[SPARK-7620] [ML] [MLLIB] Removed calling size, length in while condi…

…tion to avoid extra JVM call

Author: DB Tsai <[email protected]>

Closes apache#6137 from dbtsai/clean and squashes the following commits:

185816d [DB Tsai] fix compilication issue
f418d08 [DB Tsai] first commit

mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala

@@ -258,7 +258,8 @@ class LogisticRegressionModel private[ml] (
     rawPrediction match {
       case dv: DenseVector =>
         var i = 0
-        while (i < dv.size) {
+        val size = dv.size
+        while (i < size) {
           dv.values(i) = 1.0 / (1.0 + math.exp(-dv.values(i)))
           i += 1
         }
@@ -357,7 +358,8 @@ private[classification] class MultiClassSummarizer extends Serializable {
   def histogram: Array[Long] = {
     val result = Array.ofDim[Long](numClasses)
     var i = 0
-    while (i < result.length) {
+    val len = result.length
+    while (i < len) {
       result(i) = distinctMap.getOrElse(i, 0L)
       i += 1
     }
@@ -480,7 +482,8 @@ private class LogisticAggregator(
       var i = 0
       val localThisGradientSumArray = this.gradientSumArray
       val localOtherGradientSumArray = other.gradientSumArray
-      while (i < localThisGradientSumArray.length) {
+      val len = localThisGradientSumArray.length
+      while (i < len) {
         localThisGradientSumArray(i) += localOtherGradientSumArray(i)
         i += 1
       }

mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala

@@ -98,7 +98,8 @@ private[feature] object Bucketizer {
       false
     } else {
       var i = 0
-      while (i < splits.length - 1) {
+      val n = splits.length - 1
+      while (i < n) {
         if (splits(i) >= splits(i + 1)) return false
         i += 1
       }

mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala

@@ -189,7 +189,8 @@ private object VectorIndexer {
 
     private def addDenseVector(dv: DenseVector): Unit = {
       var i = 0
-      while (i < dv.size) {
+      val size = dv.size
+      while (i < size) {
         if (featureValueSets(i).size <= maxCategories) {
           featureValueSets(i).add(dv(i))
         }
@@ -201,7 +202,8 @@ private object VectorIndexer {
       // TODO: This might be able to handle 0's more efficiently.
       var vecIndex = 0 // index into vector
       var k = 0 // index into non-zero elements
-      while (vecIndex < sv.size) {
+      val size = sv.size
+      while (vecIndex < size) {
         val featureValue = if (k < sv.indices.length && vecIndex == sv.indices(k)) {
           k += 1
           sv.values(k - 1)

mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala

@@ -167,7 +167,8 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress
     val weights = {
       val rawWeights = state.x.toArray.clone()
       var i = 0
-      while (i < rawWeights.length) {
+      val len = rawWeights.length
+      while (i < len) {
         rawWeights(i) *= { if (featuresStd(i) != 0.0) yStd / featuresStd(i) else 0.0 }
         i += 1
       }
@@ -307,7 +308,8 @@ private class LeastSquaresAggregator(
     val weightsArray = weights.toArray.clone()
     var sum = 0.0
     var i = 0
-    while (i < weightsArray.length) {
+    val len = weightsArray.length
+    while (i < len) {
       if (featuresStd(i) != 0.0) {
         weightsArray(i) /=  featuresStd(i)
         sum += weightsArray(i) * featuresMean(i)

mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala

@@ -38,7 +38,8 @@ class ChiSqSelectorModel (val selectedFeatures: Array[Int]) extends VectorTransf
 
   protected def isSorted(array: Array[Int]): Boolean = {
     var i = 1
-    while (i < array.length) {
+    val len = array.length
+    while (i < len) {
       if (array(i) < array(i-1)) return false
       i += 1
     }

mllib/src/main/scala/org/apache/spark/mllib/optimization/Updater.scala

@@ -116,7 +116,8 @@ class L1Updater extends Updater {
     // Apply proximal operator (soft thresholding)
     val shrinkageVal = regParam * thisIterStepSize
     var i = 0
-    while (i < brzWeights.length) {
+    val len = brzWeights.length
+    while (i < len) {
       val wi = brzWeights(i)
       brzWeights(i) = signum(wi) * max(0.0, abs(wi) - shrinkageVal)
       i += 1

mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala

@@ -69,7 +69,8 @@ class IsotonicRegressionModel (
   /** Asserts the input array is monotone with the given ordering. */
   private def assertOrdered(xs: Array[Double])(implicit ord: Ordering[Double]): Unit = {
     var i = 1
-    while (i < xs.length) {
+    val len = xs.length
+    while (i < len) {
       require(ord.compare(xs(i - 1), xs(i)) <= 0,
         s"Elements (${xs(i - 1)}, ${xs(i)}) are not ordered.")
       i += 1
@@ -329,11 +330,12 @@ class IsotonicRegression private (private var isotonic: Boolean) extends Seriali
     }
 
     var i = 0
-    while (i < input.length) {
+    val len = input.length
+    while (i < len) {
       var j = i
 
       // Find monotonicity violating sequence, if any.
-      while (j < input.length - 1 && input(j)._1 > input(j + 1)._1) {
+      while (j < len - 1 && input(j)._1 > input(j + 1)._1) {
         j = j + 1
       }
 

mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala

@@ -70,23 +70,30 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
     require(n == sample.size, s"Dimensions mismatch when adding new sample." +
       s" Expecting $n but got ${sample.size}.")
 
+    val localCurrMean= currMean
+    val localCurrM2n = currM2n
+    val localCurrM2 = currM2
+    val localCurrL1 = currL1
+    val localNnz = nnz
+    val localCurrMax = currMax
+    val localCurrMin = currMin
     sample.foreachActive { (index, value) =>
       if (value != 0.0) {
-        if (currMax(index) < value) {
-          currMax(index) = value
+        if (localCurrMax(index) < value) {
+          localCurrMax(index) = value
         }
-        if (currMin(index) > value) {
-          currMin(index) = value
+        if (localCurrMin(index) > value) {
+          localCurrMin(index) = value
         }
 
-        val prevMean = currMean(index)
+        val prevMean = localCurrMean(index)
         val diff = value - prevMean
-        currMean(index) = prevMean + diff / (nnz(index) + 1.0)
-        currM2n(index) += (value - currMean(index)) * diff
-        currM2(index) += value * value
-        currL1(index) += math.abs(value)
+        localCurrMean(index) = prevMean + diff / (localNnz(index) + 1.0)
+        localCurrM2n(index) += (value - localCurrMean(index)) * diff
+        localCurrM2(index) += value * value
+        localCurrL1(index) += math.abs(value)
 
-        nnz(index) += 1.0
+        localNnz(index) += 1.0
       }
     }
 
@@ -130,14 +137,14 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
       }
     } else if (totalCnt == 0 && other.totalCnt != 0) {
       this.n = other.n
-      this.currMean = other.currMean.clone
-      this.currM2n = other.currM2n.clone
-      this.currM2 = other.currM2.clone
-      this.currL1 = other.currL1.clone
+      this.currMean = other.currMean.clone()
+      this.currM2n = other.currM2n.clone()
+      this.currM2 = other.currM2.clone()
+      this.currL1 = other.currL1.clone()
       this.totalCnt = other.totalCnt
-      this.nnz = other.nnz.clone
-      this.currMax = other.currMax.clone
-      this.currMin = other.currMin.clone
+      this.nnz = other.nnz.clone()
+      this.currMax = other.currMax.clone()
+      this.currMin = other.currMin.clone()
     }
     this
   }
@@ -165,7 +172,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
     if (denominator > 0.0) {
       val deltaMean = currMean
       var i = 0
-      while (i < currM2n.size) {
+      val len = currM2n.length
+      while (i < len) {
         realVariance(i) =
           currM2n(i) + deltaMean(i) * deltaMean(i) * nnz(i) * (totalCnt - nnz(i)) / totalCnt
         realVariance(i) /= denominator
@@ -211,7 +219,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
     val realMagnitude = Array.ofDim[Double](n)
 
     var i = 0
-    while (i < currM2.size) {
+    val len = currM2.length
+    while (i < len) {
       realMagnitude(i) = math.sqrt(currM2(i))
       i += 1
     }

mllib/src/main/scala/org/apache/spark/mllib/stat/test/ChiSqTest.scala

@@ -205,8 +205,10 @@ private[stat] object ChiSqTest extends Logging {
     val colSums = new Array[Double](numCols)
     val rowSums = new Array[Double](numRows)
     val colMajorArr = counts.toArray
+    val colMajorArrLen = colMajorArr.length
+
     var i = 0
-    while (i < colMajorArr.size) {
+    while (i < colMajorArrLen) {
       val elem = colMajorArr(i)
       if (elem < 0.0) {
         throw new IllegalArgumentException("Contingency table cannot contain negative entries.")
@@ -220,7 +222,7 @@ private[stat] object ChiSqTest extends Logging {
     // second pass to collect statistic
     var statistic = 0.0
     var j = 0
-    while (j < colMajorArr.size) {
+    while (j < colMajorArrLen) {
       val col = j / numRows
       val colSum = colSums(col)
       if (colSum == 0.0) {

mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala

@@ -111,11 +111,12 @@ private[tree] abstract class ImpurityCalculator(val stats: Array[Double]) {
    * Add the stats from another calculator into this one, modifying and returning this calculator.
    */
   def add(other: ImpurityCalculator): ImpurityCalculator = {
-    require(stats.size == other.stats.size,
+    require(stats.length == other.stats.length,
       s"Two ImpurityCalculator instances cannot be added with different counts sizes." +
-        s"  Sizes are ${stats.size} and ${other.stats.size}.")
+        s"  Sizes are ${stats.length} and ${other.stats.length}.")
     var i = 0
-    while (i < other.stats.size) {
+    val len = other.stats.length
+    while (i < len) {
       stats(i) += other.stats(i)
       i += 1
     }
@@ -127,11 +128,12 @@ private[tree] abstract class ImpurityCalculator(val stats: Array[Double]) {
    * calculator.
    */
   def subtract(other: ImpurityCalculator): ImpurityCalculator = {
-    require(stats.size == other.stats.size,
+    require(stats.length == other.stats.length,
       s"Two ImpurityCalculator instances cannot be subtracted with different counts sizes." +
-      s"  Sizes are ${stats.size} and ${other.stats.size}.")
+      s"  Sizes are ${stats.length} and ${other.stats.length}.")
     var i = 0
-    while (i < other.stats.size) {
+    val len = other.stats.length
+    while (i < len) {
       stats(i) -= other.stats(i)
       i += 1
     }

mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala

@@ -107,7 +107,8 @@ object LinearDataGenerator {
 
     x.foreach { v =>
       var i = 0
-      while (i < v.length) {
+      val len = v.length
+      while (i < len) {
         v(i) = (v(i) - 0.5) * math.sqrt(12.0 * xVariance(i)) + xMean(i)
         i += 1
       }

mllib/src/test/scala/org/apache/spark/ml/feature/BucketizerSuite.scala

@@ -122,7 +122,8 @@ private object BucketizerSuite extends FunSuite {
   def linearSearchForBuckets(splits: Array[Double], feature: Double): Double = {
     require(feature >= splits.head)
     var i = 0
-    while (i < splits.length - 1) {
+    val n = splits.length - 1
+    while (i < n) {
       if (feature < splits(i + 1)) return i
       i += 1
     }
@@ -138,7 +139,8 @@ private object BucketizerSuite extends FunSuite {
           s" ${splits.mkString(", ")}")
     }
     var i = 0
-    while (i < splits.length - 1) {
+    val n = splits.length - 1
+    while (i < n) {
       // Split i should fall in bucket i.
       testFeature(splits(i), i)
       // Value between splits i,i+1 should be in i, which is also true if the (i+1)-th split is inf.

mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala

@@ -101,7 +101,8 @@ object LogisticRegressionSuite {
       // This doesn't work if `vector` is a sparse vector.
       val vectorArray = vector.toArray
       var i = 0
-      while (i < vectorArray.length) {
+      val len = vectorArray.length
+      while (i < len) {
         vectorArray(i) = vectorArray(i) * math.sqrt(xVariance(i)) + xMean(i)
         i += 1
       }