com.cloudera.oryx.ml.speed.als.ALSSpeedModel.java Source code

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Here is the source code for com.cloudera.oryx.ml.speed.als.ALSSpeedModel.java

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/*
 * Copyright (c) 2014, 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.ml.speed.als;

import java.util.Collection;
import java.util.HashSet;
import java.util.concurrent.locks.Lock;
import java.util.concurrent.locks.ReadWriteLock;
import java.util.concurrent.locks.ReentrantReadWriteLock;

import com.google.common.base.Preconditions;
import net.openhft.koloboke.collect.map.ObjObjMap;
import net.openhft.koloboke.collect.map.hash.HashObjObjMaps;
import org.apache.commons.math3.linear.RealMatrix;

import com.cloudera.oryx.common.collection.AndPredicate;
import com.cloudera.oryx.common.collection.KeyOnlyBiPredicate;
import com.cloudera.oryx.common.collection.NotContainsPredicate;
import com.cloudera.oryx.common.math.LinearSystemSolver;
import com.cloudera.oryx.common.math.Solver;
import com.cloudera.oryx.common.math.VectorMath;

/**
 * Contains all data structures needed to create near-real-time updates for an
 * ALS-based recommender.
 */
public final class ALSSpeedModel {

    /** User-feature matrix, where row is keyed by user ID string and row is a dense float array. */
    private final ObjObjMap<String, float[]> X;
    /** Item-feature matrix, where row is keyed by item ID string and row is a dense float array. */
    private final ObjObjMap<String, float[]> Y;
    /** Remembers user IDs added since last model. */
    private final Collection<String> recentNewUsers;
    /** Remembers item IDs added since last model. Partitioned like Y. */
    private final Collection<String> recentNewItems;
    /** Controls access to X, and recentNewUsers. */
    private final ReadWriteLock xLock;
    /** Controls access to Y, and recentNewItems. */
    private final ReadWriteLock yLock;
    /** Whether model uses implicit feedback. */
    private final int features;

    /**
     * Creates an empty model.
     *
     * @param features number of features expected for user/item feature vectors
     */
    ALSSpeedModel(int features) {
        Preconditions.checkArgument(features > 0);
        X = HashObjObjMaps.newMutableMap();
        Y = HashObjObjMaps.newMutableMap();
        recentNewUsers = new HashSet<>();
        recentNewItems = new HashSet<>();
        xLock = new ReentrantReadWriteLock();
        yLock = new ReentrantReadWriteLock();
        this.features = features;
    }

    public int getFeatures() {
        return features;
    }

    public float[] getUserVector(String user) {
        Lock lock = xLock.readLock();
        lock.lock();
        try {
            return X.get(user);
        } finally {
            lock.unlock();
        }
    }

    public float[] getItemVector(String item) {
        Lock lock = yLock.readLock();
        lock.lock();
        try {
            return Y.get(item);
        } finally {
            lock.unlock();
        }
    }

    public void setUserVector(String user, float[] vector) {
        Preconditions.checkNotNull(vector);
        Preconditions.checkArgument(vector.length == features);
        Lock lock = xLock.writeLock();
        lock.lock();
        try {
            if (X.put(user, vector) == null) {
                // User was actually new
                recentNewUsers.add(user);
            }
        } finally {
            lock.unlock();
        }
    }

    public void setItemVector(String item, float[] vector) {
        Preconditions.checkNotNull(vector);
        Preconditions.checkArgument(vector.length == features);
        Lock lock = yLock.writeLock();
        lock.lock();
        try {
            if (Y.put(item, vector) == null) {
                // Item was actually new
                recentNewItems.add(item);
            }
        } finally {
            lock.unlock();
        }
    }

    public void pruneX(Collection<String> users) {
        // Keep all users in the new model, or, that have been added since last model
        Lock lock = xLock.writeLock();
        lock.lock();
        try {
            X.removeIf(new KeyOnlyBiPredicate<>(new AndPredicate<>(new NotContainsPredicate<>(users),
                    new NotContainsPredicate<>(recentNewUsers))));
            recentNewUsers.clear();
        } finally {
            lock.unlock();
        }
    }

    public void pruneY(Collection<String> items) {
        // Keep all items in the new model, or, that have been added since last model
        Lock lock = yLock.writeLock();
        lock.lock();
        try {
            Y.removeIf(new KeyOnlyBiPredicate<>(new AndPredicate<>(new NotContainsPredicate<>(items),
                    new NotContainsPredicate<>(recentNewItems))));
            recentNewItems.clear();
        } finally {
            lock.unlock();
        }
    }

    public Solver getXTXSolver() {
        RealMatrix XTX;
        Lock lock = xLock.readLock();
        lock.lock();
        try {
            XTX = VectorMath.transposeTimesSelf(X.values());
        } finally {
            lock.unlock();
        }
        return new LinearSystemSolver().getSolver(XTX);
    }

    public Solver getYTYSolver() {
        RealMatrix YTY;
        Lock lock = yLock.readLock();
        lock.lock();
        try {
            YTY = VectorMath.transposeTimesSelf(Y.values());
        } finally {
            lock.unlock();
        }
        return new LinearSystemSolver().getSolver(YTY);
    }

    @Override
    public String toString() {
        return "ALSSpeedModel[features:" + features + ", X:(" + X.size() + " users), Y:(" + Y.size() + " items)]";
    }

}