Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/7705#discussion_r35726221
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala ---
@@ -197,8 +238,86 @@ class LocalLDAModel private[clustering] (
// TODO:
// override def topicDistributions(documents: RDD[(Long, Vector)]):
RDD[(Long, Vector)] = ???
+ /**
+ * Calculate and return per-word likelihood bound, using the `batch` of
+ * documents as evaluation corpus.
+ */
+ // TODO: calcualte logPerplexity over training set online during
training, reusing gammad instead
+ // of performing variational inference again in [[bound()]]
+ def logPerplexity(
+ batch: RDD[(Long, Vector)],
+ totalDocs: Long): Double = {
+ val corpusWords = batch
+ .flatMap { case (_, termCounts) => termCounts.toArray }
+ .reduce(_ + _)
+ val subsampleRatio = totalDocs.toDouble / batch.count()
+ val batchVariationalBound = bound(
+ batch,
+ subsampleRatio,
+ docConcentration,
+ topicConcentration,
+ topicsMatrix.toBreeze.toDenseMatrix,
+ gammaShape,
+ k,
+ vocabSize)
+ val perWordBound = batchVariationalBound / (subsampleRatio *
corpusWords)
+
+ perWordBound
+ }
+
+
+ /**
+ * Estimate the variational bound of documents from `batch`:
+ * E_q[log p(bath)] - E_q[log q(batch)]
+ */
+ private def bound(
+ batch: RDD[(Long, Vector)],
+ subsampleRatio: Double,
+ alpha: Vector,
+ eta: Double,
+ lambda: BDM[Double],
+ gammaShape: Double,
+ k: Int,
+ vocabSize: Long): Double = {
+ val brzAlpha = alpha.toBreeze.toDenseVector
+ // transpose because dirichletExpectation normalizes by row and we
need to normalize
+ // by topic (columns of lambda)
+ val Elogbeta = LDAUtils.dirichletExpectation(lambda.t).t
+
+ var score = batch.map { case (id: Long, termCounts: Vector) =>
+ var docScore = 0.0D
+ val (gammad: BDV[Double], _) =
OnlineLDAOptimizer.variationalTopicInference(
--- End diff --
Will this work when there is an empty document? Can you please test it in
a unit test? If it fails, you can check to see if termCounts has any non-zeros
before calling it (as in the other location variationalTopicInference is
called).
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