net.librec.recommender.cf.rating.BPoissMFRecommender Maven / Gradle / Ivy
/**
* Copyright (C) 2016 LibRec
*
* This file is part of LibRec.
* LibRec is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* LibRec is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with LibRec. If not, see
*
* @author Haidong Zhang
*/
public class BPoissMFRecommender extends MatrixFactorizationRecommender {
// The parameters of users
private GammaDenseMatrix userTheta;
private GammaDenseVector userThetaRate;
// The parameters of items
private GammaDenseMatrix itemBeta;
private GammaDenseVector itemBetaRate;
// The parameters of topics;
private Table> zTopic;
@Override
protected void setup() throws LibrecException {
super.setup();
userThetaRate = new GammaDenseVector(numFactors);
itemBetaRate = new GammaDenseVector(numFactors);
userTheta = new GammaDenseMatrix(numUsers, numFactors);
itemBeta = new GammaDenseMatrix(numItems, numFactors);
userThetaRate.shapePrior = conf.getDouble("rec.recommender.user.rateShapePrior", 0.1);
userThetaRate.ratePrior = conf.getDouble("rec.recommender.user.rateRatePrior", 0.1);
userThetaRate.init2(numFactors);
userThetaRate.computeExpectations();
itemBetaRate.shapePrior = conf.getDouble("rec.recommender.item.rateShapePrior", 0.1);
itemBetaRate.ratePrior = conf.getDouble("rec.recommender.item.rateRatePrior", 0.1);
itemBetaRate.init2(numFactors);
itemBetaRate.computeExpectations();
userTheta.shapePrior = conf.getDouble("rec.recommender.user.shapePrior", 0.1);
userTheta.ratePrior = conf.getDouble("rec.recommender.user.ratePrior", 0.1);
userTheta.init();
itemBeta.shapePrior = conf.getDouble("rec.recommender.item.shapePrior", 0.1);
itemBeta.ratePrior = conf.getDouble("rec.recommender.item.ratePrior", 0.1);
itemBeta.init();
}
protected DenseVector getPhi(DenseMatrix Theta, int indexTheta, DenseMatrix Beta, int indexBeta, int number) throws LibrecException {
//get_phi(_htheta, n, _hbeta, m, phi);
DenseVector phi = new DenseVector(number);
phi.setAll(0.0);
assert (Theta.numColumns == Beta.numColumns);
for (int i = 0; i < Theta.numColumns; i++) {
phi.add(Math.log(Beta.get(indexBeta, i)) + Theta.get(indexTheta, i));
}
phi = phi.scale(1.0 / phi.sum());
for (int i = 0; i < Theta.numColumns; i++) {
phi.set(i, Math.log(phi.get(i)));
}
return phi;
}
@Override
protected void trainModel() throws LibrecException {
DenseVector phi;
double[] loglikelihood = new double[numIterations];
for (int iter = 1; iter <= numIterations; iter++) {
for (MatrixEntry matrixEntry : trainMatrix) {
int user_id = matrixEntry.row();
int item_id = matrixEntry.column();
double rating = matrixEntry.get();
phi = getPhi(userTheta.logValue, user_id, itemBeta.value, item_id, numFactors);
if (rating > 1) {
phi.scale(rating);
}
}
}
}
@Override
protected double predict(int userIdx, int itemIdx) throws LibrecException {
return DenseMatrix.rowMult(userTheta.value, userIdx, itemBeta.value, itemIdx);
}
public class GammaDenseVector {
protected int size;
protected double shapePrior, ratePrior;
protected DenseVector shape, rate;
protected DenseVector value, logValue;
public GammaDenseVector(int _size) {
size = _size;
shape = new DenseVector(size);
rate = new DenseVector(size);
value = new DenseVector(size);
logValue = new DenseVector(size);
}
public void init() {
for (int i = 0; i < size; i++) {
shape.set(i, shapePrior + 0.01 * Randoms.uniform(0.0, 1.0));
rate.set(i, ratePrior + 0.1 * Randoms.uniform(0.0, 1.0));
}
}
public void init2(double v) {
for (int i = 0; i < size; i++) {
shape.set(i, shapePrior + 0.01 * Randoms.uniform(0.0, 1.0));
rate.set(i, ratePrior + v);
}
}
public void computeExpectations() {
double a = 0.0, b = 0.0;
for (int i = 0; i < size; i++) {
if (shape.get(i) <= 0.0) {
a = 1e-30;
}
if (rate.get(i) <= 0.0) {
b = 1e-30;
}
value.set(i, a / b);
logValue.set(i, Gamma.digamma(a) - Math.log(b));
}
}
}
public class GammaDenseMatrix {
protected int numRows, numColumns;
protected double shapePrior;
protected double ratePrior;
protected DenseMatrix shape;
protected DenseMatrix rate;
protected DenseMatrix value;
protected DenseMatrix logValue;
public GammaDenseMatrix(int _numRows, int _numColumns) {
numRows = _numRows;
numColumns = _numColumns;
shape = new DenseMatrix(numRows, numColumns);
rate = new DenseMatrix(numRows, numColumns);
value = new DenseMatrix(numRows, numColumns);
logValue = new DenseMatrix(numRows, numColumns);
}
public void init() {
for (int i = 0; i < numRows; i++) {
for (int j = 0; j < numColumns; j++) {
shape.set(i, j, shapePrior + 0.01 * Randoms.uniform(0.0, 1.0));
if (i == 0) {
rate.set(0, j, ratePrior + 0.1 * Randoms.uniform(0.0, 1.0));
} else {
rate.set(i, j, rate.get(0, j));
}
}
}
for (int i = 0; i < numRows; i++) {
for (int j = 0; j < numColumns; j++) {
value.set(i, j, shape.get(i, j) / rate.get(i, j));
logValue.set(i, j, Gamma.digamma(shape.get(i, j)) - Math.log(rate.get(i, j)));
}
}
}
public void computeExpectations() {
double a = 0.0, b = 0.0;
for (int i = 0; i < numRows; i++) {
for (int j = 0; j < numColumns; j++) {
if (shape.get(i, j) <= 0) {
a = 1e-30;
}
if (rate.get(i, j) <= 0) {
b = 1e-30;
}
value.set(i, j, a / b);
logValue.set(i, j, Gamma.digamma(a) - Math.log(b));
}
}
}
}
public class GammaDenseMatrixGR {
protected int numRows, numColumns;
protected double shapePrior;
protected double ratePrior;
protected DenseMatrix shape;
protected DenseVector rate;
protected DenseMatrix value;
protected DenseMatrix logValue;
public GammaDenseMatrixGR(int _numRows, int _numColumns) {
numRows = _numRows;
numColumns = _numColumns;
shape = new DenseMatrix(numRows, numColumns);
rate = new DenseVector(numColumns);
value = new DenseMatrix(numRows, numColumns);
logValue = new DenseMatrix(numRows, numColumns);
}
public void init() {
for (int i = 0; i < numRows; i++) {
for (int j = 0; j < numColumns; j++) {
shape.set(i, j, shapePrior + 0.01 * Randoms.uniform(0.0, 1.0));
}
}
for (int j = 0; j < numColumns; j++) {
rate.set(j, ratePrior + 0.1 * Randoms.uniform(0.0, 1.0));
}
for (int i = 0; i < numRows; i++) {
for (int j = 0; j < numColumns; j++) {
value.set(i, j, shape.get(i, j) / rate.get(j));
logValue.set(i, j, Gamma.digamma(shape.get(i, j)) - Math.log(rate.get(j)));
}
}
}
public void computeExpectations() {
double a = 0.0, b = 0.0;
for (int i = 0; i < numRows; i++) {
for (int j = 0; j < numColumns; j++) {
if (shape.get(i, j) <= 0) {
a = 1e-30;
}
if (rate.get(j) <= 0) {
b = 1e-30;
}
value.set(i, j, a / b);
logValue.set(i, j, Gamma.digamma(a) - Math.log(b));
}
}
}
}
}