org.apache.mahout.cf.taste.hadoop.RecommenderMapper.java Source code

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Here is the source code for org.apache.mahout.cf.taste.hadoop.RecommenderMapper.java

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/**
 * 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.apache.mahout.cf.taste.hadoop;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.impl.model.file.FileDataModel;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.cf.taste.recommender.Recommender;

import java.io.File;
import java.io.IOException;
import java.lang.reflect.Constructor;
import java.lang.reflect.InvocationTargetException;
import java.util.List;

/**
 * <p>The {@link Mapper} which takes as input a file of user IDs (treated as Strings, note), and for each unique user
 * ID, computes recommendations with the configured {@link Recommender}. The results are output as {@link
 * RecommendedItemsWritable}.</p>
 *
 * <p>Note that there is no corresponding {@link org.apache.hadoop.mapreduce.Reducer}; this implementation can only
 * partially take advantage of the mapreduce paradigm and only really leverages it for easy parallelization. Therefore,
 * use the {@link IdentityReducer} when running this on Hadoop.</p>
 *
 * @see RecommenderJob
 */
public final class RecommenderMapper
        extends Mapper<LongWritable, LongWritable, LongWritable, RecommendedItemsWritable> {

    static final String RECOMMENDER_CLASS_NAME = "recommenderClassName";
    static final String RECOMMENDATIONS_PER_USER = "recommendationsPerUser";
    static final String DATA_MODEL_FILE = "dataModelFile";

    private Recommender recommender;
    private int recommendationsPerUser;

    @Override
    protected void map(LongWritable key, LongWritable value, Context context)
            throws IOException, InterruptedException {
        long userID = value.get();
        List<RecommendedItem> recommendedItems;
        try {
            recommendedItems = recommender.recommend(userID, recommendationsPerUser);
        } catch (TasteException te) {
            throw new RuntimeException(te);
        }
        RecommendedItemsWritable writable = new RecommendedItemsWritable(recommendedItems);
        context.write(value, writable);
        context.getCounter(ReducerMetrics.USERS_PROCESSED).increment(1L);
        context.getCounter(ReducerMetrics.RECOMMENDATIONS_MADE).increment(recommendedItems.size());
    }

    @Override
    protected void setup(Context context) {
        Configuration jobConf = context.getConfiguration();
        String dataModelFile = jobConf.get(DATA_MODEL_FILE);
        String recommenderClassName = jobConf.get(RECOMMENDER_CLASS_NAME);
        FileDataModel fileDataModel;
        try {
            Path dataModelPath = new Path(dataModelFile);
            FileSystem fs = FileSystem.get(dataModelPath.toUri(), jobConf);
            File tempDataFile = File.createTempFile("mahout-taste-hadoop", "txt");
            tempDataFile.deleteOnExit();
            fs.copyToLocalFile(dataModelPath, new Path(tempDataFile.getAbsolutePath()));
            fileDataModel = new FileDataModel(tempDataFile);
        } catch (IOException ioe) {
            throw new RuntimeException(ioe);
        }
        try {
            Class<? extends Recommender> recommenderClass = Class.forName(recommenderClassName)
                    .asSubclass(Recommender.class);
            Constructor<? extends Recommender> constructor = recommenderClass.getConstructor(DataModel.class);
            recommender = constructor.newInstance(fileDataModel);
        } catch (NoSuchMethodException nsme) {
            throw new RuntimeException(nsme);
        } catch (ClassNotFoundException cnfe) {
            throw new RuntimeException(cnfe);
        } catch (InstantiationException ie) {
            throw new RuntimeException(ie);
        } catch (IllegalAccessException iae) {
            throw new RuntimeException(iae);
        } catch (InvocationTargetException ite) {
            throw new RuntimeException(ite.getCause());
        }
        recommendationsPerUser = Integer.parseInt(jobConf.get(RECOMMENDATIONS_PER_USER));
    }

}