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TimeSVDplus.scala
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TimeSVDplus.scala
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package function
import function._
import scala.util.Random
import scala.math
import java.util.concurrent.TimeUnit
class TimeSVDplus extends TrainingModel {
val (gamma, lambda1, lambda2) = (0.00032, 0.05, 0.05)
//beta = 0.4 from paper
val beta = 0.32
//scale
def w(value: Double): Double = math.sqrt(value) * 10.0
val numBins = 30
//b_u
val userDeviation = Array.fill(numUsers)(Random.nextDouble)
//b_u
val userDeviationT = Array.fill(numUsers)(collection.mutable.HashMap[Int, Double]())
ratingFile.foreach{ x =>
val t = days(x.timestamp, minStamp)
userDeviationT(x.userID - 1) += (t -> 0.0)
}
//b_i
val movieDeviation = Array.fill(numMovies)(Random.nextDouble)
//b_i-bin(t)
val movieDeviationT = Array.fill(numMovies)(Array.fill(numBins)(Random.nextDouble))
//alpha_u
val alpha = Array.fill(numUsers)(Random.nextDouble)
//alpahK_u
val alphaK = Array.fill(numUsers)(Array.fill(numFactors)(Random.nextDouble))
//p_u(t)
val timePreference = Array.fill(numUsers)( Array.fill(numFactors)(collection.mutable.HashMap[Int, Double]()) )
//y_i
val feedback = Array.fill(numFactors)(Array.fill[Double](numMovies)(Random.nextGaussian() * 0.1))
val minStamp = ratingFile.reduceLeft( (a,b) => if (a.timestamp < b.timestamp) a else b).timestamp
val maxStamp = ratingFile.reduceLeft( (a,b) => if (a.timestamp > b.timestamp) a else b).timestamp
val numDays = days(maxStamp, minStamp) + 1
val times = Array.fill(numUsers)(Array.fill[Long](numMovies)(0))
//For the rating(u)(i) which we want to predict(to test), its timestamp is preserved
//另一種可能的作法是都設為現在的時間
ratingFile.foreach{ x => times(x.userID - 1)(x.movieID - 1) = x.timestamp }
val ratedMovieOfUsers = ratings.map{ x =>
val s = x.size
//取有評過分的電影,記錄其 index
for(i <- 0 until s if x(i) > 0.0)
yield i
}
val userMeanDate = Array.tabulate(ratedMovieOfUsers.size) { u =>
val sum: Double = ratedMovieOfUsers(u)
.map{ i => days(times(u)(i), minStamp)}
.foldLeft(0.0)(_+_)
if(ratedMovieOfUsers(u).size > 0)
sum / ratedMovieOfUsers(u).size
else
globalMeanDate
}
//mui
val overallAverage = {
var count = 0
var sum = 0.0
for(u <- 0 until numUsers ; i <- 0 until numMovies)
if(ratings(u)(i) > 0.0){
sum += ratings(u)(i)
count += 1
}
sum / count
}
val globalMeanDate = {
var count = 0
var sum = 0.0
for(u <- 0 until numUsers ; i <- 0 until numMovies)
if(ratings(u)(i) > 0.0){
sum += days(times(u)(i), minStamp)
count += 1
}
sum / count
}
def predict(userIndex: Int, movieIndex: Int) = {
val stamp = times(userIndex)(movieIndex)
val t = days(stamp, minStamp)
val binT = bin(t)
val bUT = if (userDeviationT(userIndex).contains(t)) userDeviationT(userIndex)(t) else 0.0
var sum = 0.0
for(k <- 0 until numFactors) {
val put = if (timePreference(userIndex)(k).contains(t))
timePreference(userIndex)(k)(t)
else
0.0
var sumY = 0.0
for(j <- ratedMovieOfUsers(userIndex))
sumY += feedback(k)(j)
//q_i * ( p_u + alpha_u * dev_u(t) + p_u(t) + SUMy_j/sqrt(R))
sum += matrixQ(k)(movieIndex) *
( matrixP(userIndex)(k) +
alphaK(userIndex)(k) * dev(userIndex, t) +
put +
sumY / w(ratedMovieOfUsers(userIndex).size)
)
}
//!! check bui-contain
//prediction = mui + b_u + alaph_u * dev_u(t) + b_ut + b_i + b_i-bin(t) + q_i * ( p_u + alpha_u * dev_u(t) + p_u(t))
overallAverage +
userDeviation(userIndex) + alpha(userIndex) * dev(userIndex, t) + bUT +
movieDeviation(movieIndex) + movieDeviationT(movieIndex)(binT) + sum
}
private def gradientDescent(): Double = {
for(u <- 0 until numUsers ; i <- 0 until numMovies){
if (ratings(u)(i) > 0){ //??
//唯有ratings(u)(i) > 0 時,stamp才不為0、t才會合理
val stamp = times(u)(i)
val t = days(stamp, minStamp)
val binT = bin(t)
val bit = movieDeviationT(i)(binT)
if(!userDeviationT(u).contains(t))
userDeviationT(u) += (t -> 0.0)
val eui = ratings(u)(i) - predict(u,i)
val but = userDeviationT(u)(t) +
gamma * (eui - lambda1 * userDeviationT(u)(t))
//update b_u
userDeviation(u) += gamma * (eui - lambda1 * userDeviation(u))
//update alpha_u
alpha(u) += gamma * (eui * dev(u, t) - lambda1 * alpha(u))
//update b_ut
userDeviationT(u) += (t -> but)
//update b_i
movieDeviation(i) += gamma * (eui - lambda1 * movieDeviation(i))
//update b_i-bin(t)
movieDeviationT(i)(binT) += gamma * (eui - lambda1 * bit)
val sumYj = feedback.map{x => x.reduceLeft(_ + _) / w(ratedMovieOfUsers(u).size)}
for(k <- 0 until numFactors){
val pu = matrixP(u)(k)
val qi = matrixQ(k)(i)
val alphaU = alphaK(u)(k)
if(!timePreference(u)(k).contains(t))
timePreference(u)(k) += (t -> 0.0)
val put = timePreference(u)(k)(t) +
gamma * (eui * qi - lambda2 * timePreference(u)(k)(t))
//update p_u
matrixP(u)(k) += gamma * ( eui * qi - lambda2 * pu)
//update q_i
matrixQ(k)(i) += gamma * ( eui * (pu + alphaU * dev(u,t) + put + sumYj(k)) - lambda2 * qi )
//update alpha_uk
alphaK(u)(k) += gamma * ( eui * qi * dev(u, t) - lambda2 * alphaU)
//update p_ku(t)
timePreference(u)(k) += (t -> put)
//update y_j
for(j <- ratedMovieOfUsers(u)) {
feedback(k)(j) += gamma * ( eui * qi / w(ratedMovieOfUsers(u).size) - lambda2 * feedback(k)(j))
feedback(k)(j) = if (feedback(k)(j) > 0.5) 1 else 0
}
}
}
}
var error = 0.0
for(u <- 0 until numUsers; i <- 0 until numMovies){
if (ratings(u)(i) > 0){ // prevent overfit
val eui = ratings(u)(i) - predict(u,i)
error += eui * eui
val stamp = times(u)(i)
val t = days(stamp, minStamp)
val binT = bin(t)
val bit = movieDeviationT(i)(binT)
val bu = userDeviation(u)
val au = alpha(u)
val but = if(userDeviationT(u).contains(t)) userDeviationT(u)(t) else 0.0
val bi = movieDeviation(i)
error += (lambda1/2.0) *
( bu * bu + au * au + but * but + bi * bi + bit * bit)
for(k <- 0 until numFactors){
val auk = alphaK(u)(k)
val pu = matrixP(u)(k)
val qi = matrixQ(k)(i)
val put = if (timePreference(u)(k).contains(t))
timePreference(u)(k)(t)
else
0.0
error += (lambda2/2.0) * ( auk * auk + pu * pu + qi * qi + put * put)
for(j <- ratedMovieOfUsers(u))
error += (lambda2/2.0) * feedback(k)(j) * feedback(k)(j)
}
}
}
error
}
//def days(d1: Long, d2: Long) = (TimeUnit.SECONDS.toDays(math.abs(d1 - d2))).toInt
def days(d1: Long, d2: Long) = (TimeUnit.MILLISECONDS.toDays(math.abs(d1 - d2))).toInt
def bin(day: Int) = (numBins.toDouble * day / numDays.toDouble ).toInt
def dev(u: Int, t: Int) = math.signum(t - userMeanDate(u)) * math.pow(math.abs(t - userMeanDate(u)), beta)
var min = math.abs(gradientDescent())
var step = 0
for(i <- 1 to steps){
val err = math.abs(gradientDescent())
println("Training step " + i + " : error = " + err)
if(err < min){
min = err
step = i
}
}
println("min error : " + min + " at step " + step)
} //end of class TimeSVDplus