Java tutorial
/** * 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.hf.mls.mahout.cf.taste.hadoop.preparation; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.mapreduce.Mapper; import org.apache.mahout.cf.taste.hadoop.TasteHadoopUtils; import org.apache.mahout.math.RandomAccessSparseVector; import org.apache.mahout.math.VarLongWritable; import org.apache.mahout.math.Vector; import org.apache.mahout.math.VectorWritable; import org.apache.mahout.math.hadoop.similarity.cooccurrence.Vectors; import java.io.IOException; public class ToItemVectorsMapper extends Mapper<VarLongWritable, VectorWritable, IntWritable, VectorWritable> { public static final String SAMPLE_SIZE = ToItemVectorsMapper.class + ".sampleSize"; enum Elements { USER_RATINGS_USED, USER_RATINGS_NEGLECTED } private final IntWritable itemID = new IntWritable(); private final VectorWritable itemVectorWritable = new VectorWritable(); private int sampleSize; @Override protected void setup(Context ctx) throws IOException, InterruptedException { sampleSize = ctx.getConfiguration().getInt(SAMPLE_SIZE, Integer.MAX_VALUE); } @Override protected void map(VarLongWritable rowIndex, VectorWritable vectorWritable, Context ctx) throws IOException, InterruptedException { Vector userRatings = vectorWritable.get(); int numElementsBeforeSampling = userRatings.getNumNondefaultElements(); userRatings = Vectors.maybeSample(userRatings, sampleSize); int numElementsAfterSampling = userRatings.getNumNondefaultElements(); int column = TasteHadoopUtils.idToIndex(rowIndex.get()); itemVectorWritable.setWritesLaxPrecision(true); Vector itemVector = new RandomAccessSparseVector(Integer.MAX_VALUE, 1); for (Vector.Element elem : userRatings.nonZeroes()) { itemID.set(elem.index()); itemVector.setQuick(column, elem.get()); itemVectorWritable.set(itemVector); ctx.write(itemID, itemVectorWritable); // reset vector for reuse itemVector.setQuick(elem.index(), 0.0); } ctx.getCounter(Elements.USER_RATINGS_USED).increment(numElementsAfterSampling); ctx.getCounter(Elements.USER_RATINGS_NEGLECTED) .increment(numElementsBeforeSampling - numElementsAfterSampling); } }