Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/7916#discussion_r36149612
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala ---
@@ -267,32 +304,42 @@ class LocalLDAModel private[clustering] (
// by topic (columns of lambda)
val Elogbeta = LDAUtils.dirichletExpectation(lambda.t).t
- var score = documents.filter(_._2.numNonzeros > 0).map { case (id:
Long, termCounts: Vector) =>
- var docScore = 0.0D
+ // Sum bounds for each document:
+ // bounds for prob(tokens) + bounds for prob(document-topic
distribution)
+ documents.filter(_._2.numNonzeros > 0).map { case (id: Long,
termCounts: Vector) =>
+ var docBound = 0.0D
val (gammad: BDV[Double], _) =
OnlineLDAOptimizer.variationalTopicInference(
termCounts, exp(Elogbeta), brzAlpha, gammaShape, k)
val Elogthetad: BDV[Double] = LDAUtils.dirichletExpectation(gammad)
// E[log p(doc | theta, beta)]
termCounts.foreachActive { case (idx, count) =>
- docScore += count * LDAUtils.logSumExp(Elogthetad + Elogbeta(idx,
::).t)
+ docBound += count * LDAUtils.logSumExp(Elogthetad + Elogbeta(idx,
::).t)
}
- // E[log p(theta | alpha) - log q(theta | gamma)]; assumes alpha is
a vector
- docScore += sum((brzAlpha - gammad) :* Elogthetad)
- docScore += sum(lgamma(gammad) - lgamma(brzAlpha))
- docScore += lgamma(sum(brzAlpha)) - lgamma(sum(gammad))
+ // E[log p(theta | alpha) - log q(theta | gamma)]
+ docBound += sum((brzAlpha - gammad) :* Elogthetad)
+ docBound += sum(lgamma(gammad) - lgamma(brzAlpha))
+ docBound += lgamma(sum(brzAlpha)) - lgamma(sum(gammad))
- docScore
+ docBound
}.sum()
+ }
- // E[log p(beta | eta) - log q (beta | lambda)]; assumes eta is a
scalar
- score += sum((eta - lambda) :* Elogbeta)
- score += sum(lgamma(lambda) - lgamma(eta))
-
+ /**
+ * Bound for prob(topic-term distributions):
+ * E[log p(beta | eta) - log q(beta | lambda)]
+ *
+ * See Equation (16) in original Online LDA paper, as well as Appendix
A.3 in the JMLR version of
+ * the original LDA paper.
+ * @param eta topic-word Dirichlet prior parameter
+ * @param lambda parameters for variational q(beta | lambda) topic-word
distributions
+ */
+ private def topicsBound(eta: Double, lambda: BDM[Double]): Double = {
--- End diff --
Did we separate this out just to be consistent with DistributedLDAModel? I
found it a bit confusing since the original calculation follows the
factorization given in the paper (Eq (3)
https://www.cs.princeton.edu/~blei/papers/HoffmanBleiBach2010b.pdf)
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