diff options
Diffstat (limited to 'mllib')
-rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala | 63 | ||||
-rw-r--r-- | mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala | 15 |
2 files changed, 30 insertions, 48 deletions
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 969a0c5f7c..8f187c9df5 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 @@ -42,7 +42,6 @@ class LBFGS(private var gradient: Gradient, private var updater: Updater) private var convergenceTol = 1E-4 private var maxNumIterations = 100 private var regParam = 0.0 - private var miniBatchFraction = 1.0 /** * Set the number of corrections used in the LBFGS update. Default 10. @@ -58,14 +57,6 @@ class LBFGS(private var gradient: Gradient, private var updater: Updater) } /** - * Set fraction of data to be used for each L-BFGS iteration. Default 1.0. - */ - def setMiniBatchFraction(fraction: Double): this.type = { - this.miniBatchFraction = fraction - this - } - - /** * Set the convergence tolerance of iterations for L-BFGS. Default 1E-4. * Smaller value will lead to higher accuracy with the cost of more iterations. */ @@ -110,7 +101,7 @@ class LBFGS(private var gradient: Gradient, private var updater: Updater) } override def optimize(data: RDD[(Double, Vector)], initialWeights: Vector): Vector = { - val (weights, _) = LBFGS.runMiniBatchLBFGS( + val (weights, _) = LBFGS.runLBFGS( data, gradient, updater, @@ -118,7 +109,6 @@ class LBFGS(private var gradient: Gradient, private var updater: Updater) convergenceTol, maxNumIterations, regParam, - miniBatchFraction, initialWeights) weights } @@ -132,10 +122,8 @@ class LBFGS(private var gradient: Gradient, private var updater: Updater) @DeveloperApi object LBFGS extends Logging { /** - * Run Limited-memory BFGS (L-BFGS) in parallel using mini batches. - * In each iteration, we sample a subset (fraction miniBatchFraction) of the total data - * in order to compute a gradient estimate. - * Sampling, and averaging the subgradients over this subset is performed using one standard + * Run Limited-memory BFGS (L-BFGS) in parallel. + * Averaging the subgradients over different partitions is performed using one standard * spark map-reduce in each iteration. * * @param data - Input data for L-BFGS. RDD of the set of data examples, each of @@ -147,14 +135,12 @@ object LBFGS extends Logging { * @param convergenceTol - The convergence tolerance of iterations for L-BFGS * @param maxNumIterations - Maximal number of iterations that L-BFGS can be run. * @param regParam - Regularization parameter - * @param miniBatchFraction - Fraction of the input data set that should be used for - * one iteration of L-BFGS. Default value 1.0. * * @return A tuple containing two elements. The first element is a column matrix containing * weights for every feature, and the second element is an array containing the loss * computed for every iteration. */ - def runMiniBatchLBFGS( + def runLBFGS( data: RDD[(Double, Vector)], gradient: Gradient, updater: Updater, @@ -162,23 +148,33 @@ object LBFGS extends Logging { convergenceTol: Double, maxNumIterations: Int, regParam: Double, - miniBatchFraction: Double, initialWeights: Vector): (Vector, Array[Double]) = { val lossHistory = new ArrayBuffer[Double](maxNumIterations) val numExamples = data.count() - val miniBatchSize = numExamples * miniBatchFraction val costFun = - new CostFun(data, gradient, updater, regParam, miniBatchFraction, lossHistory, miniBatchSize) + new CostFun(data, gradient, updater, regParam, numExamples) val lbfgs = new BreezeLBFGS[BDV[Double]](maxNumIterations, numCorrections, convergenceTol) - val weights = Vectors.fromBreeze( - lbfgs.minimize(new CachedDiffFunction(costFun), initialWeights.toBreeze.toDenseVector)) + val states = + lbfgs.iterations(new CachedDiffFunction(costFun), initialWeights.toBreeze.toDenseVector) + + /** + * NOTE: lossSum and loss is computed using the weights from the previous iteration + * and regVal is the regularization value computed in the previous iteration as well. + */ + var state = states.next() + while(states.hasNext) { + lossHistory.append(state.value) + state = states.next() + } + lossHistory.append(state.value) + val weights = Vectors.fromBreeze(state.x) - logInfo("LBFGS.runMiniBatchSGD finished. Last 10 losses %s".format( + logInfo("LBFGS.runLBFGS finished. Last 10 losses %s".format( lossHistory.takeRight(10).mkString(", "))) (weights, lossHistory.toArray) @@ -193,9 +189,7 @@ object LBFGS extends Logging { gradient: Gradient, updater: Updater, regParam: Double, - miniBatchFraction: Double, - lossHistory: ArrayBuffer[Double], - miniBatchSize: Double) extends DiffFunction[BDV[Double]] { + numExamples: Long) extends DiffFunction[BDV[Double]] { private var i = 0 @@ -204,8 +198,7 @@ object LBFGS extends Logging { val localData = data val localGradient = gradient - val (gradientSum, lossSum) = localData.sample(false, miniBatchFraction, 42 + i) - .aggregate((BDV.zeros[Double](weights.size), 0.0))( + val (gradientSum, lossSum) = localData.aggregate((BDV.zeros[Double](weights.size), 0.0))( seqOp = (c, v) => (c, v) match { case ((grad, loss), (label, features)) => val l = localGradient.compute( features, label, Vectors.fromBreeze(weights), Vectors.fromBreeze(grad)) @@ -223,7 +216,7 @@ object LBFGS extends Logging { Vectors.fromBreeze(weights), Vectors.dense(new Array[Double](weights.size)), 0, 1, regParam)._2 - val loss = lossSum / miniBatchSize + regVal + val loss = lossSum / numExamples + regVal /** * It will return the gradient part of regularization using updater. * @@ -245,14 +238,8 @@ object LBFGS extends Logging { Vectors.fromBreeze(weights), Vectors.dense(new Array[Double](weights.size)), 1, 1, regParam)._1.toBreeze - // gradientTotal = gradientSum / miniBatchSize + gradientTotal - axpy(1.0 / miniBatchSize, gradientSum, gradientTotal) - - /** - * NOTE: lossSum and loss is computed using the weights from the previous iteration - * and regVal is the regularization value computed in the previous iteration as well. - */ - lossHistory.append(loss) + // gradientTotal = gradientSum / numExamples + gradientTotal + axpy(1.0 / numExamples, gradientSum, gradientTotal) i += 1 diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala index f33770aed3..6af1b502eb 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala @@ -59,7 +59,7 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers { val convergenceTol = 1e-12 val maxNumIterations = 10 - val (_, loss) = LBFGS.runMiniBatchLBFGS( + val (_, loss) = LBFGS.runLBFGS( dataRDD, gradient, simpleUpdater, @@ -67,7 +67,6 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers { convergenceTol, maxNumIterations, regParam, - miniBatchFrac, initialWeightsWithIntercept) // Since the cost function is convex, the loss is guaranteed to be monotonically decreasing @@ -104,7 +103,7 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers { val convergenceTol = 1e-12 val maxNumIterations = 10 - val (weightLBFGS, lossLBFGS) = LBFGS.runMiniBatchLBFGS( + val (weightLBFGS, lossLBFGS) = LBFGS.runLBFGS( dataRDD, gradient, squaredL2Updater, @@ -112,7 +111,6 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers { convergenceTol, maxNumIterations, regParam, - miniBatchFrac, initialWeightsWithIntercept) val numGDIterations = 50 @@ -150,7 +148,7 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers { val maxNumIterations = 8 var convergenceTol = 0.0 - val (_, lossLBFGS1) = LBFGS.runMiniBatchLBFGS( + val (_, lossLBFGS1) = LBFGS.runLBFGS( dataRDD, gradient, squaredL2Updater, @@ -158,7 +156,6 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers { convergenceTol, maxNumIterations, regParam, - miniBatchFrac, initialWeightsWithIntercept) // Note that the first loss is computed with initial weights, @@ -166,7 +163,7 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers { assert(lossLBFGS1.length == 9) convergenceTol = 0.1 - val (_, lossLBFGS2) = LBFGS.runMiniBatchLBFGS( + val (_, lossLBFGS2) = LBFGS.runLBFGS( dataRDD, gradient, squaredL2Updater, @@ -174,7 +171,6 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers { convergenceTol, maxNumIterations, regParam, - miniBatchFrac, initialWeightsWithIntercept) // Based on observation, lossLBFGS2 runs 3 iterations, no theoretically guaranteed. @@ -182,7 +178,7 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers { assert((lossLBFGS2(2) - lossLBFGS2(3)) / lossLBFGS2(2) < convergenceTol) convergenceTol = 0.01 - val (_, lossLBFGS3) = LBFGS.runMiniBatchLBFGS( + val (_, lossLBFGS3) = LBFGS.runLBFGS( dataRDD, gradient, squaredL2Updater, @@ -190,7 +186,6 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers { convergenceTol, maxNumIterations, regParam, - miniBatchFrac, initialWeightsWithIntercept) // With smaller convergenceTol, it takes more steps. |