Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1361#discussion_r15723222
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
---
@@ -162,45 +162,55 @@ object GradientDescent extends Logging {
val numExamples = data.count()
val miniBatchSize = numExamples * miniBatchFraction
- // Initialize weights as a column vector
- var weights = Vectors.dense(initialWeights.toArray)
- val n = weights.size
-
- /**
- * For the first iteration, the regVal will be initialized as sum of
weight squares
- * if it's L2 updater; for L1 updater, the same logic is followed.
- */
- 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) = data.sample(false, miniBatchFraction,
42 + i)
- .treeAggregate((BDV.zeros[Double](n), 0.0))(
- seqOp = (c, v) => (c, v) match { case ((grad, loss), (label,
features)) =>
- val l = gradient.compute(features, label, bcWeights.value,
Vectors.fromBreeze(grad))
- (grad, loss + l)
- },
- combOp = (c1, c2) => (c1, c2) match { case ((grad1, loss1),
(grad2, loss2)) =>
- (grad1 += grad2, loss1 + loss2)
- })
+ // if no data, return initial weights to avoid NaNs
+ if (numExamples == 0) {
+
+ logInfo("GradientDescent.runMiniBatchSGD returning initial weights,
no data found")
+ (initialWeights, stochasticLossHistory.toArray)
+
+ } else {
+
+ // Initialize weights as a column vector
+ var weights = Vectors.dense(initialWeights.toArray)
+ val n = weights.size
/**
- * 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.
+ * For the first iteration, the regVal will be initialized as sum of
weight squares
+ * if it's L2 updater; for L1 updater, the same logic is followed.
*/
- stochasticLossHistory.append(lossSum / miniBatchSize + regVal)
- val update = updater.compute(
- weights, Vectors.fromBreeze(gradientSum / miniBatchSize),
stepSize, i, regParam)
- weights = update._1
- regVal = update._2
+ var regVal = updater.compute(
+ weights, Vectors.dense(new Array[Double](weights.size)), 0, 1,
regParam)._2
+
+ for (i <- 1 to numIterations) {
+ // 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) = data.sample(false, miniBatchFraction,
42 + i)
+ .aggregate((BDV.zeros[Double](weights.size), 0.0))(
--- End diff --
`aggregate` -> `.treeAggregate`. We use a tree pattern to avoid sending too
much data to the driver. Does it hurt streaming update performance?
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