Merge pull request #124 from jianpingjwang/master

refactor and bug fix

2 files changed, 59 insertions, 114 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 26b999f4cf..18395bdc5f 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 +5,15 @@ 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,
+  var minVal: Double,
+  var maxVal: Double,
+  var gamma1: Double,
+  var gamma2: Double,
+  var gamma6: Double,
+  var gamma7: Double) extends Serializable
 
 object Svdpp {
   /**
@@ -24,136 +21,83 @@ object Svdpp {
    * 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 
+   * @param edges edges for constructing the graph
+   *
+   * @param conf Svdpp parameters
    *
    * @return a graph with vertex attributes containing the trained model
    */
 
-  def run(edges: RDD[Edge[Double]]): Graph[VT, Double] = {
-    // defalut parameters
-    val rank = 10
-    val maxIters = 20
-    val minVal = 0.0
-    val maxVal = 5.0
-    val gamma1 = 0.007
-    val gamma2 = 0.007
-    val gamma6 = 0.005
-    val gamma7 = 0.015
+  def run(edges: RDD[Edge[Double]], conf: SvdppConf): (Graph[(RealVector, RealVector, Double, 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
-    }
-
-    // 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)))
-    }
-    def reduceF0(g1: (Long, Double), g2: (Long, Double)) = {
-      (g1._1 + g2._1, g1._2 + g2._2)
-    }
-    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)
-      }
-      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
 
-    // make graph
-    var g = Graph.fromEdges(edges, defaultF(rank)).cache()
+    // construct graph
+    var g = Graph.fromEdges(edges, defaultF(conf.rank)).cache()
 
     // calculate initial bias and norm
-    val t0 = g.mapReduceTriplets(mapF0, reduceF0)
-    g.outerJoinVertices(t0) {updateF0}
-
-    // phase 1
-    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: VT, msg: Option[RealVector]) = {
-      if (msg.isDefined) {
-        vd.v2 = vd.v1.add(msg.get.mapMultiply(vd.norm)) // pu + |N(u)|^(-0.5)*sum(y)
-      }
-      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))
     }
 
-    // phase 2
-    def mapF2(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)
-      pred = math.max(pred, minVal)
-      pred = math.min(pred, maxVal)
+      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(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)
-      g1.bias += g2.bias
-      g1
-    }
-    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 nodes update y
-          vd.v2 = vd.v2.add(msg.get.v2.mapMultiply(gamma2))
-        }
-        vd.bias += msg.get.bias*gamma1
-      }
-      vd
+      val updateP = ((q.mapMultiply(err)).subtract(p.mapMultiply(conf.gamma7))).mapMultiply(conf.gamma2)
+      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 maxIters) {
-      // phase 1, calculate v2 for user nodes
-      val t1: VertexRDD[RealVector] = g.mapReduceTriplets(mapF1, reduceF1)
-      g.outerJoinVertices(t1) {updateF1}
+    for (i <- 0 until conf.maxIters) {
+      // phase 1, calculate pu + |N(u)|^(-0.5)*sum(y) for user nodes
+      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(mapF2, reduceF2)
-      g.outerJoinVertices(t2) {updateF2}
+      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 mapF3(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)
-      pred = math.max(pred, minVal)
-      pred = math.min(pred, maxVal)
-      val err = (et.attr - pred)*(et.attr - pred)
+      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))
     }
-    def updateF3(vid: Vid, vd: VT, msg: Option[Double]) = {
-      if (msg.isDefined && vid % 2 == 1) { // item nodes sum up the errors
-        vd.norm = msg.get
-      }
-      vd
+    val t3 = g.mapReduceTriplets(mapTestF(conf, u), (g1: Double, g2: Double) => g1 + g2)
+    g = g.outerJoinVertices(t3) { (vid: Vid, vd: (RealVector, RealVector, Double, Double), msg: Option[Double]) =>
+      if (msg.isDefined) (vd._1, vd._2, vd._3, msg.get) else vd
     }
-    val t3: VertexRDD[Double] = g.mapReduceTriplets(mapF3, _ + _)
-    g.outerJoinVertices(t3) {updateF3}
-  g
+    (g, u)
   }
 }
diff --git a/graph/src/test/scala/org/apache/spark/graph/algorithms/SvdppSuite.scala b/graph/src/test/scala/org/apache/spark/graph/algorithms/SvdppSuite.scala
index 4ea675c2dc..411dd3d336 100644
--- a/graph/src/test/scala/org/apache/spark/graph/algorithms/SvdppSuite.scala
+++ b/graph/src/test/scala/org/apache/spark/graph/algorithms/SvdppSuite.scala
@@ -13,16 +13,17 @@ class SvdppSuite extends FunSuite with LocalSparkContext {
 
   test("Test SVD++ with mean square error on training set") {
     withSpark { sc =>
-      val SvdppErr = 0.01
+      val SvdppErr = 8.0
       val edges = sc.textFile("mllib/data/als/test.data").map { line =>
         val fields = line.split(",")
         Edge(fields(0).toLong * 2, fields(1).toLong * 2 + 1, fields(2).toDouble)
       }
-      val graph = Svdpp.run(edges)
+      val conf = new SvdppConf(10, 2, 0.0, 5.0, 0.007, 0.007, 0.005, 0.015) // 2 iterations
+      var (graph, u) = Svdpp.run(edges, conf)
       val err = graph.vertices.collect.map{ case (vid, vd) =>
-        if (vid % 2 == 1) { vd.norm } else { 0.0 }
+        if (vid % 2 == 1) vd._4 else 0.0
       }.reduce(_ + _) / graph.triplets.collect.size
-      assert(err < SvdppErr)
+      assert(err <= SvdppErr)
     }
   }