com.cloudera.oryx.als.common.lsh.LocationSensitiveHashIT.java Source code

Introduction

Here is the source code for com.cloudera.oryx.als.common.lsh.LocationSensitiveHashIT.java

Source

/*
 * Copyright (c) 2013, Cloudera, Inc. All Rights Reserved.
 *
 * Cloudera, Inc. 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
 *
 * This software 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.
 */

package com.cloudera.oryx.als.common.lsh;

import java.util.Collections;
import java.util.Iterator;
import java.util.List;
import java.util.Map;
import java.util.SortedMap;

import com.google.common.collect.Lists;
import com.google.common.collect.Maps;
import org.apache.commons.math3.random.RandomGenerator;
import org.apache.commons.math3.stat.descriptive.moment.Mean;
import org.junit.Test;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import com.cloudera.oryx.common.OryxTest;
import com.cloudera.oryx.common.collection.LongObjectMap;
import com.cloudera.oryx.common.collection.LongSet;
import com.cloudera.oryx.common.math.SimpleVectorMath;
import com.cloudera.oryx.common.random.RandomManager;
import com.cloudera.oryx.common.random.RandomUtils;

/**
 * A long-runnign integration test for {@link LocationSensitiveHash}.
 *
 * @author Sean Owen
 */
public final class LocationSensitiveHashIT extends OryxTest {

    private static final Logger log = LoggerFactory.getLogger(LocationSensitiveHashIT.class);

    private static final int NUM_FEATURES = 30;
    private static final int NUM_ITEMS = 1000000;
    private static final int NUM_RECS = 10;
    private static final int ITERATIONS = 10;
    private static final double LN2 = Math.log(2.0);

    @Test
    public void testLSH() {
        RandomGenerator random = RandomManager.getRandom();

        Mean avgPercentTopRecsConsidered = new Mean();
        Mean avgNDCG = new Mean();
        Mean avgPercentAllItemsConsidered = new Mean();

        for (int iteration = 0; iteration < ITERATIONS; iteration++) {

            LongObjectMap<float[]> Y = new LongObjectMap<float[]>();
            for (int i = 0; i < NUM_ITEMS; i++) {
                Y.put(i, RandomUtils.randomUnitVector(NUM_FEATURES, random));
            }
            float[] userVec = RandomUtils.randomUnitVector(NUM_FEATURES, random);

            double[] results = doTestRandomVecs(Y, userVec);
            double percentTopRecsConsidered = results[0];
            double ndcg = results[1];
            double percentAllItemsConsidered = results[2];

            log.info("Considered {}% of all candidates, {} nDCG, got {}% recommendations correct",
                    100 * percentAllItemsConsidered, ndcg, 100 * percentTopRecsConsidered);

            avgPercentTopRecsConsidered.increment(percentTopRecsConsidered);
            avgNDCG.increment(ndcg);
            avgPercentAllItemsConsidered.increment(percentAllItemsConsidered);
        }

        log.info("{}", avgPercentTopRecsConsidered.getResult());
        log.info("{}", avgNDCG.getResult());
        log.info("{}", avgPercentAllItemsConsidered.getResult());

        assertTrue(avgPercentTopRecsConsidered.getResult() > 0.8);
        assertTrue(avgNDCG.getResult() > 0.8);
        assertTrue(avgPercentAllItemsConsidered.getResult() < 0.09);
    }

    private static double[] doTestRandomVecs(LongObjectMap<float[]> Y, float[] userVec) {

        LocationSensitiveHash lsh = new LocationSensitiveHash(Y, 0.1, 20);

        LongSet candidates = new LongSet();
        float[][] userVecs = { userVec };
        for (Iterator<LongObjectMap.MapEntry<float[]>> candidatesIterator : lsh.getCandidateIterator(userVecs)) {
            while (candidatesIterator.hasNext()) {
                candidates.add(candidatesIterator.next().getKey());
            }
        }

        List<Long> topIDs = findTopRecommendations(Y, userVec);

        double score = 0.0;
        double maxScore = 0.0;
        int intersectionSize = 0;
        for (int i = 0; i < topIDs.size(); i++) {
            double value = LN2 / Math.log(2.0 + i);
            long id = topIDs.get(i);
            if (candidates.contains(id)) {
                intersectionSize++;
                score += value;
            }
            maxScore += value;
        }

        double percentTopRecsConsidered = (double) intersectionSize / topIDs.size();
        double ndcg = maxScore == 0.0 ? 0.0 : score / maxScore;
        double percentAllItemsConsidered = (double) candidates.size() / Y.size();

        return new double[] { percentTopRecsConsidered, ndcg, percentAllItemsConsidered };
    }

    private static List<Long> findTopRecommendations(LongObjectMap<float[]> Y, float[] userVec) {
        SortedMap<Double, Long> allScores = Maps.newTreeMap(Collections.reverseOrder());
        for (LongObjectMap.MapEntry<float[]> entry : Y.entrySet()) {
            double dot = SimpleVectorMath.dot(entry.getValue(), userVec);
            allScores.put(dot, entry.getKey());
        }
        List<Long> topRecommendations = Lists.newArrayList();
        for (Map.Entry<Double, Long> entry : allScores.entrySet()) {
            topRecommendations.add(entry.getValue());
            if (topRecommendations.size() == NUM_RECS) {
                return topRecommendations;
            }
        }
        return topRecommendations;
    }

}