minor revision

mllib/src/main/scala/org/apache/spark/mllib/rdd/VectorRDDFunctions.scala

@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.rdd
 
-import breeze.linalg.{axpy, Vector => BV}
+import breeze.linalg.{Vector => BV, DenseVector => BDV}
 
 import org.apache.spark.mllib.linalg.{Vectors, Vector}
 import org.apache.spark.rdd.RDD
@@ -29,60 +29,59 @@ import org.apache.spark.rdd.RDD
 trait VectorRDDStatisticalSummary {
   def mean: Vector
   def variance: Vector
-  def totalCount: Long
+  def count: Long
   def numNonZeros: Vector
   def max: Vector
   def min: Vector
 }
 
 /**
  * Aggregates [[org.apache.spark.mllib.rdd.VectorRDDStatisticalSummary VectorRDDStatisticalSummary]]
- * together with add() and merge() function.
+ * together with add() and merge() function. Online variance solution used in add() function, while
+ * parallel variance solution used in merge() function. Reference here:
+ * [[http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance variance-wiki]]. Solution here
+ * ignoring the zero elements when calling add() and merge(), for decreasing the O(n) algorithm to
+ * O(nnz). Real variance is computed here after we get other statistics, simply by another parallel
+ * combination process.
  */
-private class Aggregator(
-    val currMean: BV[Double],
-    val currM2n: BV[Double],
+private class VectorRDDStatisticsAggregator(
+    val currMean: BDV[Double],
+    val currM2n: BDV[Double],
     var totalCnt: Double,
-    val nnz: BV[Double],
-    val currMax: BV[Double],
-    val currMin: BV[Double]) extends VectorRDDStatisticalSummary with Serializable {
+    val nnz: BDV[Double],
+    val currMax: BDV[Double],
+    val currMin: BDV[Double]) extends VectorRDDStatisticalSummary with Serializable {
 
   // lazy val is used for computing only once time. Same below.
   override lazy val mean = Vectors.fromBreeze(currMean :* nnz :/ totalCnt)
 
-  // Online variance solution used in add() function, while parallel variance solution used in
-  // merge() function. Reference here:
-  // http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
-  // Solution here ignoring the zero elements when calling add() and merge(), for decreasing the
-  // O(n) algorithm to O(nnz). Real variance is computed here after we get other statistics, simply
-  // by another parallel combination process.
   override lazy val variance = {
     val deltaMean = currMean
     var i = 0
-    while(i < currM2n.size) {
-      currM2n(i) += deltaMean(i) * deltaMean(i) * nnz(i) * (totalCnt-nnz(i)) / totalCnt
+    while (i < currM2n.size) {
+      currM2n(i) += deltaMean(i) * deltaMean(i) * nnz(i) * (totalCnt - nnz(i)) / totalCnt
       currM2n(i) /= totalCnt
       i += 1
     }
     Vectors.fromBreeze(currM2n)
   }
 
-  override lazy val totalCount: Long = totalCnt.toLong
+  override lazy val count: Long = totalCnt.toLong
 
   override lazy val numNonZeros: Vector = Vectors.fromBreeze(nnz)
 
   override lazy val max: Vector = {
     nnz.iterator.foreach {
       case (id, count) =>
-        if ((count == 0.0) || ((count < totalCnt) && (currMax(id) < 0.0)))  currMax(id) = 0.0
+        if ((count < totalCnt) && (currMax(id) < 0.0))  currMax(id) = 0.0
     }
     Vectors.fromBreeze(currMax)
   }
 
   override lazy val min: Vector = {
     nnz.iterator.foreach {
       case (id, count) =>
-        if ((count == 0.0) || ((count < totalCnt) && (currMin(id) > 0.0))) currMin(id) = 0.0
+        if ((count < totalCnt) && (currMin(id) > 0.0)) currMin(id) = 0.0
     }
     Vectors.fromBreeze(currMin)
   }
@@ -92,7 +91,7 @@ private class Aggregator(
    */
   def add(currData: BV[Double]): this.type = {
     currData.activeIterator.foreach {
-      // this case is used for filtering the zero elements if the vector is a dense one.
+      // this case is used for filtering the zero elements if the vector.
       case (id, 0.0) =>
       case (id, value) =>
         if (currMax(id) < value) currMax(id) = value
@@ -112,7 +111,7 @@ private class Aggregator(
   /**
    * Combine function used for combining intermediate results together from every worker.
    */
-  def merge(other: Aggregator): this.type = {
+  def merge(other: VectorRDDStatisticsAggregator): this.type = {
 
     totalCnt += other.totalCnt
 
@@ -145,7 +144,7 @@ private class Aggregator(
         if (currMin(id) > value) currMin(id) = value
     }
 
-    axpy(1.0, other.nnz, nnz)
+    nnz += other.nnz
     this
   }
 }
@@ -160,18 +159,18 @@ class VectorRDDFunctions(self: RDD[Vector]) extends Serializable {
   /**
    * Compute full column-wise statistics for the RDD with the size of Vector as input parameter.
    */
-  def summarizeStatistics(): VectorRDDStatisticalSummary = {
-    val size = self.take(1).head.size
+  def computeSummaryStatistics(): VectorRDDStatisticalSummary = {
+    val size = self.first().size
 
-    val zeroValue = new Aggregator(
-      BV.zeros[Double](size),
-      BV.zeros[Double](size),
+    val zeroValue = new VectorRDDStatisticsAggregator(
+      BDV.zeros[Double](size),
+      BDV.zeros[Double](size),
       0.0,
-      BV.zeros[Double](size),
-      BV.fill(size)(Double.MinValue),
-      BV.fill(size)(Double.MaxValue))
+      BDV.zeros[Double](size),
+      BDV.fill(size)(Double.MinValue),
+      BDV.fill(size)(Double.MaxValue))
 
-    self.map(_.toBreeze).aggregate[Aggregator](zeroValue)(
+    self.map(_.toBreeze).aggregate[VectorRDDStatisticsAggregator](zeroValue)(
       (aggregator, data) => aggregator.add(data),
       (aggregator1, aggregator2) => aggregator1.merge(aggregator2)
     )

mllib/src/test/scala/org/apache/spark/mllib/rdd/VectorRDDFunctionsSuite.scala

@@ -45,15 +45,15 @@ class VectorRDDFunctionsSuite extends FunSuite with LocalSparkContext {
 
   test("dense statistical summary") {
     val data = sc.parallelize(localData, 2)
-    val summary = data.summarizeStatistics()
+    val summary = data.computeSummaryStatistics()
 
     assert(equivVector(summary.mean, Vectors.dense(4.0, 5.0, 6.0)),
       "Dense column mean do not match.")
 
     assert(equivVector(summary.variance, Vectors.dense(6.0, 6.0, 6.0)),
       "Dense column variance do not match.")
 
-    assert(summary.totalCount === 3, "Dense column cnt do not match.")
+    assert(summary.count === 3, "Dense column cnt do not match.")
 
     assert(equivVector(summary.numNonZeros, Vectors.dense(3.0, 3.0, 3.0)),
       "Dense column nnz do not match.")
@@ -67,15 +67,15 @@ class VectorRDDFunctionsSuite extends FunSuite with LocalSparkContext {
 
   test("sparse statistical summary") {
     val dataForSparse = sc.parallelize(sparseData.toSeq, 2)
-    val summary = dataForSparse.summarizeStatistics()
+    val summary = dataForSparse.computeSummaryStatistics()
 
     assert(equivVector(summary.mean, Vectors.dense(0.06, 0.05, 0.0)),
       "Sparse column mean do not match.")
 
     assert(equivVector(summary.variance, Vectors.dense(0.2564, 0.2475, 0.0)),
       "Sparse column variance do not match.")
 
-    assert(summary.totalCount === 100, "Sparse column cnt do not match.")
+    assert(summary.count === 100, "Sparse column cnt do not match.")
 
     assert(equivVector(summary.numNonZeros, Vectors.dense(2.0, 1.0, 0.0)),
       "Sparse column nnz do not match.")