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Update Svdpp.scala

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This commit is contained in:
Wang Jianping J 2013-12-18 06:39:28 +08:00
parent 772b192910
commit e94fe39d0f
1 changed files with 14 additions and 13 deletions
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27
graph/src/main/scala/org/apache/spark/graph/algorithms/Svdpp.scala
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@ -6,14 +6,14 @@ import org.apache.spark.graph._
import scala.util.Random
import org.apache.commons.math.linear._
class VD (
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 // only for user node
) extends Serializable
class Msg (
class Msg ( // message
var v1: RealVector,
var v2: RealVector,
var bias: Double
@ -22,7 +22,7 @@ class Msg (
object Svdpp {
// implement SVD++ based on http://public.research.att.com/~volinsky/netflix/kdd08koren.pdf
def run(edges: RDD[Edge[Double]]): Graph[VD,Double] = {
def run(edges: RDD[Edge[Double]]): Graph[VT, Double] = {
// defalut parameters
val rank = 10
val maxIters = 20
@ -40,19 +40,19 @@ object Svdpp {
v1.setEntry(i, Random.nextDouble)
v2.setEntry(i, Random.nextDouble)
}
var vd = new VD(v1, v2, 0.0, 0.0)
var vd = new VT(v1, v2, 0.0, 0.0)
vd
}
// calculate initial norm and bias
def mapF0(et: EdgeTriplet[VD, Double]): Iterator[(Vid, (Long, Double))] = {
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: VD, msg: Option[(Long, Double)]) = {
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)
@ -65,21 +65,21 @@ object Svdpp {
val u = rs / rc // global rating mean
// make graph
var g = Graph.fromEdges(edges, defaultF(rank)).cache()
var g = Graph.fromEdges(edges, defaultF(rank), RandomVertexCut).cache()
// calculate initial norm and bias
val t0 = g.mapReduceTriplets(mapF0, reduceF0)
g.outerJoinVertices(t0) {updateF0}
// phase 1
def mapF1(et: EdgeTriplet[VD, Double]): Iterator[(Vid, RealVector)] = {
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: VD, msg: Option[RealVector]) = {
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)
}
@ -87,7 +87,7 @@ object Svdpp {
}
// phase 2
def mapF2(et: EdgeTriplet[VD, Double]): Iterator[(Vid, Msg)] = {
def mapF2(et: EdgeTriplet[VT, Double]): Iterator[(Vid, Msg)] = {
assert(et.srcAttr != null && et.dstAttr != null)
val usr = et.srcAttr
val itm = et.dstAttr
@ -96,6 +96,7 @@ object Svdpp {
val itmBias = 0.0
val usrBias = 0.0
var pred = u + usr.bias + itm.bias + q.dotProduct(usr.v2)
println(pred)
pred = math.max(pred, minVal)
pred = math.min(pred, maxVal)
val err = et.attr - pred
@ -110,7 +111,7 @@ object Svdpp {
g1.bias += g2.bias
g1
}
def updateF2(vid: Vid, vd: VD, msg: Option[Msg]) = {
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 node update y
@ -131,7 +132,7 @@ object Svdpp {
}
// calculate error on training set
def mapF3(et: EdgeTriplet[VD, Double]): Iterator[(Vid, Double)] = {
def mapF3(et: EdgeTriplet[VT, Double]): Iterator[(Vid, Double)] = {
assert(et.srcAttr != null && et.dstAttr != null)
val usr = et.srcAttr
val itm = et.dstAttr
@ -145,7 +146,7 @@ object Svdpp {
val err = (et.attr - pred)*(et.attr - pred)
Iterator((et.dstId, err))
}
def updateF3(vid: Vid, vd: VD, msg: Option[Double]) = {
def updateF3(vid: Vid, vd: VT, msg: Option[Double]) = {
if (msg.isDefined && vid % 2 == 1) { // item sum up the errors
vd.norm = msg.get
}