Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/7705#discussion_r35726249
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala ---
@@ -463,15 +458,73 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
new BDM[Double](col, row, temp).t
}
+
+ override private[clustering] def getLDAModel(iterationTimes:
Array[Double]): LDAModel = {
+ new LocalLDAModel(Matrices.fromBreeze(lambda).transpose, alpha, eta,
gammaShape)
+ }
+
+}
+
+/**
+ * Serializable companion object containing helper methods and shared code
for
+ * [[OnlineLDAOptimizer]] and [[LocalLDAModel]].
+ */
+object OnlineLDAOptimizer {
/**
- * For theta ~ Dir(alpha), computes E[log(theta)] given alpha. Currently
the implementation
- * uses digamma which is accurate but expensive.
+ * Uses variational inference to infer the topic distribution `gammad`
given the term counts
+ * for a document.
+ *
+ * An optimization (Lee, Seung: Algorithms for non-negative matrix
factorization, NIPS 2001)
+ * avoids explicit computation of variational parameter `phi`.
+ * @see http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.31.7566
*/
- private def dirichletExpectation(alpha: BDM[Double]): BDM[Double] = {
- val rowSum = sum(alpha(breeze.linalg.*, ::))
- val digAlpha = digamma(alpha)
- val digRowSum = digamma(rowSum)
- val result = digAlpha(::, breeze.linalg.*) - digRowSum
- result
+ private[clustering] def variationalTopicInference(
+ termCounts: Vector,
+ expElogbeta: BDM[Double],
+ alpha: breeze.linalg.Vector[Double],
+ gammaShape: Double,
+ k: Int): (BDV[Double], BDM[Double]) = {
+ val (ids: List[Int], cts: Array[Double]) = termCounts match {
+ case v: DenseVector => ((0 until v.size).toList, v.values)
+ case v: SparseVector => (v.indices.toList, v.values)
+ case v => throw new IllegalArgumentException("Online LDA does not
support vector type "
+ + v.getClass)
+ }
+ // Initialize the variational distribution q(theta|gamma) for the
mini-batch
+ val gammad: BDV[Double] =
+ new Gamma(gammaShape, 1.0 / gammaShape).samplesVector(k)
// K
+ val expElogthetad: BDV[Double] =
exp(LDAUtils.dirichletExpectation(gammad)) // K
+ val expElogbetad = expElogbeta(ids, ::).toDenseMatrix
// ids * K
+
+ val phinorm: BDV[Double] = expElogbetad * expElogthetad + 1e-100
// ids
+ var meanchange = 1D
+ val ctsVector = new BDV[Double](cts)
// ids
+
+ // Iterate between gamma and phi until convergence
+ while (meanchange > 1e-3) {
+ val lastgamma = gammad.copy
+ // K K * ids ids
+ gammad := (expElogthetad :* (expElogbetad.t * (ctsVector /
phinorm))) + alpha
--- End diff --
I'd prefer not to change Breeze operators if there is no need, even if they
are equivalent, since we could accidentally introduce errors. Feel free to
leave what you have; this is just a note for the future.
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