Merge pull request #116 from jianpingjwang/master
remove unused variables and fix a bug
This commit is contained in:
Reynold Xin
commit
44e4205ac5
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@ -1,6 +1,5 @@
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package org.apache.spark.graph.algorithms
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import org.apache.spark._
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import org.apache.spark.rdd._
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import org.apache.spark.graph._
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import scala.util.Random
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@ -10,7 +9,7 @@ class VT ( // vertex type
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var v1: RealVector, // v1: p for user node, q for item node
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var v2: RealVector, // v2: pu + |N(u)|^(-0.5)*sum(y) for user node, y for item node
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var bias: Double,
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var norm: Double // only for user node
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var norm: Double // |N(u)|^(-0.5) for user node
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) extends Serializable
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class Msg ( // message
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@ -20,7 +19,15 @@ class Msg ( // message
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) extends Serializable
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object Svdpp {
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// implement SVD++ based on http://public.research.att.com/~volinsky/netflix/kdd08koren.pdf
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/**
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* Implement SVD++ based on "Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model",
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* paper is available at [[http://public.research.att.com/~volinsky/netflix/kdd08koren.pdf]].
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* The prediction rule is rui = u + bu + bi + qi*(pu + |N(u)|^(-0.5)*sum(y)), see the details on page 6.
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*
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* @param edges edges for constructing the graph
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*
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* @return a graph with vertex attributes containing the trained model
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*/
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def run(edges: RDD[Edge[Double]]): Graph[VT, Double] = {
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// defalut parameters
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@ -32,7 +39,8 @@ object Svdpp {
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val gamma2 = 0.007
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val gamma6 = 0.005
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val gamma7 = 0.015
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// generate default vertex attribute
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def defaultF(rank: Int) = {
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val v1 = new ArrayRealVector(rank)
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val v2 = new ArrayRealVector(rank)
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@ -44,7 +52,7 @@ object Svdpp {
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vd
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}
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// calculate initial norm and bias
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// calculate initial bias and norm
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def mapF0(et: EdgeTriplet[VT, Double]): Iterator[(Vid, (Long, Double))] = {
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assert(et.srcAttr != null && et.dstAttr != null)
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Iterator((et.srcId, (1L, et.attr)), (et.dstId, (1L, et.attr)))
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@ -67,10 +75,10 @@ object Svdpp {
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// make graph
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var g = Graph.fromEdges(edges, defaultF(rank)).cache()
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// calculate initial norm and bias
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// calculate initial bias and norm
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val t0 = g.mapReduceTriplets(mapF0, reduceF0)
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g.outerJoinVertices(t0) {updateF0}
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g.outerJoinVertices(t0) {updateF0}
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// phase 1
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def mapF1(et: EdgeTriplet[VT, Double]): Iterator[(Vid, RealVector)] = {
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assert(et.srcAttr != null && et.dstAttr != null)
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@ -89,21 +97,18 @@ object Svdpp {
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// phase 2
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def mapF2(et: EdgeTriplet[VT, Double]): Iterator[(Vid, Msg)] = {
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assert(et.srcAttr != null && et.dstAttr != null)
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val usr = et.srcAttr
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val itm = et.dstAttr
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var p = usr.v1
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var q = itm.v1
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val itmBias = 0.0
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val usrBias = 0.0
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val (usr, itm) = (et.srcAttr, et.dstAttr)
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val (p, q) = (usr.v1, itm.v1)
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var pred = u + usr.bias + itm.bias + q.dotProduct(usr.v2)
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pred = math.max(pred, minVal)
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pred = math.min(pred, maxVal)
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val err = et.attr - pred
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val y = (q.mapMultiply(err*usr.norm)).subtract((usr.v2).mapMultiply(gamma7))
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val newP = (q.mapMultiply(err)).subtract(p.mapMultiply(gamma7)) // for each connected item q
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val newQ = (usr.v2.mapMultiply(err)).subtract(q.mapMultiply(gamma7))
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Iterator((et.srcId, new Msg(newP, y, err - gamma6*usr.bias)), (et.dstId, new Msg(newQ, y, err - gamma6*itm.bias)))
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}
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val updateP = (q.mapMultiply(err)).subtract(p.mapMultiply(gamma7))
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val updateQ = (usr.v2.mapMultiply(err)).subtract(q.mapMultiply(gamma7))
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val updateY = (q.mapMultiply(err*usr.norm)).subtract((itm.v2).mapMultiply(gamma7))
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Iterator((et.srcId, new Msg(updateP, updateY, err - gamma6*usr.bias)),
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(et.dstId, new Msg(updateQ, updateY, err - gamma6*itm.bias)))
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}
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def reduceF2(g1: Msg, g2: Msg):Msg = {
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g1.v1 = g1.v1.add(g2.v1)
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g1.v2 = g1.v2.add(g2.v2)
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@ -113,7 +118,7 @@ object Svdpp {
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def updateF2(vid: Vid, vd: VT, msg: Option[Msg]) = {
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if (msg.isDefined) {
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vd.v1 = vd.v1.add(msg.get.v1.mapMultiply(gamma2))
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if (vid % 2 == 1) { // item node update y
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if (vid % 2 == 1) { // item nodes update y
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vd.v2 = vd.v2.add(msg.get.v2.mapMultiply(gamma2))
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}
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vd.bias += msg.get.bias*gamma1
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@ -122,23 +127,19 @@ object Svdpp {
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}
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for (i <- 0 until maxIters) {
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// phase 1
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// phase 1, calculate v2 for user nodes
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val t1: VertexRDD[RealVector] = g.mapReduceTriplets(mapF1, reduceF1)
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g.outerJoinVertices(t1) {updateF1}
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// phase 2
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g.outerJoinVertices(t1) {updateF1}
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// phase 2, update p for user nodes and q, y for item nodes
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val t2: VertexRDD[Msg] = g.mapReduceTriplets(mapF2, reduceF2)
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g.outerJoinVertices(t2) {updateF2}
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}
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// calculate error on training set
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def mapF3(et: EdgeTriplet[VT, Double]): Iterator[(Vid, Double)] = {
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assert(et.srcAttr != null && et.dstAttr != null)
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val usr = et.srcAttr
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val itm = et.dstAttr
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var p = usr.v1
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var q = itm.v1
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val itmBias = 0.0
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val usrBias = 0.0
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val (usr, itm) = (et.srcAttr, et.dstAttr)
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val (p, q) = (usr.v1, itm.v1)
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var pred = u + usr.bias + itm.bias + q.dotProduct(usr.v2)
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pred = math.max(pred, minVal)
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pred = math.min(pred, maxVal)
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@ -146,7 +147,7 @@ object Svdpp {
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Iterator((et.dstId, err))
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}
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def updateF3(vid: Vid, vd: VT, msg: Option[Double]) = {
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if (msg.isDefined && vid % 2 == 1) { // item sum up the errors
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if (msg.isDefined && vid % 2 == 1) { // item nodes sum up the errors
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vd.norm = msg.get
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}
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vd
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