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/* Copyright 2014 Twitter, Inc. Licensed 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. */ /** * 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 com.twitter.algebra; import java.io.IOException; import java.util.Iterator; import java.util.List; import java.util.Map; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.WritableComparable; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.util.ToolRunner; import org.apache.mahout.common.AbstractJob; import org.apache.mahout.math.RandomAccessSparseVector; import org.apache.mahout.math.Vector; import org.apache.mahout.math.VectorWritable; import org.apache.mahout.math.hadoop.DistributedRowMatrix; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import com.twitter.algebra.matrix.format.MatrixOutputFormat; import com.twitter.algebra.matrix.format.RowPartitioner; import com.twitter.algebra.nmf.NMFCommon; /** * Transpose a matrix borrowed from Mahout's {@link TransposeJob} */ public class TransposeJob extends AbstractJob { private static final Logger log = LoggerFactory.getLogger(TransposeJob.class); public static final String NUM_ORIG_ROWS_KEY = "SparseRowMatrix.numRows"; public static void main(String[] args) throws Exception { ToolRunner.run(new TransposeJob(), args); } @Override public int run(String[] strings) throws Exception { addInputOption(); addOption("numRows", "nr", "Number of rows of the input matrix"); addOption("numCols", "nc", "Number of columns of the input matrix"); Map<String, List<String>> parsedArgs = parseArguments(strings); if (parsedArgs == null) { return -1; } int numRows = Integer.parseInt(getOption("numRows")); int numCols = Integer.parseInt(getOption("numCols")); DistributedRowMatrix matrix = new DistributedRowMatrix(getInputPath(), getTempPath(), numRows, numCols); matrix.setConf(new Configuration(getConf())); transpose(matrix, getConf(), "Transpose-" + getInputPath().getName()); return 0; } /** * Perform transpose of A, where A is already wrapped in a DistributedRowMatrix * object. * * @param distM * input matrix A * @param conf * the initial configuration * @param label * the label for the output directory * @return At wrapped in a DistributedRowMatrix object * @throws IOException * @throws InterruptedException * @throws ClassNotFoundException */ public static DistributedRowMatrix transpose(DistributedRowMatrix distM, Configuration conf, String label) throws IOException, InterruptedException, ClassNotFoundException { Path outputPath = new Path(distM.getOutputTempPath(), label); FileSystem fs = FileSystem.get(outputPath.toUri(), conf); TransposeJob job = new TransposeJob(); if (!fs.exists(outputPath)) { job.run(conf, distM.getRowPath(), outputPath, distM.numRows(), distM.numCols()); } else { log.warn("----------- Skip already exists: " + outputPath); } DistributedRowMatrix m = new DistributedRowMatrix(outputPath, distM.getOutputTempPath(), distM.numCols(), distM.numRows()); m.setConf(conf); return m; } /** * Perform transpose of A, where A refers to the path that contains a matrix * in {@link SequenceFileInputFormat}. * * @param conf * the initial configuration * @param matrixInputPath * the path to the input files that we process * @param matrixOutputPath * the path of the resulting transpose matrix * @param numInputRows * rows * @param numInputCols * cols * @return the running job * @throws IOException * @throws InterruptedException * @throws ClassNotFoundException */ public void run(Configuration conf, Path matrixInputPath, Path matrixOutputPath, int numInputRows, int numInputCols) throws IOException, InterruptedException, ClassNotFoundException { conf.setInt(NUM_ORIG_ROWS_KEY, numInputRows); conf.setInt(RowPartitioner.TOTAL_KEYS, numInputCols); FileSystem fs = FileSystem.get(matrixInputPath.toUri(), conf); NMFCommon.setNumberOfMapSlots(conf, fs, matrixInputPath, "transpose"); @SuppressWarnings("deprecation") Job job = new Job(conf); job.setJarByClass(TransposeJob.class); job.setJobName(TransposeJob.class.getSimpleName()); matrixInputPath = fs.makeQualified(matrixInputPath); matrixOutputPath = fs.makeQualified(matrixOutputPath); FileInputFormat.addInputPath(job, matrixInputPath); job.setInputFormatClass(SequenceFileInputFormat.class); FileOutputFormat.setOutputPath(job, matrixOutputPath); job.setMapperClass(TransposeMapper.class); job.setMapOutputKeyClass(IntWritable.class); job.setMapOutputValueClass(VectorWritable.class); int numReducers = NMFCommon.getNumberOfReduceSlots(conf, "transpose"); job.setNumReduceTasks(numReducers); // job.setPartitionerClass(RowPartitioner.IntRowPartitioner.class); RowPartitioner.setPartitioner(job, RowPartitioner.IntRowPartitioner.class, numInputCols); job.setCombinerClass(MergeVectorsCombiner.class); job.setReducerClass(MergeVectorsReducer.class); job.setOutputFormatClass(MatrixOutputFormat.class); job.setOutputKeyClass(IntWritable.class); job.setOutputValueClass(VectorWritable.class); job.submit(); boolean res = job.waitForCompletion(true); if (!res) throw new IOException("Job failed!"); } public static class TransposeMapper extends Mapper<IntWritable, VectorWritable, IntWritable, VectorWritable> { private int newNumCols; @Override public void setup(Context context) throws IOException { Configuration conf = context.getConfiguration(); newNumCols = conf.getInt(NUM_ORIG_ROWS_KEY, Integer.MAX_VALUE); } @Override public void map(IntWritable r, VectorWritable v, Context context) throws IOException, InterruptedException { int row = r.get(); Iterator<Vector.Element> it = v.get().nonZeroes().iterator(); while (it.hasNext()) { Vector.Element e = it.next(); RandomAccessSparseVector tmp = new RandomAccessSparseVector(newNumCols, 1); tmp.setQuick(row, e.get()); r.set(e.index()); context.write(r, new VectorWritable(tmp)); } } } public static class MergeVectorsCombiner extends Reducer<WritableComparable<?>, VectorWritable, WritableComparable<?>, VectorWritable> { @Override public void reduce(WritableComparable<?> key, Iterable<VectorWritable> vectors, Context context) throws IOException, InterruptedException { context.write(key, VectorWritable.merge(vectors.iterator())); } } }