Github user mengxr commented on a diff in the pull request: https://github.com/apache/spark/pull/353#discussion_r11571555 --- Diff: mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala --- @@ -0,0 +1,259 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.optimization + +import scala.collection.mutable.ArrayBuffer + +import breeze.linalg.{DenseVector => BDV, axpy} +import breeze.optimize.{CachedDiffFunction, DiffFunction} + +import org.apache.spark.Logging +import org.apache.spark.rdd.RDD +import org.apache.spark.mllib.linalg.{Vectors, Vector} + +/** + * Class used to solve an optimization problem using Limited-memory BFGS. + * Reference: [[http://en.wikipedia.org/wiki/Limited-memory_BFGS]] + * @param gradient Gradient function to be used. + * @param updater Updater to be used to update weights after every iteration. + */ +class LBFGS(private var gradient: Gradient, private var updater: Updater) + extends Optimizer with Logging { + + private var numCorrections = 10 + 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. + * Values of numCorrections less than 3 are not recommended; large values + * of numCorrections will result in excessive computing time. + * 3 < numCorrections < 10 is recommended. + * Restriction: numCorrections > 0 + */ + def setNumCorrections(corrections: Int): this.type = { + assert(corrections > 0) + this.numCorrections = corrections + this + } + + /** + * 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. + */ + def setConvergenceTol(tolerance: Int): this.type = { + this.convergenceTol = tolerance + this + } + + /** + * Set the maximal number of iterations for L-BFGS. Default 100. + */ + def setMaxNumIterations(iters: Int): this.type = { + this.maxNumIterations = iters + this + } + + /** + * Set the regularization parameter. Default 0.0. + */ + def setRegParam(regParam: Double): this.type = { + this.regParam = regParam + this + } + + /** + * Set the gradient function (of the loss function of one single data example) + * to be used for L-BFGS. + */ + def setGradient(gradient: Gradient): this.type = { + this.gradient = gradient + this + } + + /** + * Set the updater function to actually perform a gradient step in a given direction. + * The updater is responsible to perform the update from the regularization term as well, + * and therefore determines what kind or regularization is used, if any. + */ + def setUpdater(updater: Updater): this.type = { + this.updater = updater + this + } + + override def optimize(data: RDD[(Double, Vector)], initialWeights: Vector): Vector = { + val (weights, _) = LBFGS.runMiniBatchLBFGS( + data, + gradient, + updater, + numCorrections, + convergenceTol, + maxNumIterations, + regParam, + miniBatchFraction, + initialWeights) + weights + } + +} + +/** + * Top-level method to run LBFGS. + */ +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 + * spark map-reduce in each iteration. + * + * @param data - Input data for L-BFGS. RDD of the set of data examples, each of + * the form (label, [feature values]). + * @param gradient - Gradient object (used to compute the gradient of the loss function of + * one single data example) + * @param updater - Updater function to actually perform a gradient step in a given direction. + * @param numCorrections - The number of corrections used in the L-BFGS update. + * @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( + data: RDD[(Double, Vector)], + gradient: Gradient, + updater: Updater, + numCorrections: Int, + convergenceTol: Double, + maxNumIterations: Int, + regParam: Double, + miniBatchFraction: Double, + initialWeights: Vector): (Vector, Array[Double]) = { + + val lossHistory = new ArrayBuffer[Double](maxNumIterations) + + val nexamples: Long = data.count() --- End diff -- Use `numExamples` instead? `: Long` is not necessary.
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