[SPARK-5900][MLLIB] make PIC and FPGrowth Java-friendly

In the previous version, PIC stores clustering assignments as an `RDD[(Long, Int)]`. This is mapped to `RDD<Tuple2<Object, Object>>` in Java and hence Java users have to cast types manually. We should either create a new method called `javaAssignments` that returns `JavaRDD[(java.lang.Long, java.lang.Int)]` or wrap the result pair in a class. I chose the latter approach in this PR. Now assignments are stored as an `RDD[Assignment]`, where `Assignment` is a class with `id` and `cluster`.

Similarly, in FPGrowth, the frequent itemsets are stored as an `RDD[(Array[Item], Long)]`, which is mapped to `RDD<Tuple2<Object, Object>>`. Though we provide a "Java-friendly" method `javaFreqItemsets` that returns `JavaRDD[(Array[Item], java.lang.Long)]`. It doesn't really work because `Array[Item]` is mapped to `Object` in Java. So in this PR I created a class `FreqItemset` to wrap the results. It has `items` and `freq`, as well as a `javaItems` method that returns `List<Item>` in Java.

I'm not certain that the names I chose are proper: `Assignment`/`id`/`cluster` and `FreqItemset`/`items`/`freq`. Please let me know if there are better suggestions.

CC: jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #4695 from mengxr/SPARK-5900 and squashes the following commits:

865b5ca [Xiangrui Meng] make Assignment serializable
cffa96e [Xiangrui Meng] fix test
9c0e590 [Xiangrui Meng] remove unused Tuple2
1b9db3d [Xiangrui Meng] make PIC and FPGrowth Java-friendly

2 files changed, 4 insertions, 8 deletions

diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/FPGrowthExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/FPGrowthExample.scala
index ae66107d70..aaae275ec5 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/FPGrowthExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/FPGrowthExample.scala
@@ -42,8 +42,8 @@ object FPGrowthExample {
       .setMinSupport(0.3)
     val model = fpg.run(transactions)
 
-    model.freqItemsets.collect().foreach { case (itemset, freq) =>
-      println(itemset.mkString("[", ",", "]") + ", " + freq)
+    model.freqItemsets.collect().foreach { itemset =>
+      println(itemset.items.mkString("[", ",", "]") + ", " + itemset.freq)
     }
 
     sc.stop()
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala
index b2373adba1..91c9772744 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala
@@ -44,8 +44,7 @@ import org.apache.spark.{SparkConf, SparkContext}
  *
  * Here is a sample run and output:
  *
- * ./bin/run-example mllib.PowerIterationClusteringExample
- * -k 3 --n 30 --maxIterations 15
+ * ./bin/run-example mllib.PowerIterationClusteringExample -k 3 --n 30 --maxIterations 15
  *
  * Cluster assignments: 1 -> [0,1,2,3,4],2 -> [5,6,7,8,9,10,11,12,13,14],
  * 0 -> [15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]
@@ -103,7 +102,7 @@ object PowerIterationClusteringExample {
       .setMaxIterations(params.maxIterations)
       .run(circlesRdd)
 
-    val clusters = model.assignments.collect.groupBy(_._2).mapValues(_.map(_._1))
+    val clusters = model.assignments.collect().groupBy(_.cluster).mapValues(_.map(_.id))
     val assignments = clusters.toList.sortBy { case (k, v) => v.length}
     val assignmentsStr = assignments
       .map { case (k, v) =>
@@ -153,8 +152,5 @@ object PowerIterationClusteringExample {
     val expCoeff = -1.0 / 2.0 * math.pow(sigma, 2.0)
     val ssquares = (p1._1 - p2._1) * (p1._1 - p2._1) + (p1._2 - p2._2) * (p1._2 - p2._2)
     coeff * math.exp(expCoeff * ssquares)
-    //    math.exp((p1._1 - p2._1) * (p1._1 - p2._1) + (p1._2 - p2._2) * (p1._2 - p2._2))
   }
-
-
 }