Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/7705#discussion_r35786222
--- 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 = {
--- End diff --
Don't think I understand what the distinction between "average" and "total"
perplexity is.
I removed this argument since we can just do `documents.count()` inside the
method.
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