Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/8329#discussion_r37571488
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala ---
@@ -575,6 +575,48 @@ class DistributedLDAModel private[clustering] (
}
}
+ /**
+ * Return the top topic for each (doc, term) pair. I.e., for each
document, what is the most
+ * likely topic generating each term?
+ *
+ * @return RDD of (doc ID, assignment of top topic index for each term),
+ * where the assignment is specified via a pair of zippable
arrays
+ * (term indices, topic indices). Note that terms will be
omitted if not present in
+ * the document.
+ */
+ lazy val topicAssignments: RDD[(Long, Array[Int], Array[Int])] = {
+ // For reference, compare the below code with the core part of
EMLDAOptimizer.next().
+ val eta = topicConcentration
+ val W = vocabSize
+ val alpha = docConcentration(0)
+ val N_k = globalTopicTotals
+ val sendMsg: EdgeContext[TopicCounts, TokenCount, (Array[Int],
Array[Int])] => Unit =
+ (edgeContext) => {
+ // E-STEP: Compute gamma_{wjk} (smoothed topic distributions).
+ val scaledTopicDistribution: TopicCounts =
+ computePTopic(edgeContext.srcAttr, edgeContext.dstAttr, N_k, W,
eta, alpha)
+ // For this (doc j, term w), send top topic k to doc vertex.
+ val topTopic: Int = argmax(scaledTopicDistribution)
+ val term: Int = index2term(edgeContext.dstId)
+ edgeContext.sendToSrc((Array(term), Array(topTopic)))
+ }
+ val mergeMsg: ((Array[Int], Array[Int]), (Array[Int], Array[Int])) =>
(Array[Int], Array[Int]) =
+ (terms_topics0, terms_topics1) => {
+ (terms_topics0._1 ++ terms_topics1._1, terms_topics0._2 ++
terms_topics1._2)
+ }
+ // M-STEP: Aggregation computes new N_{kj}, N_{wk} counts.
+ graph.aggregateMessages[(Array[Int], Array[Int])](sendMsg,
mergeMsg).filter(isDocumentVertex)
+ .map { case (docID: Long, (terms: Array[Int], topics: Array[Int]))
=>
--- End diff --
fix indentation
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