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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
// scalastyle:off println
package org.apache.spark.examples
import org.apache.commons.math3.linear._
/**
* Alternating least squares matrix factorization.
*
* This is an example implementation for learning how to use Spark. For more conventional use,
* please refer to org.apache.spark.mllib.recommendation.ALS
*/
object LocalALS {
// Parameters set through command line arguments
var M = 0 // Number of movies
var U = 0 // Number of users
var F = 0 // Number of features
var ITERATIONS = 0
val LAMBDA = 0.01 // Regularization coefficient
def generateR(): RealMatrix = {
val mh = randomMatrix(M, F)
val uh = randomMatrix(U, F)
mh.multiply(uh.transpose())
}
def rmse(targetR: RealMatrix, ms: Array[RealVector], us: Array[RealVector]): Double = {
val r = new Array2DRowRealMatrix(M, U)
for (i <- 0 until M; j <- 0 until U) {
r.setEntry(i, j, ms(i).dotProduct(us(j)))
}
val diffs = r.subtract(targetR)
var sumSqs = 0.0
for (i <- 0 until M; j <- 0 until U) {
val diff = diffs.getEntry(i, j)
sumSqs += diff * diff
}
math.sqrt(sumSqs / (M.toDouble * U.toDouble))
}
def updateMovie(i: Int, m: RealVector, us: Array[RealVector], R: RealMatrix) : RealVector = {
var XtX: RealMatrix = new Array2DRowRealMatrix(F, F)
var Xty: RealVector = new ArrayRealVector(F)
// For each user that rated the movie
for (j <- 0 until U) {
val u = us(j)
// Add u * u^t to XtX
XtX = XtX.add(u.outerProduct(u))
// Add u * rating to Xty
Xty = Xty.add(u.mapMultiply(R.getEntry(i, j)))
}
// Add regularization coefficients to diagonal terms
for (d <- 0 until F) {
XtX.addToEntry(d, d, LAMBDA * U)
}
// Solve it with Cholesky
new CholeskyDecomposition(XtX).getSolver.solve(Xty)
}
def updateUser(j: Int, u: RealVector, ms: Array[RealVector], R: RealMatrix) : RealVector = {
var XtX: RealMatrix = new Array2DRowRealMatrix(F, F)
var Xty: RealVector = new ArrayRealVector(F)
// For each movie that the user rated
for (i <- 0 until M) {
val m = ms(i)
// Add m * m^t to XtX
XtX = XtX.add(m.outerProduct(m))
// Add m * rating to Xty
Xty = Xty.add(m.mapMultiply(R.getEntry(i, j)))
}
// Add regularization coefficients to diagonal terms
for (d <- 0 until F) {
XtX.addToEntry(d, d, LAMBDA * M)
}
// Solve it with Cholesky
new CholeskyDecomposition(XtX).getSolver.solve(Xty)
}
def showWarning() {
System.err.println(
"""WARN: This is a naive implementation of ALS and is given as an example!
|Please use the ALS method found in org.apache.spark.mllib.recommendation
|for more conventional use.
""".stripMargin)
}
def main(args: Array[String]) {
args match {
case Array(m, u, f, iters) => {
M = m.toInt
U = u.toInt
F = f.toInt
ITERATIONS = iters.toInt
}
case _ => {
System.err.println("Usage: LocalALS <M> <U> <F> <iters>")
System.exit(1)
}
}
showWarning()
println(s"Running with M=$M, U=$U, F=$F, iters=$ITERATIONS")
val R = generateR()
// Initialize m and u randomly
var ms = Array.fill(M)(randomVector(F))
var us = Array.fill(U)(randomVector(F))
// Iteratively update movies then users
for (iter <- 1 to ITERATIONS) {
println(s"Iteration $iter:")
ms = (0 until M).map(i => updateMovie(i, ms(i), us, R)).toArray
us = (0 until U).map(j => updateUser(j, us(j), ms, R)).toArray
println("RMSE = " + rmse(R, ms, us))
println()
}
}
private def randomVector(n: Int): RealVector =
new ArrayRealVector(Array.fill(n)(math.random))
private def randomMatrix(rows: Int, cols: Int): RealMatrix =
new Array2DRowRealMatrix(Array.fill(rows, cols)(math.random))
}
// scalastyle:on println
|
