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author | Jianping J Wang <jianping.j.wang@gmail.com> | 2013-12-31 16:59:05 +0800 |
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committer | Jianping J Wang <jianping.j.wang@gmail.com> | 2013-12-31 16:59:05 +0800 |
commit | 779c66ae4ee681f9cf8ab85cd48f4761ee49e031 (patch) | |
tree | bf778a314fb585b455527756942590007b02efd2 | |
parent | 600421d8bc258508d0e298131384d0abed4b87b7 (diff) | |
download | spark-779c66ae4ee681f9cf8ab85cd48f4761ee49e031.tar.gz spark-779c66ae4ee681f9cf8ab85cd48f4761ee49e031.tar.bz2 spark-779c66ae4ee681f9cf8ab85cd48f4761ee49e031.zip |
refactor and fix bugs
-rw-r--r-- | graph/src/main/scala/org/apache/spark/graph/algorithms/Svdpp.scala | 87 |
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 } |