Merge pull request #116 from jianpingjwang/master

remove unused variables and fix a bug

1 files changed, 32 insertions, 31 deletions

diff --git a/graph/src/main/scala/org/apache/spark/graph/algorithms/Svdpp.scala b/graph/src/main/scala/org/apache/spark/graph/algorithms/Svdpp.scala
index 4ddf0b1fd5..26b999f4cf 100644
--- a/graph/src/main/scala/org/apache/spark/graph/algorithms/Svdpp.scala
+++ b/graph/src/main/scala/org/apache/spark/graph/algorithms/Svdpp.scala
@@ -1,6 +1,5 @@
 package org.apache.spark.graph.algorithms
 
-import org.apache.spark._
 import org.apache.spark.rdd._
 import org.apache.spark.graph._
 import scala.util.Random
@@ -10,7 +9,7 @@ class VT ( // vertex type
   var v1: RealVector, // v1: p for user node, q for item node
   var v2: RealVector, // v2: pu + |N(u)|^(-0.5)*sum(y) for user node, y for item node
   var bias: Double,
-  var norm: Double // only for user node
+  var norm: Double // |N(u)|^(-0.5) for user node
 ) extends Serializable
 
 class Msg ( // message
@@ -20,7 +19,15 @@ class Msg ( // message
 ) extends Serializable
 
 object Svdpp {
-  // implement SVD++ based on http://public.research.att.com/~volinsky/netflix/kdd08koren.pdf
+  /**
+   * Implement SVD++ based on "Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model",
+   * paper is available at [[http://public.research.att.com/~volinsky/netflix/kdd08koren.pdf]].
+   * The prediction rule is rui = u + bu + bi + qi*(pu + |N(u)|^(-0.5)*sum(y)), see the details on page 6.
+   *
+   * @param edges edges for constructing the graph 
+   *
+   * @return a graph with vertex attributes containing the trained model
+   */
 
   def run(edges: RDD[Edge[Double]]): Graph[VT, Double] = {
     // defalut parameters
@@ -32,7 +39,8 @@ object Svdpp {
     val gamma2 = 0.007
     val gamma6 = 0.005
     val gamma7 = 0.015
-	
+
+    // generate default vertex attribute
     def defaultF(rank: Int) = {
       val v1 = new ArrayRealVector(rank)
       val v2 = new ArrayRealVector(rank)
@@ -44,7 +52,7 @@ object Svdpp {
       vd
     }
 
-    // calculate initial norm and bias
+    // calculate initial bias and norm
     def mapF0(et: EdgeTriplet[VT, Double]): Iterator[(Vid, (Long, Double))] = {
       assert(et.srcAttr != null && et.dstAttr != null)
       Iterator((et.srcId, (1L, et.attr)), (et.dstId, (1L, et.attr)))
@@ -67,10 +75,10 @@ object Svdpp {
     // make graph
     var g = Graph.fromEdges(edges, defaultF(rank)).cache()
 
-    // calculate initial norm and bias
+    // calculate initial bias and norm
     val t0 = g.mapReduceTriplets(mapF0, reduceF0)
-    g.outerJoinVertices(t0) {updateF0} 
-	
+    g.outerJoinVertices(t0) {updateF0}
+
     // phase 1
     def mapF1(et: EdgeTriplet[VT, Double]): Iterator[(Vid, RealVector)] = {
       assert(et.srcAttr != null && et.dstAttr != null)
@@ -89,21 +97,18 @@ object Svdpp {
     // phase 2
     def mapF2(et: EdgeTriplet[VT, Double]): Iterator[(Vid, Msg)] = {
       assert(et.srcAttr != null && et.dstAttr != null)
-      val usr = et.srcAttr
-      val itm = et.dstAttr
-      var p = usr.v1
-      var q = itm.v1
-      val itmBias = 0.0
-      val usrBias = 0.0
+      val (usr, itm) = (et.srcAttr, et.dstAttr)
+      val (p, q) = (usr.v1, itm.v1)
       var pred = u + usr.bias + itm.bias + q.dotProduct(usr.v2)
       pred = math.max(pred, minVal)
       pred = math.min(pred, maxVal)
       val err = et.attr - pred
-      val y = (q.mapMultiply(err*usr.norm)).subtract((usr.v2).mapMultiply(gamma7))
-      val newP = (q.mapMultiply(err)).subtract(p.mapMultiply(gamma7)) // for each connected item q
-      val newQ = (usr.v2.mapMultiply(err)).subtract(q.mapMultiply(gamma7))
-      Iterator((et.srcId, new Msg(newP, y, err - gamma6*usr.bias)), (et.dstId, new Msg(newQ, y, err - gamma6*itm.bias)))
-    }	
+      val updateP = (q.mapMultiply(err)).subtract(p.mapMultiply(gamma7))
+      val updateQ = (usr.v2.mapMultiply(err)).subtract(q.mapMultiply(gamma7))
+      val updateY = (q.mapMultiply(err*usr.norm)).subtract((itm.v2).mapMultiply(gamma7))
+      Iterator((et.srcId, new Msg(updateP, updateY, err - gamma6*usr.bias)),
+        (et.dstId, new Msg(updateQ, updateY, err - gamma6*itm.bias)))
+    }
     def reduceF2(g1: Msg, g2: Msg):Msg = {
       g1.v1 = g1.v1.add(g2.v1)
       g1.v2 = g1.v2.add(g2.v2)
@@ -113,7 +118,7 @@ object Svdpp {
     def updateF2(vid: Vid, vd: VT, msg: Option[Msg]) = {
       if (msg.isDefined) {
         vd.v1 = vd.v1.add(msg.get.v1.mapMultiply(gamma2))
-        if (vid % 2 == 1) { // item node update y
+        if (vid % 2 == 1) { // item nodes update y
           vd.v2 = vd.v2.add(msg.get.v2.mapMultiply(gamma2))
         }
         vd.bias += msg.get.bias*gamma1
@@ -122,23 +127,19 @@ object Svdpp {
     }
 
     for (i <- 0 until maxIters) {
-      // phase 1
+      // phase 1, calculate v2 for user nodes
       val t1: VertexRDD[RealVector] = g.mapReduceTriplets(mapF1, reduceF1)
-      g.outerJoinVertices(t1) {updateF1}  
-      // phase 2
+      g.outerJoinVertices(t1) {updateF1}
+      // phase 2, update p for user nodes and q, y for item nodes
       val t2: VertexRDD[Msg] = g.mapReduceTriplets(mapF2, reduceF2)
       g.outerJoinVertices(t2) {updateF2}
     }
-	
+
     // calculate error on training set
     def mapF3(et: EdgeTriplet[VT, Double]): Iterator[(Vid, Double)] = {
       assert(et.srcAttr != null && et.dstAttr != null)
-      val usr = et.srcAttr
-      val itm = et.dstAttr
-      var p = usr.v1
-      var q = itm.v1
-      val itmBias = 0.0
-      val usrBias = 0.0
+      val (usr, itm) = (et.srcAttr, et.dstAttr)
+      val (p, q) = (usr.v1, itm.v1)
       var pred = u + usr.bias + itm.bias + q.dotProduct(usr.v2)
       pred = math.max(pred, minVal)
       pred = math.min(pred, maxVal)
@@ -146,7 +147,7 @@ object Svdpp {
       Iterator((et.dstId, err))
     }
     def updateF3(vid: Vid, vd: VT, msg: Option[Double]) = {
-      if (msg.isDefined && vid % 2 == 1) { // item sum up the errors
+      if (msg.isDefined && vid % 2 == 1) { // item nodes sum up the errors
         vd.norm = msg.get
       }
       vd