NearestNUserNeighborhood.java

/**
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF 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
 *
 * 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 org.codelibs.elasticsearch.taste.neighborhood;

import java.util.List;

import org.codelibs.elasticsearch.taste.common.LongPrimitiveIterator;
import org.codelibs.elasticsearch.taste.common.SamplingLongPrimitiveIterator;
import org.codelibs.elasticsearch.taste.model.DataModel;
import org.codelibs.elasticsearch.taste.recommender.SimilarUser;
import org.codelibs.elasticsearch.taste.recommender.TopItems;
import org.codelibs.elasticsearch.taste.similarity.UserSimilarity;

import com.google.common.base.Preconditions;

/**
 * <p>
 * Computes a neighborhood consisting of the nearest n users to a given user. "Nearest" is defined by the
 * given {@link UserSimilarity}.
 * </p>
 */
public final class NearestNUserNeighborhood extends AbstractUserNeighborhood {

    private final int n;

    private final double minSimilarity;

    /**
     * @param n neighborhood size; capped at the number of users in the data model
     * @throws IllegalArgumentException
     *           if {@code n < 1}, or userSimilarity or dataModel are {@code null}
     */
    public NearestNUserNeighborhood(final int n,
            final UserSimilarity userSimilarity, final DataModel dataModel) {
        this(n, Double.NEGATIVE_INFINITY, userSimilarity, dataModel, 1.0);
    }

    /**
     * @param n neighborhood size; capped at the number of users in the data model
     * @param minSimilarity minimal similarity required for neighbors
     * @throws IllegalArgumentException
     *           if {@code n < 1}, or userSimilarity or dataModel are {@code null}
     */
    public NearestNUserNeighborhood(final int n, final double minSimilarity,
            final UserSimilarity userSimilarity, final DataModel dataModel) {
        this(n, minSimilarity, userSimilarity, dataModel, 1.0);
    }

    /**
     * @param n neighborhood size; capped at the number of users in the data model
     * @param minSimilarity minimal similarity required for neighbors
     * @param samplingRate percentage of users to consider when building neighborhood -- decrease to trade quality for
     *   performance
     * @throws IllegalArgumentException
     *           if {@code n < 1} or samplingRate is NaN or not in (0,1], or userSimilarity or dataModel are
     *           {@code null}
     */
    public NearestNUserNeighborhood(final int n, final double minSimilarity,
            final UserSimilarity userSimilarity, final DataModel dataModel,
            final double samplingRate) {
        super(userSimilarity, dataModel, samplingRate);
        Preconditions.checkArgument(n >= 1, "n must be at least 1");
        final int numUsers = dataModel.getNumUsers();
        this.n = n > numUsers ? numUsers : n;
        this.minSimilarity = minSimilarity;
    }

    @Override
    public List<SimilarUser> getUserNeighborhood(final long userID) {

        final DataModel dataModel = getDataModel();
        final UserSimilarity userSimilarityImpl = getUserSimilarity();

        final TopItems.Estimator<Long> estimator = new Estimator(
                userSimilarityImpl, userID, minSimilarity);

        final LongPrimitiveIterator userIDs = SamplingLongPrimitiveIterator
                .maybeWrapIterator(dataModel.getUserIDs(), getSamplingRate());

        return TopItems.getTopUsers(n, userIDs, null, estimator);
    }

    @Override
    public String toString() {
        return "NearestNUserNeighborhood";
    }

    private static final class Estimator implements TopItems.Estimator<Long> {
        private final UserSimilarity userSimilarityImpl;

        private final long theUserID;

        private final double minSim;

        private Estimator(final UserSimilarity userSimilarityImpl,
                final long theUserID, final double minSim) {
            this.userSimilarityImpl = userSimilarityImpl;
            this.theUserID = theUserID;
            this.minSim = minSim;
        }

        @Override
        public double estimate(final Long userID) {
            if (userID == theUserID) {
                return Double.NaN;
            }
            final double sim = userSimilarityImpl.userSimilarity(theUserID,
                    userID);
            return sim >= minSim ? sim : Double.NaN;
        }
    }
}