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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.math.hadoop.decomposer;
import java.io.IOException;
import java.util.List;
import java.util.Map;
import com.google.common.io.Closeables;
import org.apache.hadoop.conf.Configurable;
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.SequenceFile;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
import org.apache.mahout.common.AbstractJob;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.NamedVector;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorIterable;
import org.apache.mahout.math.VectorWritable;
import org.apache.mahout.math.decomposer.lanczos.LanczosSolver;
import org.apache.mahout.math.decomposer.lanczos.LanczosState;
import org.apache.mahout.math.hadoop.DistributedRowMatrix;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
* See the SSVD code for a better option than using this:
*
* http://mahout.apache.org/users/dim-reduction/ssvd.html
* @see org.apache.mahout.math.hadoop.stochasticsvd.SSVDSolver
*/
@Deprecated
public class DistributedLanczosSolver extends LanczosSolver implements Tool {
public static final String RAW_EIGENVECTORS = "rawEigenvectors";
private static final Logger log = LoggerFactory.getLogger(DistributedLanczosSolver.class);
private Configuration conf;
private Map> parsedArgs;
/**
* For the distributed case, the best guess at a useful initialization state for Lanczos we'll chose to be
* uniform over all input dimensions, L_2 normalized.
*/
public static Vector getInitialVector(VectorIterable corpus) {
Vector initialVector = new DenseVector(corpus.numCols());
initialVector.assign(1.0 / Math.sqrt(corpus.numCols()));
return initialVector;
}
public LanczosState runJob(Configuration originalConfig,
LanczosState state,
int desiredRank,
boolean isSymmetric,
String outputEigenVectorPathString) throws IOException {
((Configurable) state.getCorpus()).setConf(new Configuration(originalConfig));
setConf(originalConfig);
solve(state, desiredRank, isSymmetric);
serializeOutput(state, new Path(outputEigenVectorPathString));
return state;
}
/**
* Factored-out LanczosSolver for the purpose of invoking it programmatically
*/
public LanczosState runJob(Configuration originalConfig,
Path inputPath,
Path outputTmpPath,
int numRows,
int numCols,
boolean isSymmetric,
int desiredRank,
String outputEigenVectorPathString) throws IOException {
DistributedRowMatrix matrix = new DistributedRowMatrix(inputPath, outputTmpPath, numRows, numCols);
matrix.setConf(new Configuration(originalConfig));
LanczosState state = new LanczosState(matrix, desiredRank, getInitialVector(matrix));
return runJob(originalConfig, state, desiredRank, isSymmetric, outputEigenVectorPathString);
}
@Override
public int run(String[] strings) throws Exception {
Path inputPath = new Path(AbstractJob.getOption(parsedArgs, "--input"));
Path outputPath = new Path(AbstractJob.getOption(parsedArgs, "--output"));
Path outputTmpPath = new Path(AbstractJob.getOption(parsedArgs, "--tempDir"));
Path workingDirPath = AbstractJob.getOption(parsedArgs, "--workingDir") != null
? new Path(AbstractJob.getOption(parsedArgs, "--workingDir")) : null;
int numRows = Integer.parseInt(AbstractJob.getOption(parsedArgs, "--numRows"));
int numCols = Integer.parseInt(AbstractJob.getOption(parsedArgs, "--numCols"));
boolean isSymmetric = Boolean.parseBoolean(AbstractJob.getOption(parsedArgs, "--symmetric"));
int desiredRank = Integer.parseInt(AbstractJob.getOption(parsedArgs, "--rank"));
boolean cleansvd = Boolean.parseBoolean(AbstractJob.getOption(parsedArgs, "--cleansvd"));
if (cleansvd) {
double maxError = Double.parseDouble(AbstractJob.getOption(parsedArgs, "--maxError"));
double minEigenvalue = Double.parseDouble(AbstractJob.getOption(parsedArgs, "--minEigenvalue"));
boolean inMemory = Boolean.parseBoolean(AbstractJob.getOption(parsedArgs, "--inMemory"));
return run(inputPath,
outputPath,
outputTmpPath,
workingDirPath,
numRows,
numCols,
isSymmetric,
desiredRank,
maxError,
minEigenvalue,
inMemory);
}
return run(inputPath, outputPath, outputTmpPath, workingDirPath, numRows, numCols, isSymmetric, desiredRank);
}
/**
* Run the solver to produce raw eigenvectors, then run the EigenVerificationJob to clean them
*
* @param inputPath the Path to the input corpus
* @param outputPath the Path to the output
* @param outputTmpPath a Path to a temporary working directory
* @param numRows the int number of rows
* @param numCols the int number of columns
* @param isSymmetric true if the input matrix is symmetric
* @param desiredRank the int desired rank of eigenvectors to produce
* @param maxError the maximum allowable error
* @param minEigenvalue the minimum usable eigenvalue
* @param inMemory true if the verification can be done in memory
* @return an int indicating success (0) or otherwise
*/
public int run(Path inputPath,
Path outputPath,
Path outputTmpPath,
Path workingDirPath,
int numRows,
int numCols,
boolean isSymmetric,
int desiredRank,
double maxError,
double minEigenvalue,
boolean inMemory) throws Exception {
int result = run(inputPath, outputPath, outputTmpPath, workingDirPath, numRows, numCols,
isSymmetric, desiredRank);
if (result != 0) {
return result;
}
Path rawEigenVectorPath = new Path(outputPath, RAW_EIGENVECTORS);
return new EigenVerificationJob().run(inputPath,
rawEigenVectorPath,
outputPath,
outputTmpPath,
maxError,
minEigenvalue,
inMemory,
getConf() != null ? new Configuration(getConf()) : new Configuration());
}
/**
* Run the solver to produce the raw eigenvectors
*
* @param inputPath the Path to the input corpus
* @param outputPath the Path to the output
* @param outputTmpPath a Path to a temporary working directory
* @param numRows the int number of rows
* @param numCols the int number of columns
* @param isSymmetric true if the input matrix is symmetric
* @param desiredRank the int desired rank of eigenvectors to produce
* @return an int indicating success (0) or otherwise
*/
public int run(Path inputPath,
Path outputPath,
Path outputTmpPath,
Path workingDirPath,
int numRows,
int numCols,
boolean isSymmetric,
int desiredRank) throws Exception {
DistributedRowMatrix matrix = new DistributedRowMatrix(inputPath, outputTmpPath, numRows, numCols);
matrix.setConf(new Configuration(getConf() != null ? getConf() : new Configuration()));
LanczosState state;
if (workingDirPath == null) {
state = new LanczosState(matrix, desiredRank, getInitialVector(matrix));
} else {
HdfsBackedLanczosState hState =
new HdfsBackedLanczosState(matrix, desiredRank, getInitialVector(matrix), workingDirPath);
hState.setConf(matrix.getConf());
state = hState;
}
solve(state, desiredRank, isSymmetric);
Path outputEigenVectorPath = new Path(outputPath, RAW_EIGENVECTORS);
serializeOutput(state, outputEigenVectorPath);
return 0;
}
/**
* @param state The final LanczosState to be serialized
* @param outputPath The path (relative to the current Configuration's FileSystem) to save the output to.
*/
public void serializeOutput(LanczosState state, Path outputPath) throws IOException {
int numEigenVectors = state.getIterationNumber();
log.info("Persisting {} eigenVectors and eigenValues to: {}", numEigenVectors, outputPath);
Configuration conf = getConf() != null ? getConf() : new Configuration();
FileSystem fs = FileSystem.get(outputPath.toUri(), conf);
SequenceFile.Writer seqWriter =
new SequenceFile.Writer(fs, conf, outputPath, IntWritable.class, VectorWritable.class);
try {
IntWritable iw = new IntWritable();
for (int i = 0; i < numEigenVectors; i++) {
// Persist eigenvectors sorted by eigenvalues in descending order\
NamedVector v = new NamedVector(state.getRightSingularVector(numEigenVectors - 1 - i),
"eigenVector" + i + ", eigenvalue = " + state.getSingularValue(numEigenVectors - 1 - i));
Writable vw = new VectorWritable(v);
iw.set(i);
seqWriter.append(iw, vw);
}
} finally {
Closeables.close(seqWriter, false);
}
}
@Override
public void setConf(Configuration configuration) {
conf = configuration;
}
@Override
public Configuration getConf() {
return conf;
}
public DistributedLanczosSolverJob job() {
return new DistributedLanczosSolverJob();
}
/**
* Inner subclass of AbstractJob so we get access to AbstractJob's functionality w.r.t. cmdline options, but still
* sublcass LanczosSolver.
*/
public class DistributedLanczosSolverJob extends AbstractJob {
@Override
public void setConf(Configuration conf) {
DistributedLanczosSolver.this.setConf(conf);
}
@Override
public Configuration getConf() {
return DistributedLanczosSolver.this.getConf();
}
@Override
public int run(String[] args) throws Exception {
addInputOption();
addOutputOption();
addOption("numRows", "nr", "Number of rows of the input matrix");
addOption("numCols", "nc", "Number of columns of the input matrix");
addOption("rank", "r", "Desired decomposition rank (note: only roughly 1/4 to 1/3 "
+ "of these will have the top portion of the spectrum)");
addOption("symmetric", "sym", "Is the input matrix square and symmetric?");
addOption("workingDir", "wd", "Working directory path to store Lanczos basis vectors "
+ "(to be used on restarts, and to avoid too much RAM usage)");
// options required to run cleansvd job
addOption("cleansvd", "cl", "Run the EigenVerificationJob to clean the eigenvectors after SVD", false);
addOption("maxError", "err", "Maximum acceptable error", "0.05");
addOption("minEigenvalue", "mev", "Minimum eigenvalue to keep the vector for", "0.0");
addOption("inMemory", "mem", "Buffer eigen matrix into memory (if you have enough!)", "false");
DistributedLanczosSolver.this.parsedArgs = parseArguments(args);
if (DistributedLanczosSolver.this.parsedArgs == null) {
return -1;
} else {
return DistributedLanczosSolver.this.run(args);
}
}
}
public static void main(String[] args) throws Exception {
ToolRunner.run(new DistributedLanczosSolver().job(), args);
}
}
