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/*
* 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 spark.mllib.optimization
import spark.{Logging, RDD, SparkContext}
import spark.SparkContext._
import org.jblas.DoubleMatrix
import scala.collection.mutable.ArrayBuffer
object GradientDescent {
/**
* Run gradient descent in parallel using mini batches.
* Based on Matlab code written by John Duchi.
*
* @param data - Input data for SGD. RDD of form (label, [feature values]).
* @param gradient - Gradient object that will be used to compute the gradient.
* @param updater - Updater object that will be used to update the model.
* @param stepSize - stepSize to be used during update.
* @param numIters - number of iterations that SGD should be run.
* @param miniBatchFraction - fraction of the input data set that should be used for
* one iteration of SGD. Default value 1.0.
*
* @return weights - Column matrix containing weights for every feature.
* @return lossHistory - Array containing the loss computed for every iteration.
*/
def runMiniBatchSGD(
data: RDD[(Double, Array[Double])],
gradient: Gradient,
updater: Updater,
stepSize: Double,
numIters: Int,
miniBatchFraction: Double=1.0) : (DoubleMatrix, Array[Double]) = {
val lossHistory = new ArrayBuffer[Double](numIters)
val nfeatures: Int = data.take(1)(0)._2.length
val nexamples: Long = data.count()
val miniBatchSize = nexamples * miniBatchFraction
// Initialize weights as a column matrix
var weights = DoubleMatrix.ones(nfeatures)
var reg_val = 0.0
for (i <- 1 to numIters) {
val (gradientSum, lossSum) = data.sample(false, miniBatchFraction, 42+i).map {
case (y, features) =>
val featuresRow = new DoubleMatrix(features.length, 1, features:_*)
val (grad, loss) = gradient.compute(featuresRow, y, weights)
(grad, loss)
}.reduce((a, b) => (a._1.addi(b._1), a._2 + b._2))
lossHistory.append(lossSum / miniBatchSize + reg_val)
val update = updater.compute(weights, gradientSum.div(miniBatchSize), stepSize, i)
weights = update._1
reg_val = update._2
}
(weights, lossHistory.toArray)
}
}
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