Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/3636#discussion_r21724910
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
---
@@ -182,34 +203,46 @@ object GradientDescent extends Logging {
var regVal = updater.compute(
weights, Vectors.dense(new Array[Double](weights.size)), 0, 1,
regParam)._2
- for (i <- 1 to numIterations) {
- val bcWeights = data.context.broadcast(weights)
- // 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, miniBatchSize) = data.sample(false,
miniBatchFraction, 42 + i)
- .treeAggregate((BDV.zeros[Double](n), 0.0, 0L))(
- seqOp = (c, v) => {
- // c: (grad, loss, count), v: (label, features)
- val l = gradient.compute(v._2, v._1, bcWeights.value,
Vectors.fromBreeze(c._1))
- (c._1, c._2 + l, c._3 + 1)
- },
- combOp = (c1, c2) => {
- // c: (grad, loss, count)
- (c1._1 += c2._1, c1._2 + c2._2, c1._3 + c2._3)
- })
-
- if (miniBatchSize > 0) {
- /**
- * 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, Vectors.fromBreeze(gradientSum /
miniBatchSize.toDouble), stepSize, i, regParam)
- weights = update._1
- regVal = update._2
- } else {
- logWarning(s"Iteration ($i/$numIterations). The size of sampled
batch is zero")
+ val b = new Breaks
+ b.breakable {
+ for (i <- 1 to numIterations) {
+ val bcWeights = data.context.broadcast(weights)
+ // 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, miniBatchSize) = data.sample(false,
miniBatchFraction, 42 + i)
+ .treeAggregate((BDV.zeros[Double](n), 0.0, 0L))(
+ seqOp = (c, v) => {
+ // c: (grad, loss, count), v: (label, features)
+ val l = gradient.compute(v._2, v._1, bcWeights.value,
Vectors.fromBreeze(c._1))
+ (c._1, c._2 + l, c._3 + 1)
+ },
+ combOp = (c1, c2) => {
+ // c: (grad, loss, count)
+ (c1._1 += c2._1, c1._2 + c2._2, c1._3 + c2._3)
+ })
+
+ if (miniBatchSize > 0) {
+ /**
+ * 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(
--- End diff --
No need to split arguments across multiple lines; I would leave this as it
was. It's OK to have a line wrap in the middle of the argument list, if needed
to make each line < 100 chars.
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