/* * 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.item; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.mapreduce.Mapper; import org.apache.lucene.util.PriorityQueue; import org.apache.mahout.cf.taste.impl.common.FastIDSet; import org.apache.mahout.math.VarIntWritable; import org.apache.mahout.math.VarLongWritable; import org.apache.mahout.math.Vector; import org.apache.mahout.math.Vector.Element; import org.apache.mahout.math.VectorWritable; import java.io.IOException; import org.slf4j.Logger; import org.slf4j.LoggerFactory; public final class UserVectorSplitterMapper extends Mapper<VarLongWritable,VectorWritable, VarIntWritable,VectorOrPrefWritable> { private static final Logger log = LoggerFactory.getLogger(UserVectorSplitterMapper.class); static final String USERS_FILE = "usersFile"; static final String MAX_PREFS_PER_USER_CONSIDERED = "maxPrefsPerUserConsidered"; static final int DEFAULT_MAX_PREFS_PER_USER_CONSIDERED = 10; private int maxPrefsPerUserConsidered; private FastIDSet usersToRecommendFor; private final VarIntWritable itemIndexWritable = new VarIntWritable(); private final VectorOrPrefWritable vectorOrPref = new VectorOrPrefWritable(); @Override protected void setup(Context context) throws IOException { Configuration jobConf = context.getConfiguration(); maxPrefsPerUserConsidered = jobConf.getInt(MAX_PREFS_PER_USER_CONSIDERED, DEFAULT_MAX_PREFS_PER_USER_CONSIDERED); IDReader idReader = new IDReader (jobConf); idReader.readIDs(); usersToRecommendFor = idReader.getUserIds(); } @Override protected void map(VarLongWritable key, VectorWritable value, Context context) throws IOException, InterruptedException { long userID = key.get(); log.info("UserID = {}", userID); if (usersToRecommendFor != null && !usersToRecommendFor.contains(userID)) { return; } Vector userVector = maybePruneUserVector(value.get()); for (Element e : userVector.nonZeroes()) { itemIndexWritable.set(e.index()); vectorOrPref.set(userID, (float) e.get()); context.write(itemIndexWritable, vectorOrPref); } } private Vector maybePruneUserVector(Vector userVector) { if (userVector.getNumNondefaultElements() <= maxPrefsPerUserConsidered) { return userVector; } float smallestLargeValue = findSmallestLargeValue(userVector); // "Blank out" small-sized prefs to reduce the amount of partial products // generated later. They're not zeroed, but NaN-ed, so they come through // and can be used to exclude these items from prefs. for (Element e : userVector.nonZeroes()) { float absValue = Math.abs((float) e.get()); if (absValue < smallestLargeValue) { e.set(Float.NaN); } } return userVector; } private float findSmallestLargeValue(Vector userVector) { PriorityQueue<Float> topPrefValues = new PriorityQueue<Float>(maxPrefsPerUserConsidered) { @Override protected boolean lessThan(Float f1, Float f2) { return f1 < f2; } }; for (Element e : userVector.nonZeroes()) { float absValue = Math.abs((float) e.get()); topPrefValues.insertWithOverflow(absValue); } return topPrefValues.top(); } }