org.lenskit.mf.svd.BiasedMFItemScorerTest.java Source code

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Here is the source code for org.lenskit.mf.svd.BiasedMFItemScorerTest.java

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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.lenskit.mf.svd;

import org.apache.commons.math3.linear.MatrixUtils;
import org.apache.commons.math3.linear.RealMatrix;
import org.lenskit.util.keys.HashKeyIndex;
import org.junit.Before;
import org.junit.Test;
import org.lenskit.api.ItemScorer;
import org.lenskit.api.Result;
import org.lenskit.basic.PrecomputedItemScorer;

import static org.hamcrest.Matchers.*;
import static org.junit.Assert.*;

public class BiasedMFItemScorerTest {
    private ItemScorer baseline;
    private MFModel model;
    private BiasedMFItemScorer scorer;

    @Before
    public void createModel() {
        baseline = PrecomputedItemScorer.newBuilder().addScore(1, 42, 3.0).addScore(1, 39, 2.5).addScore(1, 25, 4.2)
                .addScore(5, 42, 3.7).addScore(5, 39, 2.8).addScore(3, 42, 2.2).addScore(3, 39, 3.2)
                .addScore(17, 42, 2.5).build();

        RealMatrix umat = MatrixUtils.createRealMatrix(3, 2);
        umat.setRow(0, new double[] { 0.1, 0.3 });
        umat.setRow(1, new double[] { -0.2, 0.2 });
        umat.setRow(2, new double[] { 0.0, 0.15 });
        HashKeyIndex uidx = new HashKeyIndex();
        uidx.internId(1);
        uidx.internId(5);
        uidx.internId(3);

        RealMatrix imat = MatrixUtils.createRealMatrix(2, 2);
        imat.setRow(0, new double[] { 0.52, 0.29 });
        imat.setRow(1, new double[] { 0.3, -1.2 });
        HashKeyIndex iidx = new HashKeyIndex();
        iidx.internId(42);
        iidx.internId(39);

        model = new MFModel(umat, imat, uidx, iidx);

        scorer = new BiasedMFItemScorer(model, new DotProductKernel(), baseline);
    }

    @Test
    public void testGoodRecs() {
        Result score = scorer.score(1, 42);
        assertThat(score, notNullValue());
        assertThat(score.getScore(), closeTo(3.0 + 0.1 * 0.52 + 0.3 * 0.29, 1.0e-6));

        score = scorer.score(3, 39);
        assertThat(score, notNullValue());
        assertThat(score.getScore(), closeTo(3.2 - 0.15 * 1.2, 1.0e-6));
    }

    @Test
    public void testSkipUnscorable() {
        Result score = scorer.score(1, 25);
        assertThat(score, nullValue());
    }

    @Test
    public void baselineForUser() {
        Result score = scorer.score(17, 42);
        assertThat(score, nullValue());
    }
}