optimized MLUtils.computeStats

update some ml algorithms to use Vector (cont.)

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

@@ -22,7 +22,7 @@ import org.apache.spark.rdd.RDD
 
 import scala.collection.mutable.ArrayBuffer
 
-import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
 
 /**
  * Class used to solve an optimization problem using Gradient Descent.
@@ -139,47 +139,46 @@ object GradientDescent extends Logging {
     numIterations: Int,
     regParam: Double,
     miniBatchFraction: Double,
-    initialWeights: Vector): (Vector, Vector) = {
+    initialWeights: Vector): (Vector, Array[Double]) = {
 
     val stochasticLossHistory = new ArrayBuffer[Double](numIterations)
 
     val nexamples: Long = data.count()
     val miniBatchSize = nexamples * miniBatchFraction
 
     // Initialize weights as a column vector
-    var weights = initialWeights.toBreeze.toDenseVector
+    var weights = Vectors.dense(initialWeights.toArray)
 
     /**
      * For the first iteration, the regVal will be initialized as sum of sqrt of
      * weights if it's L2 update; for L1 update; the same logic is followed.
      */
     var regVal = updater.compute(
-      weights, new DoubleMatrix(initialWeights.length, 1), 0, 1, regParam)._2
+      weights, Vectors.dense(new Array[Double](weights.size)), 0, 1, regParam)._2
 
     for (i <- 1 to numIterations) {
       // Sample a subset (fraction miniBatchFraction) of the total data
       // compute and sum up the subgradients on this subset (this is one map-reduce)
       val (gradientSum, lossSum) = data.sample(false, miniBatchFraction, 42 + i).map {
         case (y, features) =>
-          val featuresCol = new DoubleMatrix(features.length, 1, features:_*)
-          val (grad, loss) = gradient.compute(featuresCol, y, weights)
-          (grad, loss)
-      }.reduce((a, b) => (a._1.addi(b._1), a._2 + b._2))
+          val (grad, loss) = gradient.compute(features, y, weights)
+          (grad.toBreeze, loss)
+      }.reduce((a, b) => (a._1 += b._1, a._2 + b._2))
 
       /**
        * NOTE(Xinghao): lossSum is computed using the weights from the previous iteration
        * and regVal is the regularization value computed in the previous iteration as well.
        */
       stochasticLossHistory.append(lossSum / miniBatchSize + regVal)
       val update = updater.compute(
-        weights, gradientSum.div(miniBatchSize), stepSize, i, regParam)
+        weights, Vectors.fromBreeze(gradientSum / miniBatchSize), stepSize, i, regParam)
       weights = update._1
       regVal = update._2
     }
 
     logInfo("GradientDescent.runMiniBatchSGD finished. Last 10 stochastic losses %s".format(
       stochasticLossHistory.takeRight(10).mkString(", ")))
 
-    (weights.toArray, stochasticLossHistory.toArray)
+    (weights, stochasticLossHistory.toArray)
   }
 }

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

@@ -18,7 +18,6 @@
 package org.apache.spark.mllib.optimization
 
 import scala.math._
-import org.jblas.DoubleMatrix
 
 import breeze.linalg.{norm => brzNorm}
 
@@ -122,7 +121,7 @@ class SquaredL2Updater extends Updater {
       gradient: Vector,
       stepSize: Double,
       iter: Int,
-      regParam: Double): (DoubleMatrix, Double) = {
+      regParam: Double): (Vector, Double) = {
     // add up both updates from the gradient of the loss (= step) as well as
     // the gradient of the regularizer (= regParam * weightsOld)
     // w' = w - thisIterStepSize * (gradient + regParam * w)
@@ -132,7 +131,7 @@ class SquaredL2Updater extends Updater {
       (gradient.toBreeze * thisIterStepSize)
     val norm = brzNorm(brzWeights, 2.0)
 
-    (Vectors.fromBreeze(newWeights), 0.5 * regParam * norm * norm)
+    (Vectors.fromBreeze(brzWeights), 0.5 * regParam * norm * norm)
   }
 }
 

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

@@ -118,8 +118,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
 
   /** Prepends one to the input vector. */
   private def prependOne(vector: Vector): Vector = {
-    val vectorWithIntercept = vector match {
-      case dv: BDV[Double] => BDV.vertcat(BDV.ones(1), dv)
+    val vectorWithIntercept = vector.toBreeze match {
+      case dv: BDV[Double] => BDV.vertcat(BDV.ones[Double](1), dv)
       case sv: BSV[Double] => BSV.vertcat(new BSV[Double](Array(0), Array(1.0), 1), sv)
       case v: Any => throw new IllegalArgumentException("Do not support vector type " + v.getClass)
     }

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

@@ -21,8 +21,7 @@ import org.apache.spark.SparkContext
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.optimization._
 import org.apache.spark.mllib.util.MLUtils
-
-import org.jblas.DoubleMatrix
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
 
 /**
  * Regression model trained using LinearRegression.
@@ -31,15 +30,15 @@ import org.jblas.DoubleMatrix
  * @param intercept Intercept computed for this model.
  */
 class LinearRegressionModel(
-    override val weights: Array[Double],
+    override val weights: Vector,
     override val intercept: Double)
   extends GeneralizedLinearModel(weights, intercept) with RegressionModel with Serializable {
 
   override protected def predictPoint(
-      dataMatrix: DoubleMatrix,
-      weightMatrix: DoubleMatrix,
+      dataMatrix: Vector,
+      weightMatrix: Vector,
       intercept: Double): Double = {
-    dataMatrix.dot(weightMatrix) + intercept
+    weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept
   }
 }
 
@@ -69,7 +68,7 @@ class LinearRegressionWithSGD private (
    */
   def this() = this(1.0, 100, 1.0)
 
-  override protected def createModel(weights: Array[Double], intercept: Double) = {
+  override protected def createModel(weights: Vector, intercept: Double) = {
     new LinearRegressionModel(weights, intercept)
   }
 }
@@ -98,7 +97,7 @@ object LinearRegressionWithSGD {
       numIterations: Int,
       stepSize: Double,
       miniBatchFraction: Double,
-      initialWeights: Array[Double])
+      initialWeights: Vector)
     : LinearRegressionModel =
   {
     new LinearRegressionWithSGD(stepSize, numIterations, miniBatchFraction).run(input,
@@ -172,7 +171,7 @@ object LinearRegressionWithSGD {
     val sc = new SparkContext(args(0), "LinearRegression")
     val data = MLUtils.loadLabeledData(sc, args(1))
     val model = LinearRegressionWithSGD.train(data, args(3).toInt, args(2).toDouble)
-    println("Weights: " + model.weights.mkString("[", ", ", "]"))
+    println("Weights: " + model.weights)
     println("Intercept: " + model.intercept)
 
     sc.stop()

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

@@ -21,7 +21,9 @@ import org.apache.spark.SparkContext
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.optimization._
 import org.apache.spark.mllib.util.MLUtils
-import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+import breeze.linalg.{Vector => BV, DenseVector => BDV}
 
 /**
  * Regression model trained using RidgeRegression.
@@ -36,10 +38,10 @@ class RidgeRegressionModel(
   with RegressionModel with Serializable {
 
   override protected def predictPoint(
-      dataMatrix: DoubleMatrix,
-      weightMatrix: DoubleMatrix,
+      dataMatrix: Vector,
+      weightMatrix: Vector,
       intercept: Double): Double = {
-    dataMatrix.dot(weightMatrix) + intercept
+    weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept
   }
 }
 
@@ -71,8 +73,8 @@ class RidgeRegressionWithSGD private (
   super.setIntercept(false)
 
   var yMean = 0.0
-  var xColMean: DoubleMatrix = _
-  var xColSd: DoubleMatrix = _
+  var xColMean: BV[Double] = _
+  var xColSd: BV[Double] = _
 
   /**
    * Construct a RidgeRegression object with default parameters
@@ -85,33 +87,33 @@ class RidgeRegressionWithSGD private (
     this
   }
 
-  override protected def createModel(weights: Array[Double], intercept: Double) = {
-    val weightsMat = new DoubleMatrix(weights.length, 1, weights: _*)
-    val weightsScaled = weightsMat.div(xColSd)
-    val interceptScaled = yMean - weightsMat.transpose().mmul(xColMean.div(xColSd)).get(0)
+  override protected def createModel(weights: Vector, intercept: Double) = {
+    val weightsMat = weights.toBreeze
+    val weightsScaled = weightsMat :/ xColSd
+    val interceptScaled = yMean - weightsMat.dot(xColMean :/ xColSd)
 
-    new RidgeRegressionModel(weightsScaled.data, interceptScaled)
+    new RidgeRegressionModel(Vectors.fromBreeze(weightsScaled), interceptScaled)
   }
 
   override def run(
       input: RDD[LabeledPoint],
-      initialWeights: Array[Double])
+      initialWeights: Vector)
     : RidgeRegressionModel =
   {
-    val nfeatures: Int = input.first().features.length
+    val nfeatures: Int = input.first().features.size
     val nexamples: Long = input.count()
 
     // To avoid penalizing the intercept, we center and scale the data.
     val stats = MLUtils.computeStats(input, nfeatures, nexamples)
     yMean = stats._1
-    xColMean = stats._2
-    xColSd = stats._3
+    xColMean = stats._2.toBreeze
+    xColSd = stats._3.toBreeze
 
     val normalizedData = input.map { point =>
       val yNormalized = point.label - yMean
-      val featuresMat = new DoubleMatrix(nfeatures, 1, point.features:_*)
-      val featuresNormalized = featuresMat.sub(xColMean).divi(xColSd)
-      LabeledPoint(yNormalized, featuresNormalized.toArray)
+      val featuresMat = point.features.toBreeze
+      val featuresNormalized = (featuresMat - xColMean) :/ xColSd
+      LabeledPoint(yNormalized, Vectors.fromBreeze(featuresNormalized))
     }
 
     super.run(normalizedData, initialWeights)
@@ -143,7 +145,7 @@ object RidgeRegressionWithSGD {
       stepSize: Double,
       regParam: Double,
       miniBatchFraction: Double,
-      initialWeights: Array[Double])
+      initialWeights: Vector)
     : RidgeRegressionModel =
   {
     new RidgeRegressionWithSGD(stepSize, numIterations, regParam, miniBatchFraction).run(
@@ -220,7 +222,8 @@ object RidgeRegressionWithSGD {
     val data = MLUtils.loadLabeledData(sc, args(1))
     val model = RidgeRegressionWithSGD.train(data, args(4).toInt, args(2).toDouble,
         args(3).toDouble)
-    println("Weights: " + model.weights.mkString("[", ", ", "]"))
+
+    println("Weights: " + model.weights)
     println("Intercept: " + model.intercept)
 
     sc.stop()

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

@@ -17,15 +17,13 @@
 
 package org.apache.spark.mllib.util
 
+import breeze.linalg.{Vector => BV, DenseVector => BDV, SparseVector => BSV,
+  squaredDistance => breezeSquaredDistance}
+
 import org.apache.spark.SparkContext
 import org.apache.spark.rdd.RDD
-import org.apache.spark.SparkContext._
-
-import org.jblas.DoubleMatrix
-
 import org.apache.spark.mllib.regression.LabeledPoint
-
-import breeze.linalg.{Vector => BV, SparseVector => BSV, squaredDistance => breezeSquaredDistance}
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
 
 /**
  * Helper methods to load, save and pre-process data used in ML Lib.
@@ -54,7 +52,7 @@ object MLUtils {
     sc.textFile(dir).map { line =>
       val parts = line.split(',')
       val label = parts(0).toDouble
-      val features = parts(1).trim().split(' ').map(_.toDouble)
+      val features = Vectors.dense(parts(1).trim().split(' ').map(_.toDouble))
       LabeledPoint(label, features)
     }
   }
@@ -68,52 +66,59 @@ object MLUtils {
    * @param dir Directory to save the data.
    */
   def saveLabeledData(data: RDD[LabeledPoint], dir: String) {
-    val dataStr = data.map(x => x.label + "," + x.features.mkString(" "))
+    val dataStr = data.map(x => x.label + "," + x.features.toArray.mkString(" "))
     dataStr.saveAsTextFile(dir)
   }
 
   /**
    * Utility function to compute mean and standard deviation on a given dataset.
    *
    * @param data - input data set whose statistics are computed
-   * @param nfeatures - number of features
-   * @param nexamples - number of examples in input dataset
+   * @param numFeatures - number of features
+   * @param numExamples - number of examples in input dataset
    *
    * @return (yMean, xColMean, xColSd) - Tuple consisting of
    *     yMean - mean of the labels
    *     xColMean - Row vector with mean for every column (or feature) of the input data
    *     xColSd - Row vector standard deviation for every column (or feature) of the input data.
    */
-  def computeStats(data: RDD[LabeledPoint], nfeatures: Int, nexamples: Long):
-      (Double, DoubleMatrix, DoubleMatrix) = {
-    val yMean: Double = data.map { labeledPoint => labeledPoint.label }.reduce(_ + _) / nexamples
+  def computeStats(data: RDD[LabeledPoint], numFeatures: Int, numExamples: Long)
+    : (Double, Vector, Vector) = {
 
-    // NOTE: We shuffle X by column here to compute column sum and sum of squares.
-    val xColSumSq: RDD[(Int, (Double, Double))] = data.flatMap { labeledPoint =>
-      val nCols = labeledPoint.features.length
-      // Traverse over every column and emit (col, value, value^2)
-      Iterator.tabulate(nCols) { i =>
-        (i, (labeledPoint.features(i), labeledPoint.features(i)*labeledPoint.features(i)))
-      }
-    }.reduceByKey { case(x1, x2) =>
-      (x1._1 + x2._1, x1._2 + x2._2)
+    val brzData = data.map { case LabeledPoint(label, features) =>
+      (label, features.toBreeze)
     }
-    val xColSumsMap = xColSumSq.collectAsMap()
 
-    val xColMean = DoubleMatrix.zeros(nfeatures, 1)
-    val xColSd = DoubleMatrix.zeros(nfeatures, 1)
+    val aggStats = brzData.aggregate(
+      (0L, 0.0, BDV.zeros[Double](numFeatures), BDV.zeros[Double](numFeatures))
+    )(
+      seqOp = (c , v) => (c, v) match {
+        case ((n, sumLabel, sum, sumSq), (label, features)) =>
+          features.activeIterator.foreach { case (i, x) =>
+            sumSq(i) += x * x
+          }
+          (n + 1L, sumLabel + label, sum += features, sumSq)
+      },
+      combOp = (c1, c2) => (c1, c2) match {
+        case ((n1, sumLabel1, sum1, sumSq1), (n2, sumLabel2, sum2, sumSq2)) =>
+          (n1 + n2, sumLabel1 + sumLabel2, sum1 += sum2, sumSq1 += sumSq2)
+      }
+    )
+
+    val (nl, sumLabel, sum, sumSq) = aggStats
+    require(nl > 0, "Input data is empty.")
 
-    // Compute mean and unbiased variance using column sums
-    var col = 0
-    while (col < nfeatures) {
-      xColMean.put(col, xColSumsMap(col)._1 / nexamples)
-      val variance =
-        (xColSumsMap(col)._2 - (math.pow(xColSumsMap(col)._1, 2) / nexamples)) / nexamples
-      xColSd.put(col, math.sqrt(variance))
-      col += 1
+    val n = nl.toDouble
+    val yMean = sumLabel / n
+    val mean: BDV[Double] = sum / n
+    val std = new Array[Double](sum.length)
+    var i = 0
+    while (i < numFeatures) {
+      std(i) = sumSq(i) / n - mean(i) * mean(i)
+      i += 1
     }
 
-    (yMean, xColMean, xColSd)
+    (yMean, Vectors.fromBreeze(mean), Vectors.dense(std))
   }
 
   /**

mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala

@@ -17,14 +17,12 @@
 
 package org.apache.spark.mllib.regression
 
+import org.scalatest.FunSuite
 
 import org.jblas.DoubleMatrix
-import org.scalatest.BeforeAndAfterAll
-import org.scalatest.FunSuite
 
 import org.apache.spark.mllib.util.{LinearDataGenerator, LocalSparkContext}
 
-
 class RidgeRegressionSuite extends FunSuite with LocalSparkContext {
 
   def predictionError(predictions: Seq[Double], input: Seq[LabeledPoint]) = {