refactor and fix bugs

1 files changed, 29 insertions, 58 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 cbbe240c90..7c3e0c83c9 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
@@ -5,18 +5,6 @@ import org.apache.spark.graph._
 import scala.util.Random
 import org.apache.commons.math.linear._
 
-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 // |N(u)|^(-0.5) for user node
-  ) extends Serializable
-
-class Msg( // message
-  var v1: RealVector,
-  var v2: RealVector,
-  var bias: Double) extends Serializable
-
 class SvdppConf( // Svdpp parameters
   var rank: Int,
   var maxIters: Int,
@@ -40,92 +28,75 @@ object Svdpp {
    * @return a graph with vertex attributes containing the trained model
    */
 
-  def run(edges: RDD[Edge[Double]], conf: SvdppConf): Graph[VT, Double] = {
+  def run(edges: RDD[Edge[Double]], conf: SvdppConf): Graph[(RealVector, RealVector, Double, Double), Double] = {
 
     // generate default vertex attribute
-    def defaultF(rank: Int) = {
+    def defaultF(rank: Int): (RealVector, RealVector, Double, Double) = {
       val v1 = new ArrayRealVector(rank)
       val v2 = new ArrayRealVector(rank)
       for (i <- 0 until rank) {
         v1.setEntry(i, Random.nextDouble)
         v2.setEntry(i, Random.nextDouble)
       }
-      var vd = new VT(v1, v2, 0.0, 0.0)
-      vd
+      (v1, v2, 0.0, 0.0)
     }
 
     // calculate global rating mean
     val (rs, rc) = edges.map(e => (e.attr, 1L)).reduce((a, b) => (a._1 + b._1, a._2 + b._2))
-    val u = rs / rc // global rating mean
+    val u = rs / rc
 
     // construct graph
     var g = Graph.fromEdges(edges, defaultF(conf.rank)).cache()
 
     // calculate initial bias and norm
-    var t0: VertexRDD[(Long, Double)] = g.mapReduceTriplets(et =>
-      Iterator((et.srcId, (1L, et.attr)), (et.dstId, (1L, et.attr))),
-      (g1: (Long, Double), g2: (Long, Double)) =>
-      (g1._1 + g2._1, g1._2 + g2._2))
-    g = g.outerJoinVertices(t0) {
-      (vid: Vid, vd: VT, msg: Option[(Long, Double)]) =>
-        vd.bias = msg.get._2 / msg.get._1; vd.norm = 1.0 / scala.math.sqrt(msg.get._1)
-        vd
+    var t0 = g.mapReduceTriplets(et =>
+      Iterator((et.srcId, (1L, et.attr)), (et.dstId, (1L, et.attr))), (g1: (Long, Double), g2: (Long, Double)) => (g1._1 + g2._1, g1._2 + g2._2))
+    g = g.outerJoinVertices(t0) { (vid: Vid, vd: (RealVector, RealVector, Double, Double), msg: Option[(Long, Double)]) =>
+      (vd._1, vd._2, msg.get._2 / msg.get._1, 1.0 / scala.math.sqrt(msg.get._1))
     }
 
-    def mapTrainF(conf: SvdppConf, u: Double)(et: EdgeTriplet[VT, Double]): Iterator[(Vid, Msg)] = {
-      assert(et.srcAttr != null && et.dstAttr != null)
+    def mapTrainF(conf: SvdppConf, u: Double)(et: EdgeTriplet[(RealVector, RealVector, Double, Double), Double])
+      : Iterator[(Vid, (RealVector, RealVector, Double))] = {
       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)
+      val (p, q) = (usr._1, itm._1)
+      var pred = u + usr._3 + itm._3 + q.dotProduct(usr._2)
       pred = math.max(pred, conf.minVal)
       pred = math.min(pred, conf.maxVal)
       val err = et.attr - pred
       val updateP = ((q.mapMultiply(err)).subtract(p.mapMultiply(conf.gamma7))).mapMultiply(conf.gamma2)
-      val updateQ = ((usr.v2.mapMultiply(err)).subtract(q.mapMultiply(conf.gamma7))).mapMultiply(conf.gamma2)
-      val updateY = ((q.mapMultiply(err * usr.norm)).subtract((itm.v2).mapMultiply(conf.gamma7))).mapMultiply(conf.gamma2)
-      Iterator((et.srcId, new Msg(updateP, updateY, (err - conf.gamma6 * usr.bias) * conf.gamma1)),
-        (et.dstId, new Msg(updateQ, updateY, (err - conf.gamma6 * itm.bias) * conf.gamma1)))
+      val updateQ = ((usr._2.mapMultiply(err)).subtract(q.mapMultiply(conf.gamma7))).mapMultiply(conf.gamma2)
+      val updateY = ((q.mapMultiply(err * usr._4)).subtract((itm._2).mapMultiply(conf.gamma7))).mapMultiply(conf.gamma2)
+      Iterator((et.srcId, (updateP, updateY, (err - conf.gamma6 * usr._3) * conf.gamma1)),
+        (et.dstId, (updateQ, updateY, (err - conf.gamma6 * itm._3) * conf.gamma1)))
     }
 
     for (i <- 0 until conf.maxIters) {
       // phase 1, calculate v2 for user nodes
-      var t1 = g.mapReduceTriplets(et =>
-        Iterator((et.srcId, et.dstAttr.v2)),
-        (g1: RealVector, g2: RealVector) => g1.add(g2))
-      g = g.outerJoinVertices(t1) { (vid: Vid, vd: VT, msg: Option[RealVector]) =>
-        if (msg.isDefined) vd.v2 = vd.v1.add(msg.get.mapMultiply(vd.norm))
-        vd
+      var t1 = g.mapReduceTriplets(et => Iterator((et.srcId, et.dstAttr._2)), (g1: RealVector, g2: RealVector) => g1.add(g2))
+      g = g.outerJoinVertices(t1) { (vid: Vid, vd: (RealVector, RealVector, Double, Double), msg: Option[RealVector]) =>
+        if (msg.isDefined) (vd._1, vd._1.add(msg.get.mapMultiply(vd._4)), vd._3, vd._4) else vd
       }
       // phase 2, update p for user nodes and q, y for item nodes
-      val t2: VertexRDD[Msg] = g.mapReduceTriplets(mapTrainF(conf, u), (g1: Msg, g2: Msg) => {
-        g1.v1 = g1.v1.add(g2.v1)
-        g1.v2 = g1.v2.add(g2.v2)
-        g1.bias += g2.bias
-        g1
-      })
-      g = g.outerJoinVertices(t2) { (vid: Vid, vd: VT, msg: Option[Msg]) =>
-        vd.v1 = vd.v1.add(msg.get.v1)
-        if (vid % 2 == 1) vd.v2 = vd.v2.add(msg.get.v2)
-        vd.bias += msg.get.bias
-        vd
+      val t2 = g.mapReduceTriplets(mapTrainF(conf, u), (g1: (RealVector, RealVector, Double), g2: (RealVector, RealVector, Double)) =>
+        (g1._1.add(g2._1), g1._2.add(g2._2), g1._3 + g2._3))
+      g = g.outerJoinVertices(t2) { (vid: Vid, vd: (RealVector, RealVector, Double, Double), msg: Option[(RealVector, RealVector, Double)]) =>
+        (vd._1.add(msg.get._1), vd._2.add(msg.get._2), vd._3 + msg.get._3, vd._4)
       }
     }
 
     // calculate error on training set
-    def mapTestF(conf: SvdppConf, u: Double)(et: EdgeTriplet[VT, Double]): Iterator[(Vid, Double)] = {
-      assert(et.srcAttr != null && et.dstAttr != null)
+    def mapTestF(conf: SvdppConf, u: Double)(et: EdgeTriplet[(RealVector, RealVector, Double, Double), Double]): Iterator[(Vid, Double)] = {
       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)
+      val (p, q) = (usr._1, itm._1)
+      var pred = u + usr._3 + itm._3 + q.dotProduct(usr._2)
       pred = math.max(pred, conf.minVal)
       pred = math.min(pred, conf.maxVal)
       val err = (et.attr - pred) * (et.attr - pred)
       Iterator((et.dstId, err))
     }
-    val t3: VertexRDD[Double] = g.mapReduceTriplets(mapTestF(conf, u), _ + _)
-    g.outerJoinVertices(t3) { (vid: Vid, vd: VT, msg: Option[Double]) =>
-      if (msg.isDefined && vid % 2 == 1) vd.norm = msg.get // item nodes sum up the errors
-      vd
+    val t3 = g.mapReduceTriplets(mapTestF(conf, u), (g1: Double, g2: Double) => g1 + g2)
+    g.outerJoinVertices(t3) { (vid: Vid, vd: (RealVector, RealVector, Double, Double), msg: Option[Double]) =>
+      if (msg.isDefined && vid % 2 == 1) (vd._1, vd._2, vd._3, msg.get) else vd
     }
     g
   }