Github user wangmiao1981 commented on a diff in the pull request:
https://github.com/apache/spark/pull/16464#discussion_r94984564
--- Diff: mllib/src/main/scala/org/apache/spark/ml/r/LDAWrapper.scala ---
@@ -172,6 +187,8 @@ private[r] object LDAWrapper extends
MLReadable[LDAWrapper] {
model,
ldaModel.logLikelihood(preprocessedData),
ldaModel.logPerplexity(preprocessedData),
+ trainingLogLikelihood,
+ logPrior,
--- End diff --
LogPrior is calculated based on the serialized topics etc, which are also
used by the trainingLikelyhood. But the trainingLikelyhood is the same for both
original and loaded model. Let me debug more. It looks like a bug. The original
MLLIB implementation doesn't serialize the two parameters as they can be
calculated from other saved values. In addition, there is no unit test for
comparing the two values, which could be the reason of not catching this issue.
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