address comments

Author: yanboliang

mllib/src/main/scala/org/apache/spark/mllib/api/python/PowerIterationClusteringModelWrapper.scala

@@ -26,8 +26,7 @@ import org.apache.spark.mllib.clustering.PowerIterationClusteringModel
 private[python] class PowerIterationClusteringModelWrapper(model: PowerIterationClusteringModel)
   extends PowerIterationClusteringModel(model.k, model.assignments) {
 
-  def getAssignments: RDD[(Long, Int)] = {
-    SerDe.fromTuple2RDD(model.assignments.map(x => (x.id, x.cluster)))
-      .asInstanceOf[RDD[(Long, Int)]]
+  def getAssignments: RDD[Array[Any]] = {
+    model.assignments.map(x => Array(x.id, x.cluster))
   }
 }

mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala

@@ -411,6 +411,12 @@ private[python] class PythonMLLibAPI extends Serializable {
    * handle to the Java object instead of the content of the Java object.  Extra care
    * needs to be taken in the Python code to ensure it gets freed on exit; see the
    * Py4J documentation.
+   * @param data an RDD of (i, j, s,,ij,,) tuples representing the affinity matrix.
+   * @param k number of clusters.
+   * @param maxIterations maximum number of iterations of the power iteration loop.
+   * @param initMode the initialization mode. This can be either "random" to use
+   *                 a random vector as vertex properties, or "degree" to use
+   *                 normalized sum similarities. Default: random.
    */
   def trainPowerIterationClusteringModel(
       data: JavaRDD[Vector],

python/pyspark/mllib/clustering.py

@@ -317,7 +317,7 @@ def assignments(self):
         Returns the cluster assignments of this model.
         """
         return self.call("getAssignments").map(
-            lambda x: (PowerIterationClustering.Assignment(x[0], x[1])))
+            lambda x: (PowerIterationClustering.Assignment(*x)))
 
     @classmethod
     def load(cls, sc, path):