Github user EntilZha commented on a diff in the pull request:
https://github.com/apache/spark/pull/4807#discussion_r26188892
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
---
@@ -311,165 +319,319 @@ private[clustering] object LDA {
private[clustering] type TokenCount = Double
- /** Term vertex IDs are {-1, -2, ..., -vocabSize} */
- private[clustering] def term2index(term: Int): Long = -(1 + term.toLong)
- private[clustering] def index2term(termIndex: Long): Int = -(1 +
termIndex).toInt
- private[clustering] def isDocumentVertex(v: (VertexId, _)): Boolean =
v._1 >= 0
+ object LearningAlgorithms extends Enumeration {
+ type Algorithm = Value
+ val Gibbs, EM = Value
+ }
- private[clustering] def isTermVertex(v: (VertexId, _)): Boolean = v._1 < 0
+ private[clustering] trait LearningState {
+ def next(): LearningState
+ def topicsMatrix: Matrix
+ def describeTopics(maxTermsPerTopic: Int): Array[(Array[Int],
Array[Double])]
--- End diff --
The reason for a separate method is twofold. First, although you could
calculate it from `topicsMatrix` in theory, the size of `topicsMatrix` could be
very large (too large to fit in the driver memory, as the docs warn). The
describeTopics is intended to provide an interface for the implementation to
extract a topics matrix bounded to only the top `maxTermsPerTopic` topics. It
is less likely this runs the driver out of memory and keeps computation of the
top `n` topics distributed.
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