[SPARK-5430] move treeReduce and treeAggregate from mllib to core

We have seen many use cases of `treeAggregate`/`treeReduce` outside the ML domain. Maybe it is time to move them to Core. pwendell

Author: Xiangrui Meng <meng@databricks.com>

Closes #4228 from mengxr/SPARK-5430 and squashes the following commits:

20ad40d [Xiangrui Meng] exclude tree* from mima
e89a43e [Xiangrui Meng] fix compile and update java doc
3ae1a4b [Xiangrui Meng] add treeReduce/treeAggregate to Python
6f948c5 [Xiangrui Meng] add treeReduce/treeAggregate to JavaRDDLike
d600b6c [Xiangrui Meng] move treeReduce and treeAggregate to core

8 files changed, 9 insertions, 74 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala
index 3260f27513..a89eea0e21 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala
@@ -22,7 +22,6 @@ import breeze.linalg.{DenseVector => BDV}
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
-import org.apache.spark.mllib.rdd.RDDFunctions._
 import org.apache.spark.rdd.RDD
 
 /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala
index 3c2091732f..2f2c6f94e9 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala
@@ -20,7 +20,6 @@ package org.apache.spark.mllib.feature
 import org.apache.spark.Logging
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
-import org.apache.spark.mllib.rdd.RDDFunctions._
 import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
 import org.apache.spark.rdd.RDD
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index 02075edbab..ddca30c3c0 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -30,7 +30,6 @@ import org.apache.spark.Logging
 import org.apache.spark.SparkContext._
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.mllib.linalg._
-import org.apache.spark.mllib.rdd.RDDFunctions._
 import org.apache.spark.mllib.stat.{MultivariateOnlineSummarizer, MultivariateStatisticalSummary}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.util.random.XORShiftRandom
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
index 0857877951..4b7d0589c9 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
@@ -25,7 +25,6 @@ import org.apache.spark.annotation.{Experimental, DeveloperApi}
 import org.apache.spark.Logging
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.linalg.{Vectors, Vector}
-import org.apache.spark.mllib.rdd.RDDFunctions._
 
 /**
  * Class used to solve an optimization problem using Gradient Descent.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala
index d16d0daf08..d5e4f4ccbf 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala
@@ -26,7 +26,6 @@ import org.apache.spark.Logging
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.linalg.BLAS.axpy
-import org.apache.spark.mllib.rdd.RDDFunctions._
 import org.apache.spark.rdd.RDD
 
 /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/rdd/RDDFunctions.scala b/mllib/src/main/scala/org/apache/spark/mllib/rdd/RDDFunctions.scala
index 57c0768084..78172843be 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/rdd/RDDFunctions.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/rdd/RDDFunctions.scala
@@ -21,10 +21,7 @@ import scala.language.implicitConversions
 import scala.reflect.ClassTag
 
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.HashPartitioner
-import org.apache.spark.SparkContext._
 import org.apache.spark.rdd.RDD
-import org.apache.spark.util.Utils
 
 /**
  * Machine learning specific RDD functions.
@@ -53,63 +50,25 @@ class RDDFunctions[T: ClassTag](self: RDD[T]) extends Serializable {
    * Reduces the elements of this RDD in a multi-level tree pattern.
    *
    * @param depth suggested depth of the tree (default: 2)
-   * @see [[org.apache.spark.rdd.RDD#reduce]]
+   * @see [[org.apache.spark.rdd.RDD#treeReduce]]
+   * @deprecated Use [[org.apache.spark.rdd.RDD#treeReduce]] instead.
    */
-  def treeReduce(f: (T, T) => T, depth: Int = 2): T = {
-    require(depth >= 1, s"Depth must be greater than or equal to 1 but got $depth.")
-    val cleanF = self.context.clean(f)
-    val reducePartition: Iterator[T] => Option[T] = iter => {
-      if (iter.hasNext) {
-        Some(iter.reduceLeft(cleanF))
-      } else {
-        None
-      }
-    }
-    val partiallyReduced = self.mapPartitions(it => Iterator(reducePartition(it)))
-    val op: (Option[T], Option[T]) => Option[T] = (c, x) => {
-      if (c.isDefined && x.isDefined) {
-        Some(cleanF(c.get, x.get))
-      } else if (c.isDefined) {
-        c
-      } else if (x.isDefined) {
-        x
-      } else {
-        None
-      }
-    }
-    RDDFunctions.fromRDD(partiallyReduced).treeAggregate(Option.empty[T])(op, op, depth)
-      .getOrElse(throw new UnsupportedOperationException("empty collection"))
-  }
+  @deprecated("Use RDD.treeReduce instead.", "1.3.0")
+  def treeReduce(f: (T, T) => T, depth: Int = 2): T = self.treeReduce(f, depth)
 
   /**
    * Aggregates the elements of this RDD in a multi-level tree pattern.
    *
    * @param depth suggested depth of the tree (default: 2)
-   * @see [[org.apache.spark.rdd.RDD#aggregate]]
+   * @see [[org.apache.spark.rdd.RDD#treeAggregate]]
+   * @deprecated Use [[org.apache.spark.rdd.RDD#treeAggregate]] instead.
    */
+  @deprecated("Use RDD.treeAggregate instead.", "1.3.0")
   def treeAggregate[U: ClassTag](zeroValue: U)(
       seqOp: (U, T) => U,
       combOp: (U, U) => U,
       depth: Int = 2): U = {
-    require(depth >= 1, s"Depth must be greater than or equal to 1 but got $depth.")
-    if (self.partitions.size == 0) {
-      return Utils.clone(zeroValue, self.context.env.closureSerializer.newInstance())
-    }
-    val cleanSeqOp = self.context.clean(seqOp)
-    val cleanCombOp = self.context.clean(combOp)
-    val aggregatePartition = (it: Iterator[T]) => it.aggregate(zeroValue)(cleanSeqOp, cleanCombOp)
-    var partiallyAggregated = self.mapPartitions(it => Iterator(aggregatePartition(it)))
-    var numPartitions = partiallyAggregated.partitions.size
-    val scale = math.max(math.ceil(math.pow(numPartitions, 1.0 / depth)).toInt, 2)
-    // If creating an extra level doesn't help reduce the wall-clock time, we stop tree aggregation.
-    while (numPartitions > scale + numPartitions / scale) {
-      numPartitions /= scale
-      val curNumPartitions = numPartitions
-      partiallyAggregated = partiallyAggregated.mapPartitionsWithIndex { (i, iter) =>
-        iter.map((i % curNumPartitions, _))
-      }.reduceByKey(new HashPartitioner(curNumPartitions), cleanCombOp).values
-    }
-    partiallyAggregated.reduce(cleanCombOp)
+    self.treeAggregate(zeroValue)(seqOp, combOp, depth)
   }
 }
 
@@ -117,5 +76,5 @@ class RDDFunctions[T: ClassTag](self: RDD[T]) extends Serializable {
 object RDDFunctions {
 
   /** Implicit conversion from an RDD to RDDFunctions. */
-  implicit def fromRDD[T: ClassTag](rdd: RDD[T]) = new RDDFunctions[T](rdd)
+  implicit def fromRDD[T: ClassTag](rdd: RDD[T]): RDDFunctions[T] = new RDDFunctions[T](rdd)
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/feature/StandardScalerSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/feature/StandardScalerSuite.scala
index 4c93c0ca4f..e9e510b6f5 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/feature/StandardScalerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/feature/StandardScalerSuite.scala
@@ -22,7 +22,6 @@ import org.scalatest.FunSuite
 import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.mllib.util.TestingUtils._
-import org.apache.spark.mllib.rdd.RDDFunctions._
 import org.apache.spark.mllib.stat.{MultivariateStatisticalSummary, MultivariateOnlineSummarizer}
 import org.apache.spark.rdd.RDD
 
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/rdd/RDDFunctionsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/rdd/RDDFunctionsSuite.scala
index 681ce92639..6d6c0aa5be 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/rdd/RDDFunctionsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/rdd/RDDFunctionsSuite.scala
@@ -46,22 +46,4 @@ class RDDFunctionsSuite extends FunSuite with MLlibTestSparkContext {
     val expected = data.flatMap(x => x).sliding(3).toSeq.map(_.toSeq)
     assert(sliding === expected)
   }
-
-  test("treeAggregate") {
-    val rdd = sc.makeRDD(-1000 until 1000, 10)
-    def seqOp = (c: Long, x: Int) => c + x
-    def combOp = (c1: Long, c2: Long) => c1 + c2
-    for (depth <- 1 until 10) {
-      val sum = rdd.treeAggregate(0L)(seqOp, combOp, depth)
-      assert(sum === -1000L)
-    }
-  }
-
-  test("treeReduce") {
-    val rdd = sc.makeRDD(-1000 until 1000, 10)
-    for (depth <- 1 until 10) {
-      val sum = rdd.treeReduce(_ + _, depth)
-      assert(sum === -1000)
-    }
-  }
 }