dataArray.toSeq, 1).toDF().saveAsParquetFile(Loader.dataPath(path))
+      sc.parallelize(dataArray.toSeq, 1).toDF().write.parquet(Loader.dataPath(path))
     }
   }
 

mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala

@@ -281,8 +281,8 @@ object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] {
       val metadata = compact(render(
         ("class" -> thisClassName) ~ ("version" -> thisFormatVersion) ~ ("rank" -> model.rank)))
       sc.parallelize(Seq(metadata), 1).saveAsTextFile(metadataPath(path))
-      model.userFeatures.toDF("id", "features").saveAsParquetFile(userPath(path))
+      model.userFeatures.toDF("id", "features").write.parquet(userPath(path))
-      model.productFeatures.toDF("id", "features").saveAsParquetFile(productPath(path))
+      model.productFeatures.toDF("id", "features").write.parquet(productPath(path))
     }
 
     def load(sc: SparkContext, path: String): MatrixFactorizationModel = {

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

@@ -184,7 +184,7 @@ object IsotonicRegressionModel extends Loader[IsotonicRegressionModel] {
 
       sqlContext.createDataFrame(
         boundaries.toSeq.zip(predictions).map { case (b, p) => Data(b, p) }
-      ).saveAsParquetFile(dataPath(path))
+      ).write.parquet(dataPath(path))
     }
 
     def load(sc: SparkContext, path: String): (Array[Double], Array[Double]) = {

mllib/src/main/scala/org/apache/spark/mllib/regression/impl/GLMRegressionModel.scala

@@ -60,7 +60,7 @@ private[regression] object GLMRegressionModel {
       val data = Data(weights, intercept)
       val dataRDD: DataFrame = sc.parallelize(Seq(data), 1).toDF()
       // TODO: repartition with 1 partition after SPARK-5532 gets fixed
-      dataRDD.saveAsParquetFile(Loader.dataPath(path))
+      dataRDD.write.parquet(Loader.dataPath(path))
     }
 
     /**

mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala

@@ -223,7 +223,7 @@ object DecisionTreeModel extends Loader[DecisionTreeModel] with Logging {
       val dataRDD: DataFrame = sc.parallelize(nodes)
         .map(NodeData.apply(0, _))
         .toDF()
-      dataRDD.saveAsParquetFile(Loader.dataPath(path))
+      dataRDD.write.parquet(Loader.dataPath(path))
     }
 
     def load(sc: SparkContext, path: String, algo: String, numNodes: Int): DecisionTreeModel = {

mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala

@@ -414,7 +414,7 @@ private[tree] object TreeEnsembleModel extends Logging {
       val dataRDD = sc.parallelize(model.trees.zipWithIndex).flatMap { case (tree, treeId) =>
         tree.topNode.subtreeIterator.toSeq.map(node => NodeData(treeId, node))
       }.toDF()
-      dataRDD.saveAsParquetFile(Loader.dataPath(path))
+      dataRDD.write.parquet(Loader.dataPath(path))
     }
 
     /**