Github user tillrohrmann commented on a diff in the pull request:
https://github.com/apache/flink/pull/692#discussion_r30780392
--- Diff:
flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/optimization/GradientDescent.scala
---
@@ -76,86 +77,163 @@ class GradientDescent(runParameters: ParameterMap)
extends IterativeSolver {
}.withBroadcastSet(currentWeights, WEIGHTVECTOR_BROADCAST)
}
+
+
/** Provides a solution for the given optimization problem
*
* @param data A Dataset of LabeledVector (label, features) pairs
- * @param initWeights The initial weights that will be optimized
+ * @param initialWeights The initial weights that will be optimized
* @return The weights, optimized for the provided data.
*/
override def optimize(
data: DataSet[LabeledVector],
- initWeights: Option[DataSet[WeightVector]]): DataSet[WeightVector] = {
- // TODO: Faster way to do this?
- val dimensionsDS = data.map(_.vector.size).reduce((a, b) => b)
-
- val numberOfIterations: Int = parameterMap(Iterations)
+ initialWeights: Option[DataSet[WeightVector]]): DataSet[WeightVector]
= {
+ val numberOfIterations: Int = parameters(Iterations)
+ // TODO(tvas): This looks out of place, why don't we get back an
Option from
+ // parameters(ConvergenceThreshold)?
+ val convergenceThresholdOption = parameters.get(ConvergenceThreshold)
// Initialize weights
- val initialWeightsDS: DataSet[WeightVector] = initWeights match {
- // Ensure provided weight vector is a DenseVector
- case Some(wvDS) => {
- wvDS.map{wv => {
- val denseWeights = wv.weights match {
- case dv: DenseVector => dv
- case sv: SparseVector => sv.toDenseVector
+ val initialWeightsDS: DataSet[WeightVector] =
createInitialWeightsDS(initialWeights, data)
+
+ // Perform the iterations
+ val optimizedWeights = convergenceThresholdOption match {
+ // No convergence criterion
+ case None =>
+ initialWeightsDS.iterate(numberOfIterations) {
+ weightVectorDS => {
+ SGDStep(data, weightVectorDS)
}
- WeightVector(denseWeights, wv.intercept)
}
-
+ case Some(convergence) =>
+ /** Calculates the regularized loss, from the data and given
weights **/
+ def lossCalculation(data: DataSet[LabeledVector], weightDS:
DataSet[WeightVector]):
+ DataSet[Double] = {
+ data.map {
+ new LossCalculation
+ }.withBroadcastSet(weightDS, WEIGHTVECTOR_BROADCAST)
+ .reduce {
+ (left, right) =>
+ val (leftLoss, leftCount) = left
+ val (rightLoss, rightCount) = right
+ (leftLoss + rightLoss, rightCount + leftCount)
+ }
+ .map{new RegularizedLossCalculation}
+ .withBroadcastSet(weightDS, WEIGHTVECTOR_BROADCAST)
}
- }
- case None => createInitialWeightVector(dimensionsDS)
- }
-
- // Perform the iterations
- // TODO: Enable convergence stopping criterion, as in Multiple Linear
regression
- initialWeightsDS.iterate(numberOfIterations) {
- weightVector => {
- SGDStep(data, weightVector)
- }
+ // We have to calculate for each weight vector the sum of squared
residuals,
+ // and then sum them and apply regularization
+ val initialLossSumDS = lossCalculation(data, initialWeightsDS)
+
+ // Combine weight vector with the current loss
+ val initialWeightsWithLossSum = initialWeightsDS.
+ crossWithTiny(initialLossSumDS).setParallelism(1)
+
+ val resultWithLoss = initialWeightsWithLossSum.
+ iterateWithTermination(numberOfIterations) {
+ weightsWithLossSum =>
+
+ // Extract weight vector and loss
+ val previousWeightsDS = weightsWithLossSum.map{_._1}
+ val previousLossSumDS = weightsWithLossSum.map{_._2}
+
+ val currentWeightsDS = SGDStep(data, previousWeightsDS)
+
+ val currentLossSumDS = lossCalculation(data, currentWeightsDS)
+
+ // Check if the relative change in the loss is smaller than the
+ // convergence threshold. If yes, then terminate i.e. return
empty termination data set
+ val termination =
previousLossSumDS.crossWithTiny(currentLossSumDS).setParallelism(1).
+ filter{
+ pair => {
+ val (previousLoss, currentLoss) = pair
+
+ if (previousLoss <= 0) {
+ false
+ } else {
+ math.abs((previousLoss - currentLoss)/previousLoss) >=
convergence
--- End diff --
Can we also support different convergence criterion? For example, the
absolute loss?
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