Github user hhbyyh commented on a diff in the pull request:
https://github.com/apache/spark/pull/17461#discussion_r110827869
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
---
@@ -315,6 +315,27 @@ class LDA private (
this
}
+ // Initial LDAModel can be provided rather than using random
initialization
+ private var initialModel: Option[LDAModel] = None
+
+ /**
+ * Set the initial starting point, bypassing the random initialization.
+ * This can be used for incremental learning.
+ * This is supported only for online optimizer, and the condition
model.k == this.k must be met,
+ * failure results in an IllegalArgumentException.
+ */
+ @Since("2.2.0")
+ def setInitialModel(model: LDAModel): this.type = {
+ require(model.k == k, "mismatched number of topics")
+ this.ldaOptimizer match {
+ case _: OnlineLDAOptimizer =>
+ initialModel = Some(model)
+ this
+ case _ => throw new IllegalArgumentException(
+ "Only online optimizer supports initialization with a previous
model.")
+ }
+ }
--- End diff --
I'm thinking we should move all the parameter check into run, since user
may set parameters in different orders.
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