Updating analytics to reflect changes in the pregel interface and moving...

Updating analytics to reflect changes in the pregel interface and moving degree information into the edge attribute.

graph/src/main/scala/org/apache/spark/graph/Analytics.scala

@@ -11,43 +11,99 @@ object Analytics extends Logging {
    */
   def pagerank[VD: Manifest, ED: Manifest](graph: Graph[VD, ED],
                                            numIter: Int,
-                                           resetProb: Double = 0.15) = {
-    // Compute the out degree of each vertex
-    val pagerankGraph = graph.outerJoinVertices(graph.outDegrees){
-      (vid, vdata, deg) => (deg.getOrElse(0), 1.0)
-    }
+                                           resetProb: Double = 0.15): Graph[Double, Double] = {
+
+    /**
+     * Initialize the pagerankGraph with each edge attribute 
+     * having weight 1/outDegree and each vertex with attribute 1.0.
+     */
+    val pagerankGraph: Graph[Double, Double] = graph
+      // Associate the degree with each vertex
+      .outerJoinVertices(graph.outDegrees){
+        (vid, vdata, deg) => deg.getOrElse(0)
+      }
+      // Set the weight on the edges based on the degree
+      .mapTriplets( e => 1.0 / e.srcAttr )
+      // Set the vertex attributes to the initial pagerank values
+      .mapVertices( (id, attr) => 1.0 )
 
+    // Display statistics about pagerank
     println(pagerankGraph.statistics)
-    
-    Pregel.iterate[(Int, Double), ED, Double](pagerankGraph)(
-      (vid, data, a: Double) => (data._1, (resetProb + (1.0 - resetProb) * a)), // apply
-      (me_id, edge) => Some(edge.srcAttr._2 / edge.srcAttr._1), // gather
-      (a: Double, b: Double) => a + b, // merge
-      1.0,
-      numIter).mapVertices{ case (id, (outDeg, r)) => r }
+
+    // Define the three functions needed to implement PageRank in the GraphX
+    // version of Pregel
+    def vertexProgram(id: Vid, attr: Double, msgSum: Double): Double =
+      resetProb + (1.0 - resetProb) * msgSum
+    def sendMessage(id: Vid, edge: EdgeTriplet[Double, Double]): Option[Double] =
+      Some(edge.srcAttr / edge.attr)
+    def messageCombiner(a: Double, b: Double): Double = a + b
+    // The initial message received by all vertices in PageRank
+    val initialMessage = 1.0
+
+    // Execute pregel for a fixed number of iterations.      
+    Pregel(pagerankGraph, initialMessage, numIter)(
+      vertexProgram, sendMessage, messageCombiner)
   }
 
 
   /**
    * Compute the PageRank of a graph returning the pagerank of each vertex as an RDD
    */
-  def dynamicPagerank[VD: Manifest, ED: Manifest](graph: Graph[VD, ED],
-                                                  tol: Float,
-                                                  maxIter: Int = Integer.MAX_VALUE,
-                                                  resetProb: Double = 0.15) = {
-    // Compute the out degree of each vertex
-    val pagerankGraph = graph.outerJoinVertices(graph.outDegrees){
-      (id, data, degIter) => (degIter.sum, 1.0, 1.0)
+  def dynamicPagerank[VD: Manifest, ED: Manifest](
+    graph: Graph[VD, ED], tol: Float, resetProb: Double = 0.15): Graph[Double, Double] = {
+
+    /**
+     * Initialize the pagerankGraph with each edge attribute 
+     * having weight 1/outDegree and each vertex with attribute 1.0.
+     */
+    val pagerankGraph: Graph[(Double, Double), Double] = graph
+      // Associate the degree with each vertex
+      .outerJoinVertices(graph.outDegrees){
+        (vid, vdata, deg) => deg.getOrElse(0)
+      }
+      // Set the weight on the edges based on the degree 
+      .mapTriplets( e => 1.0 / e.srcAttr )
+      // Set the vertex attributes to (initalPR, delta = 0)
+      .mapVertices( (id, attr) => (resetProb, 0.0) )
+
+    // Display statistics about pagerank
+    println(pagerankGraph.statistics)
+
+    // Define the three functions needed to implement PageRank in the GraphX
+    // version of Pregel
+    def vertexProgram(id: Vid, attr: (Double, Double), msgSum: Double): (Double, Double) = {
+      val (oldPR, lastDelta) = attr
+      val newPR = oldPR + (1.0 - resetProb) * msgSum
+      (newPR, newPR - oldPR)
     }
+    def sendMessage(id: Vid, edge: EdgeTriplet[(Double, Double), Double]): Option[Double] = {
+      if (edge.srcAttr._2 > tol) {
+        Some(edge.srcAttr._2 / edge.attr)
+      } else { None }
+    } 
+    def messageCombiner(a: Double, b: Double): Double = a + b
+    // The initial message received by all vertices in PageRank
+    val initialMessage = 1.0 / (1.0 - resetProb)
+
+    // Execute a dynamic version of Pregel.       
+    Pregel(pagerankGraph, initialMessage)(
+      vertexProgram, sendMessage, messageCombiner)
+      .mapVertices( (vid, attr) => attr._1 )
+
+
+    // // Compute the out degree of each vertex
+    // val pagerankGraph = graph.outerJoinVertices(graph.outDegrees){
+    //   (id, data, degIter) => (degIter.sum, 1.0, 1.0)
+    // }
     
-    // Run PageRank
-    GraphLab.iterate(pagerankGraph)(
-      (me_id, edge) => edge.srcAttr._2 / edge.srcAttr._1, // gather
-      (a: Double, b: Double) => a + b,
-      (id, data, a: Option[Double]) =>
-        (data._1, (resetProb + (1.0 - resetProb) * a.getOrElse(0.0)), data._2), // apply
-      (me_id, edge) => math.abs(edge.srcAttr._3 - edge.srcAttr._2) > tol, // scatter
-      maxIter).mapVertices { case (vid, data) => data._2 }
+    // // Run PageRank
+    // GraphLab.iterate(pagerankGraph)(
+    //   (me_id, edge) => edge.srcAttr._2 / edge.srcAttr._1, // gather
+    //   (a: Double, b: Double) => a + b,
+    //   (id, data, a: Option[Double]) =>
+    //     (data._1, (resetProb + (1.0 - resetProb) * a.getOrElse(0.0)), data._2), // apply
+    //   (me_id, edge) => math.abs(edge.srcAttr._3 - edge.srcAttr._2) > tol, // scatter
+    //   maxIter).mapVertices { case (vid, data) => data._2 }
   }