Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/7705#discussion_r35726237
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala ---
@@ -385,59 +387,52 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
iteration += 1
val k = this.k
val vocabSize = this.vocabSize
- val Elogbeta = dirichletExpectation(lambda).t
- val expElogbeta = exp(Elogbeta)
+ val expElogbeta = exp(LDAUtils.dirichletExpectation(lambda)).t
val alpha = this.alpha.toBreeze
val gammaShape = this.gammaShape
- val stats: RDD[BDM[Double]] = batch.mapPartitions { docs =>
+ val stats: RDD[(BDM[Double], List[BDV[Double]])] = batch.mapPartitions
{ docs =>
val stat = BDM.zeros[Double](k, vocabSize)
- docs.foreach { doc =>
- val termCounts = doc._2
- val (ids: List[Int], cts: Array[Double]) = termCounts match {
- case v: DenseVector => ((0 until v.size).toList, v.values)
- case v: SparseVector => (v.indices.toList, v.values)
- case v => throw new IllegalArgumentException("Online LDA does
not support vector type "
- + v.getClass)
+ var gammaPart = List[BDV[Double]]()
+ docs.foreach { case (_, termCounts: Vector) =>
+ val ids: List[Int] = termCounts match {
+ case v: DenseVector => (0 until v.size).toList
+ case v: SparseVector => v.indices.toList
}
if (!ids.isEmpty) {
-
- // Initialize the variational distribution q(theta|gamma) for
the mini-batch
- val gammad: BDV[Double] =
- new Gamma(gammaShape, 1.0 / gammaShape).samplesVector(k) // K
- val expElogthetad: BDV[Double] = exp(digamma(gammad) -
digamma(sum(gammad))) // K
- val expElogbetad: BDM[Double] = expElogbeta(ids,
::).toDenseMatrix // ids * K
-
- val phinorm: BDV[Double] = expElogbetad * expElogthetad :+
1e-100 // ids
- var meanchange = 1D
- val ctsVector = new BDV[Double](cts) // ids
-
- // Iterate between gamma and phi until convergence
- while (meanchange > 1e-3) {
- val lastgamma = gammad.copy
- // K K * ids ids
- gammad := (expElogthetad :* (expElogbetad.t * (ctsVector :/
phinorm))) :+ alpha
- expElogthetad := exp(digamma(gammad) - digamma(sum(gammad)))
- phinorm := expElogbetad * expElogthetad :+ 1e-100
- meanchange = sum(abs(gammad - lastgamma)) / k
- }
-
- stat(::, ids) := expElogthetad.asDenseMatrix.t * (ctsVector :/
phinorm).asDenseMatrix
+ val (gammad, sstats) =
OnlineLDAOptimizer.variationalTopicInference(
+ termCounts, expElogbeta, alpha, gammaShape, k)
+ stat(::, ids) := sstats
+ gammaPart = gammad :: gammaPart
}
}
- Iterator(stat)
+ Iterator((stat, gammaPart))
}
-
- val statsSum: BDM[Double] = stats.reduce(_ += _)
+ val statsSum: BDM[Double] = stats.map(_._1).reduce(_ += _)
+ val gammat: BDM[Double] = breeze.linalg.DenseMatrix.vertcat(
+ stats.map(_._2).reduce(_ ++ _).map(_.toDenseMatrix): _*)
val batchResult = statsSum :* expElogbeta.t
// Note that this is an optimization to avoid batch.count
- update(batchResult, iteration, (miniBatchFraction *
corpusSize).ceil.toInt)
+ updateLambda(batchResult, (miniBatchFraction * corpusSize).ceil.toInt)
this
}
- override private[clustering] def getLDAModel(iterationTimes:
Array[Double]): LDAModel = {
- new LocalLDAModel(Matrices.fromBreeze(lambda).transpose)
+ /**
+ * Update lambda based on the batch submitted. batchSize can be
different for each iteration.
+ */
+ private def updateLambda(stat: BDM[Double], batchSize: Int): Unit = {
--- End diff --
Should update() be removed? It seems like this method replaces the old
update().
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