Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/7705#discussion_r35818056
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala ---
@@ -197,8 +229,92 @@ class LocalLDAModel private[clustering] (
// TODO:
// override def topicDistributions(documents: RDD[(Long, Vector)]):
RDD[(Long, Vector)] = ???
+ /**
+ * Calculate and return log variational bound on perplexity using the
provided `documents` of
+ * documents as a test corpus.
+ * Perplexity on a test corpus is calculated as:
+ * perplexity(documents) = exp( -log p(documents) / numWords )
+ * where p is the LDA model. It is upper bounded by the variational
distribution using:
+ * perplexity(documents) <= exp( -(E_q[log p(documents] - E_q[log
q(documents)]) / numWords }
+ */
+ def logPerplexity(documents: RDD[(Long, Vector)]): Double = {
+ val numDocs = documents.count()
+ val corpusWords = documents
+ .map { case (_, termCounts) => termCounts.toArray.sum }
+ .sum()
+ val subsampleRatio = numDocs.toDouble / documents.count()
+ val batchVariationalBound = bound(documents, subsampleRatio,
docConcentration,
+ topicConcentration, topicsMatrix.toBreeze.toDenseMatrix, gammaShape,
k, vocabSize)
+ val perWordBound = batchVariationalBound / (subsampleRatio *
corpusWords)
+
+ perWordBound
+ }
+
+
+ /**
+ * Estimate the variational bound of documents from `documents`:
+ * log p(documents) >= E_q[log p(documents)] - E_q[log q(documents)])
--- End diff --
It's OK not to include this here since it's a private method and already
documented in the public method.
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