[SPARK-7537] [MLLIB] spark.mllib API updates

Minor updates to the spark.mllib APIs:

1. Add `DeveloperApi` to `PMMLExportable` and add `Experimental` to `toPMML` methods.
2. Mention `RankingMetrics.of` in the `RankingMetrics` constructor.

Author: Xiangrui Meng <meng@databricks.com>

Closes #6280 from mengxr/SPARK-7537 and squashes the following commits:

1bd2583 [Xiangrui Meng] organize imports
94afa7a [Xiangrui Meng] mark all toPMML methods experimental
4c40da1 [Xiangrui Meng] mention the factory method for RankingMetrics for Java users
88c62d0 [Xiangrui Meng] add DeveloperApi to PMMLExportable

(cherry picked from commit 2ad4837cfa)
Signed-off-by: Xiangrui Meng <meng@databricks.com>

mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala

@@ -31,6 +31,8 @@ import org.apache.spark.rdd.RDD
  * ::Experimental::
  * Evaluator for ranking algorithms.
  *
+ * Java users should use [[RankingMetrics$.of]] to create a [[RankingMetrics]] instance.
+ *
  * @param predictionAndLabels an RDD of (predicted ranking, ground truth set) pairs.
  */
 @Experimental

mllib/src/main/scala/org/apache/spark/mllib/pmml/PMMLExportable.scala

@@ -23,13 +23,16 @@ import javax.xml.transform.stream.StreamResult
 import org.jpmml.model.JAXBUtil
 
 import org.apache.spark.SparkContext
+import org.apache.spark.annotation.{DeveloperApi, Experimental}
 import org.apache.spark.mllib.pmml.export.PMMLModelExportFactory
 
 /**
+ * :: DeveloperApi ::
  * Export model to the PMML format
  * Predictive Model Markup Language (PMML) is an XML-based file format
  * developed by the Data Mining Group (www.dmg.org).
  */
+@DeveloperApi
 trait PMMLExportable {
 
   /**
@@ -41,30 +44,38 @@ trait PMMLExportable {
   }
 
   /**
+   * :: Experimental ::
    * Export the model to a local file in PMML format
    */
+  @Experimental
   def toPMML(localPath: String): Unit = {
     toPMML(new StreamResult(new File(localPath)))
   }
 
   /**
+   * :: Experimental ::
    * Export the model to a directory on a distributed file system in PMML format
    */
+  @Experimental
   def toPMML(sc: SparkContext, path: String): Unit = {
     val pmml = toPMML()
     sc.parallelize(Array(pmml), 1).saveAsTextFile(path)
   }
 
   /**
+   * :: Experimental ::
    * Export the model to the OutputStream in PMML format
    */
+  @Experimental
   def toPMML(outputStream: OutputStream): Unit = {
     toPMML(new StreamResult(outputStream))
   }
 
   /**
+   * :: Experimental ::
    * Export the model to a String in PMML format
    */
+  @Experimental
   def toPMML(): String = {
     val writer = new StringWriter
     toPMML(new StreamResult(writer))