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Facilities for evaluating recommender algorithms.
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/*
 * 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 it.unimi.dsi.fastutil.longs.LongIterator;
import it.unimi.dsi.fastutil.longs.LongList;
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.util.statistics.MeanAccumulator;
import org.grouplens.lenskit.vectors.SparseVector;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import static java.lang.Math.log;

/**
 * Evaluate a recommender's predictions with normalized discounted cumulative gain.
 *
 * This is a prediction evaluator that uses base-2 nDCG to evaluate recommender
 * accuracy. The items are ordered by predicted preference and the nDCG is
 * computed using the user's real rating as the gain for each item. Doing this
 * only over the queried items, rather than in the general recommend condition,
 * avoids penalizing recommenders for recommending items that would be better
 * if the user had known about them and provided ratings (e.g., for doing their
 * job).
 *
 * nDCG is computed per-user and then averaged over all users.
 *
 * @author GroupLens Research
 */
public class NDCGPredictMetric extends AbstractMetric {
    private static final Logger logger = LoggerFactory.getLogger(NDCGPredictMetric.class);

    public NDCGPredictMetric() {
        super(Result.class, Result.class);
    }

    @Override
    public MeanAccumulator createContext(Attributed algo, TTDataSet ds, Recommender rec) {
        return new MeanAccumulator();
    }

    /**
     * Compute the DCG of a list of items with respect to a value vector.
     */
    static double computeDCG(LongList items, SparseVector values) {
        final double lg2 = log(2);

        double gain = 0;
        int rank = 0;

        LongIterator iit = items.iterator();
        while (iit.hasNext()) {
            final long item = iit.nextLong();
            final double v = values.get(item);
            rank++;
            if (rank < 2) {
                gain += v;
            } else {
                gain += v * lg2 / log(rank);
            }
        }

        return gain;
    }

    @Override
    public Result doMeasureUser(TestUser user, MeanAccumulator context) {
        SparseVector predictions = user.getPredictions();
        if (predictions == null) {
            return null;
        }

        SparseVector ratings = user.getTestRatings();
        LongList ideal = ratings.keysByValue(true);
        LongList actual = predictions.keysByValue(true);
        double idealGain = computeDCG(ideal, ratings);
        double gain = computeDCG(actual, ratings);
        double score = gain / idealGain;
        context.add(score);
        return new Result(score);
    }

    @Override
    protected Result getTypedResults(MeanAccumulator context) {
        return new Result(context.getMean());
    }

    public static class Result {
        @ResultColumn("nDCG")
        public final double utility;

        public Result(double util) {
            utility = util;
        }
    }
}