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

@@ -37,6 +37,10 @@ import org.apache.spark.util.Utils
  *  - "token": instance of a term appearing in a document
  *  - "topic": multinomial distribution over words representing some concept
  *
+ * References:
+ *  - Original LDA paper (journal version):
+ *    Blei, Ng, and Jordan.  "Latent Dirichlet Allocation."  JMLR, 2003.
+ *
  * @see [[http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation Latent Dirichlet allocation
  *       (Wikipedia)]]
  */
@@ -47,12 +51,11 @@ class LDA private (
     private var docConcentration: Double,
     private var topicConcentration: Double,
     private var seed: Long,
-    private var checkpointInterval: Int) extends Logging {
+    private var checkpointInterval: Int,
+    private var ldaOptimizer: LDAOptimizer) extends Logging {
 
   def this() = this(k = 10, maxIterations = 20, docConcentration = -1, topicConcentration = -1,
-    seed = Utils.random.nextLong(), checkpointInterval = 10)
-
-  private var ldaOptimizer: LDAOptimizer = getDefaultOptimizer("EM")
+    seed = Utils.random.nextLong(), checkpointInterval = 10, ldaOptimizer = new EMLDAOptimizer)
 
   /**
    * Number of topics to infer.  I.e., the number of soft cluster centers.
@@ -208,7 +211,7 @@ class LDA private (
 
 
   /** LDAOptimizer used to perform the actual calculation */
-  def getOptimizer(): LDAOptimizer = ldaOptimizer
+  def getOptimizer: LDAOptimizer = ldaOptimizer
 
   /**
    * LDAOptimizer used to perform the actual calculation (default = EMLDAOptimizer)
@@ -220,24 +223,18 @@ class LDA private (
 
   /**
    * Set the LDAOptimizer used to perform the actual calculation by algorithm name.
-   * Currently "EM" is supported.
+   * Currently "em" is supported.
    */
   def setOptimizer(optimizerName: String): this.type = {
-    this.ldaOptimizer = getDefaultOptimizer(optimizerName)
+    this.ldaOptimizer =
+      optimizerName.toLowerCase match {
+        case "em" => new EMLDAOptimizer
+        case other =>
+          throw new IllegalArgumentException(s"Only em is supported but got $other.")
+      }
     this
   }
 
-  /**
-   * Get the default optimizer from String parameter.
-   */
-  private def getDefaultOptimizer(optimizerName: String): LDAOptimizer = {
-    optimizerName match{
-      case "EM" => new EMLDAOptimizer()
-      case other =>
-        throw new UnsupportedOperationException(s"Only EM are supported but got $other.")
-    }
-  }
-
   /**
    * Learn an LDA model using the given dataset.
    *

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

@@ -20,6 +20,7 @@ package org.apache.spark.mllib.clustering
 import java.util.Random
 
 import breeze.linalg.{DenseVector => BDV, normalize}
+
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.graphx._
 import org.apache.spark.graphx.impl.GraphImpl
@@ -30,13 +31,20 @@ import org.apache.spark.rdd.RDD
 /**
  * :: Experimental ::
  *
- * An LDAOptimizer contains an algorithm for LDA and performs the actual computation, which
- * stores internal data structure (Graph or Matrix) and any other parameter for the algorithm.
- * The interface is isolated to improve the extensibility of LDA.
+ * An LDAOptimizer specifies which optimization/learning/inference algorithm to use, and it can
+ * hold optimizer-specific parameters for users to set.
  */
 @Experimental
 trait LDAOptimizer{
 
+  /*
+    DEVELOPERS NOTE:
+
+    An LDAOptimizer contains an algorithm for LDA and performs the actual computation, which
+    stores internal data structure (Graph or Matrix) and other parameters for the algorithm.
+    The interface is isolated to improve the extensibility of LDA.
+   */
+
   /**
    * Initializer for the optimizer. LDA passes the common parameters to the optimizer and
    * the internal structure can be initialized properly.
@@ -75,6 +83,7 @@ trait LDAOptimizer{
 class EMLDAOptimizer extends LDAOptimizer{
 
   import LDA._
+
   /**
    * Following fields will only be initialized through initialState method
    */