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author | Jianping J Wang <jianping.j.wang@gmail.com> | 2013-12-30 23:41:15 +0800 |
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committer | Jianping J Wang <jianping.j.wang@gmail.com> | 2013-12-30 23:41:15 +0800 |
commit | 29fe6bdaa29193c9dbf3a8fbd05094f3d812d4e5 (patch) | |
tree | 636689f31a3e07a55719238378722b8971b25305 /graph/src | |
parent | 44e4205ac579a9a4dfb2f6041d34caea568059ce (diff) | |
download | spark-29fe6bdaa29193c9dbf3a8fbd05094f3d812d4e5.tar.gz spark-29fe6bdaa29193c9dbf3a8fbd05094f3d812d4e5.tar.bz2 spark-29fe6bdaa29193c9dbf3a8fbd05094f3d812d4e5.zip |
refactor and bug fix
Diffstat (limited to 'graph/src')
-rw-r--r-- | graph/src/main/scala/org/apache/spark/graph/algorithms/Svdpp.scala | 155 |
1 files changed, 64 insertions, 91 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..cbbe240c90 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 +5,27 @@ import org.apache.spark.graph._ import scala.util.Random import org.apache.commons.math.linear._ -class VT ( // vertex type +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 + ) extends Serializable -class Msg ( // message +class Msg( // message var v1: RealVector, var v2: RealVector, - var bias: Double -) extends Serializable + 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 { /** @@ -24,21 +33,14 @@ 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[VT, Double] = { // generate default vertex attribute def defaultF(rank: Int) = { @@ -52,108 +54,79 @@ object Svdpp { 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 - } - // 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 - // 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: 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 } - // phase 2 - def mapF2(et: EdgeTriplet[VT, Double]): Iterator[(Vid, Msg)] = { + def mapTrainF(conf: SvdppConf, u: Double)(et: EdgeTriplet[VT, Double]): Iterator[(Vid, Msg)] = { assert(et.srcAttr != null && et.dstAttr != null) 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) + 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.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))) } - for (i <- 0 until maxIters) { + for (i <- 0 until conf.maxIters) { // phase 1, calculate v2 for user nodes - val t1: VertexRDD[RealVector] = g.mapReduceTriplets(mapF1, reduceF1) - g.outerJoinVertices(t1) {updateF1} + 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 + } // 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: 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 + } } // calculate error on training set - def mapF3(et: EdgeTriplet[VT, Double]): Iterator[(Vid, Double)] = { + def mapTestF(conf: SvdppConf, u: Double)(et: EdgeTriplet[VT, Double]): Iterator[(Vid, Double)] = { assert(et.srcAttr != null && et.dstAttr != null) 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) + 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 - } + 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: VertexRDD[Double] = g.mapReduceTriplets(mapF3, _ + _) - g.outerJoinVertices(t3) {updateF3} - g + g } } |