net.myrrix.online.candidate.LocationSensitiveHashTest.java Source code

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Here is the source code for net.myrrix.online.candidate.LocationSensitiveHashTest.java

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/*
 * Copyright Myrrix Ltd
 *
 * Licensed 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
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License 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 net.myrrix.online.candidate;

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.mahout.cf.taste.impl.common.FullRunningAverage;
import org.apache.mahout.cf.taste.impl.common.RunningAverage;
import org.junit.Test;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import net.myrrix.common.MyrrixTest;
import net.myrrix.common.collection.FastByIDMap;
import net.myrrix.common.collection.FastIDSet;
import net.myrrix.common.math.SimpleVectorMath;
import net.myrrix.common.random.RandomManager;
import net.myrrix.common.random.RandomUtils;

public final class LocationSensitiveHashTest extends MyrrixTest {

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

    private static final int NUM_FEATURES = 50;
    private static final int NUM_ITEMS = 2000000;
    private static final int NUM_RECS = 10;
    private static final int ITERATIONS = 20;
    private static final double LN2 = Math.log(2.0);

    @Test
    public void testLSH() {
        System.setProperty("model.lsh.sampleRatio", "0.1");
        System.setProperty("model.lsh.numHashes", "20");
        RandomGenerator random = RandomManager.getRandom();

        RunningAverage avgPercentTopRecsConsidered = new FullRunningAverage();
        RunningAverage avgNDCG = new FullRunningAverage();
        RunningAverage avgPercentAllItemsConsidered = new FullRunningAverage();

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

            FastByIDMap<float[]> Y = new FastByIDMap<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.addDatum(percentTopRecsConsidered);
            avgNDCG.addDatum(ndcg);
            avgPercentAllItemsConsidered.addDatum(percentAllItemsConsidered);
        }

        log.info(avgPercentTopRecsConsidered.toString());
        log.info(avgNDCG.toString());
        log.info(avgPercentAllItemsConsidered.toString());

        assertTrue(avgPercentTopRecsConsidered.getAverage() > 0.55);
        assertTrue(avgNDCG.getAverage() > 0.55);
        assertTrue(avgPercentAllItemsConsidered.getAverage() < 0.075);
    }

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

        CandidateFilter lsh = new LocationSensitiveHash(Y);

        FastIDSet candidates = new FastIDSet();
        float[][] userVecs = { userVec };
        for (Iterator<FastByIDMap.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(FastByIDMap<float[]> Y, float[] userVec) {
        SortedMap<Double, Long> allScores = Maps.newTreeMap(Collections.reverseOrder());
        for (FastByIDMap.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;
    }

}