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author | Shivaram Venkataraman <shivaram@eecs.berkeley.edu> | 2013-07-29 16:25:17 -0700 |
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committer | Shivaram Venkataraman <shivaram@eecs.berkeley.edu> | 2013-07-29 16:25:17 -0700 |
commit | 07da72b45190f7db9daa2c6bd33577d28e19e659 (patch) | |
tree | c3bf7e2f57ca8fee63b082f4b8549075888ba6bf /mllib | |
parent | fe7298b587b89abffefab2febac4e3861ca2c1c4 (diff) | |
download | spark-07da72b45190f7db9daa2c6bd33577d28e19e659.tar.gz spark-07da72b45190f7db9daa2c6bd33577d28e19e659.tar.bz2 spark-07da72b45190f7db9daa2c6bd33577d28e19e659.zip |
Remove duplicate loss history and clarify why.
Also some minor style fixes.
Diffstat (limited to 'mllib')
3 files changed, 9 insertions, 13 deletions
diff --git a/mllib/src/main/scala/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/spark/mllib/classification/LogisticRegression.scala index bc1c327729..bf3b05dedb 100644 --- a/mllib/src/main/scala/spark/mllib/classification/LogisticRegression.scala +++ b/mllib/src/main/scala/spark/mllib/classification/LogisticRegression.scala @@ -151,7 +151,6 @@ object LogisticRegressionLocalRandomSGD { input: RDD[(Int, Array[Double])], numIterations: Int, stepSize: Double, - miniBatchFraction: Double, initialWeights: Array[Double]) : LogisticRegressionModel = @@ -174,7 +173,6 @@ object LogisticRegressionLocalRandomSGD { input: RDD[(Int, Array[Double])], numIterations: Int, stepSize: Double, - miniBatchFraction: Double) : LogisticRegressionModel = { @@ -195,8 +193,7 @@ object LogisticRegressionLocalRandomSGD { def train( input: RDD[(Int, Array[Double])], numIterations: Int, - stepSize: Double - ) + stepSize: Double) : LogisticRegressionModel = { train(input, numIterations, stepSize, 1.0) diff --git a/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala b/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala index d4b83a1456..19cda26446 100644 --- a/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala +++ b/mllib/src/main/scala/spark/mllib/optimization/GradientDescent.scala @@ -61,7 +61,7 @@ object GradientDescent { // Initialize weights as a column vector var weights = new DoubleMatrix(initialWeights.length, 1, initialWeights:_*) - var reg_val = 0.0 + var regVal = 0.0 for (i <- 1 to numIters) { val (gradientSum, lossSum) = data.sample(false, miniBatchFraction, 42+i).map { @@ -71,15 +71,14 @@ object GradientDescent { (grad, loss) }.reduce((a, b) => (a._1.addi(b._1), a._2 + b._2)) - stochasticLossHistory.append(lossSum / miniBatchSize + reg_val) + /** + * 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 = update._1 - reg_val = update._2 - stochasticLossHistory.append(lossSum / miniBatchSize + reg_val) - /* - * NOTE(Xinghao): The loss here is sum of lossSum computed using the weights before applying updater, - * and reg_val using weights after applying updater - */ + regVal = update._2 } (weights.toArray, stochasticLossHistory.toArray) diff --git a/mllib/src/main/scala/spark/mllib/optimization/Updater.scala b/mllib/src/main/scala/spark/mllib/optimization/Updater.scala index 188fe7d972..bbf21e5c28 100644 --- a/mllib/src/main/scala/spark/mllib/optimization/Updater.scala +++ b/mllib/src/main/scala/spark/mllib/optimization/Updater.scala @@ -76,7 +76,7 @@ class SquaredL2Updater extends Updater { val thisIterStepSize = stepSize / math.sqrt(iter) val normGradient = gradient.mul(thisIterStepSize) val newWeights = weightsOld.sub(normGradient).div(2.0 * thisIterStepSize * regParam + 1.0) - (newWeights, pow(newWeights.norm2,2.0) * regParam) + (newWeights, pow(newWeights.norm2, 2.0) * regParam) } } |