[SPARK-7421] [mllib] OnlineLDA cleanups

mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala

@@ -18,8 +18,9 @@
 package org.apache.spark.mllib.clustering
 
 import breeze.linalg.{DenseVector => BDV}
+
 import org.apache.spark.Logging
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{DeveloperApi, Experimental}
 import org.apache.spark.api.java.JavaPairRDD
 import org.apache.spark.graphx._
 import org.apache.spark.mllib.linalg.Vector
@@ -197,20 +198,28 @@ class LDA private (
   }
 
 
-  /** LDAOptimizer used to perform the actual calculation */
+  /**
+   * :: DeveloperApi ::
+   *
+   * LDAOptimizer used to perform the actual calculation
+   */
+  @DeveloperApi
   def getOptimizer: LDAOptimizer = ldaOptimizer
 
   /**
+   * :: DeveloperApi ::
+   *
    * LDAOptimizer used to perform the actual calculation (default = EMLDAOptimizer)
    */
+  @DeveloperApi
   def setOptimizer(optimizer: LDAOptimizer): this.type = {
     this.ldaOptimizer = optimizer
     this
   }
 
   /**
    * Set the LDAOptimizer used to perform the actual calculation by algorithm name.
-   * Currently "em", "online" is supported.
+   * Currently "em", "online" are supported.
    */
   def setOptimizer(optimizerName: String): this.type = {
     this.ldaOptimizer =

mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala

@@ -23,21 +23,21 @@ import breeze.linalg.{DenseVector => BDV, DenseMatrix => BDM, sum, normalize, kr
 import breeze.numerics.{digamma, exp, abs}
 import breeze.stats.distributions.{Gamma, RandBasis}
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.graphx._
 import org.apache.spark.graphx.impl.GraphImpl
 import org.apache.spark.mllib.impl.PeriodicGraphCheckpointer
 import org.apache.spark.mllib.linalg.{Matrices, SparseVector, DenseVector, Vector}
 import org.apache.spark.rdd.RDD
 
 /**
- * :: Experimental ::
+ * :: DeveloperApi ::
  *
  * An LDAOptimizer specifies which optimization/learning/inference algorithm to use, and it can
  * hold optimizer-specific parameters for users to set.
  */
-@Experimental
-trait LDAOptimizer {
+@DeveloperApi
+sealed trait LDAOptimizer {
 
   /*
     DEVELOPERS NOTE:
@@ -59,7 +59,7 @@ trait LDAOptimizer {
 }
 
 /**
- * :: Experimental ::
+ * :: DeveloperApi ::
  *
  * Optimizer for EM algorithm which stores data + parameter graph, plus algorithm parameters.
  *
@@ -75,8 +75,8 @@ trait LDAOptimizer {
  *    "On Smoothing and Inference for Topic Models."  UAI, 2009.
  *
  */
-@Experimental
-class EMLDAOptimizer extends LDAOptimizer {
+@DeveloperApi
+final class EMLDAOptimizer extends LDAOptimizer {
 
   import LDA._
 
@@ -211,7 +211,7 @@ class EMLDAOptimizer extends LDAOptimizer {
 
 
 /**
- * :: Experimental ::
+ * :: DeveloperApi ::
  *
  * An online optimizer for LDA. The Optimizer implements the Online variational Bayes LDA
  * algorithm, which processes a subset of the corpus on each iteration, and updates the term-topic
@@ -220,8 +220,8 @@ class EMLDAOptimizer extends LDAOptimizer {
  * Original Online LDA paper:
  *   Hoffman, Blei and Bach, "Online Learning for Latent Dirichlet Allocation." NIPS, 2010.
  */
-@Experimental
-class OnlineLDAOptimizer extends LDAOptimizer {
+@DeveloperApi
+final class OnlineLDAOptimizer extends LDAOptimizer {
 
   // LDA common parameters
   private var k: Int = 0
@@ -243,8 +243,8 @@ class OnlineLDAOptimizer extends LDAOptimizer {
   private var randomGenerator: java.util.Random = null
 
   // Online LDA specific parameters
-  // Learning rate is: (tau_0 + t)^{-kappa}
-  private var tau_0: Double = 1024
+  // Learning rate is: (tau0 + t)^{-kappa}
+  private var tau0: Double = 1024
   private var kappa: Double = 0.51
   private var miniBatchFraction: Double = 0.05
 
@@ -265,16 +265,16 @@ class OnlineLDAOptimizer extends LDAOptimizer {
    * A (positive) learning parameter that downweights early iterations. Larger values make early
    * iterations count less.
    */
-  def getTau_0: Double = this.tau_0
+  def getTau0: Double = this.tau0
 
   /**
    * A (positive) learning parameter that downweights early iterations. Larger values make early
    * iterations count less.
    * Default: 1024, following the original Online LDA paper.
    */
-  def setTau_0(tau_0: Double): this.type = {
-    require(tau_0 > 0,  s"LDA tau_0 must be positive, but was set to $tau_0")
-    this.tau_0 = tau_0
+  def setTau0(tau0: Double): this.type = {
+    require(tau0 > 0,  s"LDA tau0 must be positive, but was set to $tau0")
+    this.tau0 = tau0
     this
   }
 
@@ -434,11 +434,8 @@ class OnlineLDAOptimizer extends LDAOptimizer {
    * Update lambda based on the batch submitted. batchSize can be different for each iteration.
    */
   private[clustering] def update(stat: BDM[Double], iter: Int, batchSize: Int): Unit = {
-    val tau_0 = this.getTau_0
-    val kappa = this.getKappa
-
     // weight of the mini-batch.
-    val weight = math.pow(tau_0 + iter, -kappa)
+    val weight = math.pow(getTau0 + iter, -getKappa)
 
     // Update lambda based on documents.
     lambda = lambda * (1 - weight) +

mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java

@@ -117,7 +117,7 @@ public void OnlineOptimizerCompatibility() {
 
     // Train a model
     OnlineLDAOptimizer op = new OnlineLDAOptimizer()
-      .setTau_0(1024)
+      .setTau0(1024)
       .setKappa(0.51)
       .setGammaShape(1e40)
       .setMiniBatchFraction(0.5);

mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala

@@ -138,12 +138,12 @@ class LDASuite extends FunSuite with MLlibTestSparkContext {
     val lda = new LDA().setK(2)
     val corpus = sc.parallelize(tinyCorpus, 2)
     val op = new OnlineLDAOptimizer().initialize(corpus, lda)
-    op.setKappa(0.9876).setMiniBatchFraction(0.123).setTau_0(567)
+    op.setKappa(0.9876).setMiniBatchFraction(0.123).setTau0(567)
     assert(op.getAlpha == 0.5) // default 1.0 / k
     assert(op.getEta == 0.5)   // default 1.0 / k
     assert(op.getKappa == 0.9876)
     assert(op.getMiniBatchFraction == 0.123)
-    assert(op.getTau_0 == 567)
+    assert(op.getTau0 == 567)
   }
 
   test("OnlineLDAOptimizer one iteration") {
@@ -159,7 +159,7 @@ class LDASuite extends FunSuite with MLlibTestSparkContext {
     val corpus = sc.parallelize(docs, 2)
 
     // Set GammaShape large to avoid the stochastic impact.
-    val op = new OnlineLDAOptimizer().setTau_0(1024).setKappa(0.51).setGammaShape(1e40)
+    val op = new OnlineLDAOptimizer().setTau0(1024).setKappa(0.51).setGammaShape(1e40)
       .setMiniBatchFraction(1)
     val lda = new LDA().setK(k).setMaxIterations(1).setOptimizer(op).setSeed(12345)
 
@@ -192,7 +192,7 @@ class LDASuite extends FunSuite with MLlibTestSparkContext {
     ).zipWithIndex.map { case (wordCounts, docId) => (docId.toLong, wordCounts) }
 
     val docs = sc.parallelize(toydata)
-    val op = new OnlineLDAOptimizer().setMiniBatchFraction(1).setTau_0(1024).setKappa(0.51)
+    val op = new OnlineLDAOptimizer().setMiniBatchFraction(1).setTau0(1024).setKappa(0.51)
       .setGammaShape(1e10)
     val lda = new LDA().setK(2)
       .setDocConcentration(0.01)