Update Svdpp.scala

1 files changed, 14 insertions, 13 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 a0f025d708..ef266bb551 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
@@ -6,14 +6,14 @@ import org.apache.spark.graph._
 import scala.util.Random
 import org.apache.commons.math.linear._
 
-class VD (
+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
 ) extends Serializable
 
-class Msg (
+class Msg ( // message
   var v1: RealVector,
   var v2: RealVector,
   var bias: Double
@@ -22,7 +22,7 @@ class Msg (
 object Svdpp {
   // implement SVD++ based on http://public.research.att.com/~volinsky/netflix/kdd08koren.pdf
 
-  def run(edges: RDD[Edge[Double]]): Graph[VD,Double] = {
+  def run(edges: RDD[Edge[Double]]): Graph[VT, Double] = {
     // defalut parameters
     val rank = 10
     val maxIters = 20
@@ -40,19 +40,19 @@ object Svdpp {
         v1.setEntry(i, Random.nextDouble)
         v2.setEntry(i, Random.nextDouble)
       }
-      var vd = new VD(v1, v2, 0.0, 0.0)
+      var vd = new VT(v1, v2, 0.0, 0.0)
       vd
     }
 
     // calculate initial norm and bias
-    def mapF0(et: EdgeTriplet[VD, Double]): Iterator[(Vid, (Long, Double))] = {
+    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)))
     }
     def reduceF0(g1: (Long, Double), g2: (Long, Double)) = {
       (g1._1 + g2._1, g1._2 + g2._2)
     }
-    def updateF0(vid: Vid, vd: VD, msg: Option[(Long, Double)]) = {
+    def updateF0(vid: Vid, vd: VT, msg: Option[(Long, Double)]) = {
       if (msg.isDefined) {
         vd.bias = msg.get._2 / msg.get._1
         vd.norm = 1.0 / scala.math.sqrt(msg.get._1)
@@ -65,21 +65,21 @@ object Svdpp {
     val u = rs / rc // global rating mean
 
     // make graph
-    var g = Graph.fromEdges(edges, defaultF(rank)).cache()
+    var g = Graph.fromEdges(edges, defaultF(rank), RandomVertexCut).cache()
 
     // calculate initial norm and bias
     val t0 = g.mapReduceTriplets(mapF0, reduceF0)
     g.outerJoinVertices(t0) {updateF0} 
 	
     // phase 1
-    def mapF1(et: EdgeTriplet[VD, Double]): Iterator[(Vid, RealVector)] = {
+    def mapF1(et: EdgeTriplet[VT, Double]): Iterator[(Vid, RealVector)] = {
       assert(et.srcAttr != null && et.dstAttr != null)
       Iterator((et.srcId, et.dstAttr.v2)) // sum up y of connected item nodes
     }	
     def reduceF1(g1: RealVector, g2: RealVector) = {
       g1.add(g2)
     }
-    def updateF1(vid: Vid, vd: VD, msg: Option[RealVector]) = {
+    def updateF1(vid: Vid, vd: VT, msg: Option[RealVector]) = {
       if (msg.isDefined) {
         vd.v2 = vd.v1.add(msg.get.mapMultiply(vd.norm)) // pu + |N(u)|^(-0.5)*sum(y)
       }
@@ -87,7 +87,7 @@ object Svdpp {
     }
 
     // phase 2
-    def mapF2(et: EdgeTriplet[VD, Double]): Iterator[(Vid, Msg)] = {
+    def mapF2(et: EdgeTriplet[VT, Double]): Iterator[(Vid, Msg)] = {
       assert(et.srcAttr != null && et.dstAttr != null)
       val usr = et.srcAttr
       val itm = et.dstAttr
@@ -96,6 +96,7 @@ object Svdpp {
       val itmBias = 0.0
       val usrBias = 0.0
       var pred = u + usr.bias + itm.bias + q.dotProduct(usr.v2)
+      println(pred)
       pred = math.max(pred, minVal)
       pred = math.min(pred, maxVal)
       val err = et.attr - pred
@@ -110,7 +111,7 @@ object Svdpp {
       g1.bias += g2.bias
       g1
     }
-    def updateF2(vid: Vid, vd: VD, msg: Option[Msg]) = {
+    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
@@ -131,7 +132,7 @@ object Svdpp {
     }
 	
     // calculate error on training set
-    def mapF3(et: EdgeTriplet[VD, Double]): Iterator[(Vid, Double)] = {
+    def mapF3(et: EdgeTriplet[VT, Double]): Iterator[(Vid, Double)] = {
       assert(et.srcAttr != null && et.dstAttr != null)
       val usr = et.srcAttr
       val itm = et.dstAttr
@@ -145,7 +146,7 @@ object Svdpp {
       val err = (et.attr - pred)*(et.attr - pred)
       Iterator((et.dstId, err))
     }
-    def updateF3(vid: Vid, vd: VD, msg: Option[Double]) = {
+    def updateF3(vid: Vid, vd: VT, msg: Option[Double]) = {
       if (msg.isDefined && vid % 2 == 1) { // item sum up the errors
         vd.norm = msg.get
       }