/*
* LensKit, an open source recommender systems toolkit.
* Copyright 2010-2014 LensKit Contributors. See CONTRIBUTORS.md.
* Work on LensKit has been funded by the National Science Foundation under
* grants IIS 05-34939, 08-08692, 08-12148, and 10-17697.
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as
* published by the Free Software Foundation; either version 2.1 of the
* License, or (at your option) any later version.
*
* This program 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
* this program; if not, write to the Free Software Foundation, Inc., 51
* Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
*/
package org.grouplens.lenskit.eval.metrics.predict;
import org.grouplens.lenskit.Recommender;
import org.grouplens.lenskit.eval.Attributed;
import org.grouplens.lenskit.eval.data.traintest.TTDataSet;
import org.grouplens.lenskit.eval.metrics.AbstractMetric;
import org.grouplens.lenskit.eval.metrics.ResultColumn;
import org.grouplens.lenskit.eval.traintest.TestUser;
import org.grouplens.lenskit.vectors.SparseVector;
import org.grouplens.lenskit.vectors.VectorEntry;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import static java.lang.Math.abs;
/**
* Evaluate a recommender's predictions by Mean Absolute Error. In general, prefer
* RMSE ({@link RMSEPredictMetric}) to MAE.
*
* <p>This evaluator computes two variants of MAE. The first is <em>by-rating</em>,
* where the absolute error is averaged over all predictions. The second is
* <em>by-user</em>, where the MAE is computed per-user and then averaged
* over all users.
*
* @author <a href="http://www.grouplens.org">GroupLens Research</a>
*/
public class MAEPredictMetric extends AbstractMetric<MAEPredictMetric.Context, MAEPredictMetric.AggregateResult, MAEPredictMetric.UserResult> {
private static final Logger logger = LoggerFactory.getLogger(MAEPredictMetric.class);
public MAEPredictMetric() {
super(AggregateResult.class, UserResult.class);
}
@Override
public Context createContext(Attributed algo, TTDataSet ds, Recommender rec) {
return new Context();
}
@Override
public UserResult doMeasureUser(TestUser user, Context context) {
SparseVector ratings = user.getTestRatings();
SparseVector predictions = user.getPredictions();
if (predictions == null) {
return null;
}
double err = 0;
int n = 0;
for (VectorEntry e : predictions) {
if (Double.isNaN(e.getValue())) {
continue;
}
err += abs(e.getValue() - ratings.get(e.getKey()));
n++;
}
if (n > 0) {
double mae = err / n;
context.addUser(n, err, mae);
return new UserResult(mae);
} else {
return null;
}
}
@Override
protected AggregateResult getTypedResults(Context context) {
return context.finish();
}
public static class UserResult {
@ResultColumn("MAE")
public final double mae;
public UserResult(double err) {
mae = err;
}
}
public static class AggregateResult {
@ResultColumn("MAE.ByUser")
public final double userMAE;
@ResultColumn("MAE.ByRating")
public final double globalMAE;
public AggregateResult(double umae, double gmae) {
userMAE = umae;
globalMAE = gmae;
}
}
public class Context {
private double totalError = 0;
private double totalUserError = 0;
private int nratings = 0;
private int nusers = 0;
public void addUser(int nr, double sae, double mae) {
totalError += sae;
totalUserError += mae;
nratings += nr;
nusers += 1;
}
public AggregateResult finish() {
if (nratings > 0) {
double v = totalError / nratings;
double uv = totalUserError / nusers;
logger.info("MAE: {}", v);
return new AggregateResult(uv, v);
} else {
return null;
}
}
}
}