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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.stochasticsvd;
import com.google.common.collect.Lists;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Writable;
import org.apache.mahout.common.IOUtils;
import org.apache.mahout.common.RandomUtils;
import org.apache.mahout.math.*;
import org.apache.mahout.math.function.Functions;
import org.apache.mahout.math.solver.EigenDecomposition;
import java.io.Closeable;
import java.io.IOException;
import java.util.Deque;
import java.util.Random;
/**
* Stochastic SVD solver (API class).
*
*
* Implementation details are in my working notes in MAHOUT-376
* (https://issues.apache.org/jira/browse/MAHOUT-376).
*
*
* As of the time of this writing, I don't have benchmarks for this method in
* comparison to other methods. However, non-hadoop differentiating
* characteristics of this method are thought to be :
*
"faster" and precision is traded off in favor of speed. However, there's
* lever in terms of "oversampling parameter" p. Higher values of p produce
* better precision but are trading off speed (and minimum RAM requirement).
* This also means that this method is almost guaranteed to be less precise than
* Lanczos unless full rank SVD decomposition is sought.
*
"more scale" -- can presumably take on larger problems than Lanczos one
* (not confirmed by benchmark at this time)
*
*
*
* Specifically in regards to this implementation, I think couple of
* other differentiating points are:
*
no need to specify input matrix height or width in command line, it is
* what it gets to be.
*
supports any Writable as DRM row keys and copies them to correspondent
* rows of U matrix;
*
can request U or V or Uσ=U* Σ0.5 or
* Vσ=V* Σ0.5 none of which would require pass
* over input A and these jobs are parallel map-only jobs.
*
*
*
* This class is central public API for SSVD solver. The use pattern is as
* follows:
*
*
*
create the solver using constructor and supplying computation parameters.
*
set optional parameters thru setter methods.
*
call {@link #run()}.
*
{@link #getUPath()} (if computed) returns the path to the directory
* containing m x k U matrix file(s).
*
{@link #getVPath()} (if computed) returns the path to the directory
* containing n x k V matrix file(s).
*
*
*/
public final class SSVDSolver {
private Vector svalues;
private boolean computeU = true;
private boolean computeV = true;
private String uPath;
private String vPath;
private String uSigmaPath;
private String uHalfSigmaPath;
private String vSigmaPath;
private String vHalfSigmaPath;
private int outerBlockHeight = 30000;
private int abtBlockHeight = 200000;
// configured stuff
private final Configuration conf;
private final Path[] inputPath;
private final Path outputPath;
private final int ablockRows;
private final int k;
private final int p;
private int q;
private final int reduceTasks;
private int minSplitSize = -1;
private boolean cUHalfSigma;
private boolean cUSigma;
private boolean cVHalfSigma;
private boolean cVSigma;
private boolean overwrite;
private boolean broadcast = true;
private Path pcaMeanPath;
// for debugging
private long omegaSeed;
/**
* create new SSVD solver. Required parameters are passed to constructor to
* ensure they are set. Optional parameters can be set using setters .
*
*
* @param conf hadoop configuration
* @param inputPath Input path (should be compatible with DistributedRowMatrix as of
* the time of this writing).
* @param outputPath Output path containing U, V and singular values vector files.
* @param ablockRows The vertical hight of a q-block (bigger value require more memory
* in mappers+ perhaps larger {@code minSplitSize} values
* @param k desired rank
* @param p SSVD oversampling parameter
* @param reduceTasks Number of reduce tasks (where applicable)
*/
public SSVDSolver(Configuration conf,
Path[] inputPath,
Path outputPath,
int ablockRows,
int k,
int p,
int reduceTasks) {
this.conf = conf;
this.inputPath = inputPath;
this.outputPath = outputPath;
this.ablockRows = ablockRows;
this.k = k;
this.p = p;
this.reduceTasks = reduceTasks;
}
public int getQ() {
return q;
}
/**
* sets q, amount of additional power iterations to increase precision
* (0..2!). Defaults to 0.
*
* @param q
*/
public void setQ(int q) {
this.q = q;
}
/**
* The setting controlling whether to compute U matrix of low rank SSVD.
* Default true.
*/
public void setComputeU(boolean val) {
computeU = val;
}
/**
* Setting controlling whether to compute V matrix of low-rank SSVD.
*
* @param val true if we want to output V matrix. Default is true.
*/
public void setComputeV(boolean val) {
computeV = val;
}
/**
* @param cUHat whether produce U*Sigma^0.5 as well (default false)
*/
public void setcUHalfSigma(boolean cUHat) {
this.cUHalfSigma = cUHat;
}
/**
* @param cVHat whether produce V*Sigma^0.5 as well (default false)
*/
public void setcVHalfSigma(boolean cVHat) {
this.cVHalfSigma = cVHat;
}
/**
* @param cUSigma whether produce U*Sigma output as well (default false)
*/
public void setcUSigma(boolean cUSigma) {
this.cUSigma = cUSigma;
}
/**
* @param cVSigma whether produce V*Sigma output as well (default false)
*/
public void setcVSigma(boolean cVSigma) {
this.cVSigma = cVSigma;
}
/**
* Sometimes, if requested A blocks become larger than a split, we may need to
* use that to ensure at least k+p rows of A get into a split. This is
* requirement necessary to obtain orthonormalized Q blocks of SSVD.
*
* @param size the minimum split size to use
*/
public void setMinSplitSize(int size) {
minSplitSize = size;
}
/**
* This contains k+p singular values resulted from the solver run.
*
* @return singlular values (largest to smallest)
*/
public Vector getSingularValues() {
return svalues;
}
/**
* returns U path (if computation were requested and successful).
*
* @return U output hdfs path, or null if computation was not completed for
* whatever reason.
*/
public String getUPath() {
return uPath;
}
/**
* return V path ( if computation was requested and successful ) .
*
* @return V output hdfs path, or null if computation was not completed for
* whatever reason.
*/
public String getVPath() {
return vPath;
}
public String getuSigmaPath() {
return uSigmaPath;
}
public String getuHalfSigmaPath() {
return uHalfSigmaPath;
}
public String getvSigmaPath() {
return vSigmaPath;
}
public String getvHalfSigmaPath() {
return vHalfSigmaPath;
}
public boolean isOverwrite() {
return overwrite;
}
/**
* if true, driver to clean output folder first if exists.
*
* @param overwrite
*/
public void setOverwrite(boolean overwrite) {
this.overwrite = overwrite;
}
public int getOuterBlockHeight() {
return outerBlockHeight;
}
/**
* The height of outer blocks during Q'A multiplication. Higher values allow
* to produce less keys for combining and shuffle and sort therefore somewhat
* improving running time; but require larger blocks to be formed in RAM (so
* setting this too high can lead to OOM).
*
* @param outerBlockHeight
*/
public void setOuterBlockHeight(int outerBlockHeight) {
this.outerBlockHeight = outerBlockHeight;
}
public int getAbtBlockHeight() {
return abtBlockHeight;
}
/**
* the block height of Y_i during power iterations. It is probably important
* to set it higher than default 200,000 for extremely sparse inputs and when
* more ram is available. y_i block height and ABt job would occupy approx.
* abtBlockHeight x (k+p) x sizeof (double) (as dense).
*
* @param abtBlockHeight
*/
public void setAbtBlockHeight(int abtBlockHeight) {
this.abtBlockHeight = abtBlockHeight;
}
public boolean isBroadcast() {
return broadcast;
}
/**
* If this property is true, use DestributedCache mechanism to broadcast some
* stuff around. May improve efficiency. Default is false.
*
* @param broadcast
*/
public void setBroadcast(boolean broadcast) {
this.broadcast = broadcast;
}
/**
* Optional. Single-vector file path for a vector (aka xi in MAHOUT-817
* working notes) to be subtracted from each row of input.
*
*
* Brute force approach would force would turn input into a dense input, which
* is often not very desirable. By supplying this offset to SSVD solver, we
* can avoid most of that overhead due to increased input density.
*
*
* The vector size for this offest is n (width of A input). In PCA and R this
* is known as "column means", but in this case it can be any offset of row
* vectors of course to propagate into SSVD solution.
*
*/
public Path getPcaMeanPath() {
return pcaMeanPath;
}
public void setPcaMeanPath(Path pcaMeanPath) {
this.pcaMeanPath = pcaMeanPath;
}
long getOmegaSeed() {
return omegaSeed;
}
/**
* run all SSVD jobs.
*
* @throws IOException if I/O condition occurs.
*/
public void run() throws IOException {
Deque closeables = Lists.newLinkedList();
try {
Class labelType =
SSVDHelper.sniffInputLabelType(inputPath, conf);
FileSystem fs = FileSystem.get(conf);
Path qPath = new Path(outputPath, "Q-job");
Path btPath = new Path(outputPath, "Bt-job");
Path uHatPath = new Path(outputPath, "UHat");
Path svPath = new Path(outputPath, "Sigma");
Path uPath = new Path(outputPath, "U");
Path uSigmaPath = new Path(outputPath, "USigma");
Path uHalfSigmaPath = new Path(outputPath, "UHalfSigma");
Path vPath = new Path(outputPath, "V");
Path vHalfSigmaPath = new Path(outputPath, "VHalfSigma");
Path vSigmaPath = new Path(outputPath, "VSigma");
Path pcaBasePath = new Path(outputPath, "pca");
if (overwrite) {
fs.delete(outputPath, true);
}
if (pcaMeanPath != null) {
fs.mkdirs(pcaBasePath);
}
Random rnd = RandomUtils.getRandom();
omegaSeed = rnd.nextLong();
Path sbPath = null;
double xisquaredlen = 0.0;
if (pcaMeanPath != null) {
/*
* combute s_b0 if pca offset present.
*
* Just in case, we treat xi path as a possible reduce or otherwise
* multiple task output that we assume we need to sum up partial
* components. If it is just one file, it will work too.
*/
Vector xi = SSVDHelper.loadAndSumUpVectors(pcaMeanPath, conf);
if (xi == null) {
throw new IOException(String.format("unable to load mean path xi from %s.",
pcaMeanPath.toString()));
}
xisquaredlen = xi.dot(xi);
Omega omega = new Omega(omegaSeed, k + p);
Vector s_b0 = omega.mutlithreadedTRightMultiply(xi);
SSVDHelper.saveVector(s_b0, sbPath = new Path(pcaBasePath, "somega.seq"), conf);
}
/*
* if we work with pca offset, we need to precompute s_bq0 aka s_omega for
* jobs to use.
*/
QJob.run(conf,
inputPath,
sbPath,
qPath,
ablockRows,
minSplitSize,
k,
p,
omegaSeed,
reduceTasks);
/*
* restrict number of reducers to a reasonable number so we don't have to
* run too many additions in the frontend when reconstructing BBt for the
* last B' and BB' computations. The user may not realize that and gives a
* bit too many (I would be happy i that were ever the case though).
*/
BtJob.run(conf,
inputPath,
qPath,
pcaMeanPath,
btPath,
minSplitSize,
k,
p,
outerBlockHeight,
q <= 0 ? Math.min(1000, reduceTasks) : reduceTasks,
broadcast,
labelType,
q <= 0);
sbPath = new Path(btPath, BtJob.OUTPUT_SB + "-*");
Path sqPath = new Path(btPath, BtJob.OUTPUT_SQ + "-*");
// power iterations
for (int i = 0; i < q; i++) {
qPath = new Path(outputPath, String.format("ABt-job-%d", i + 1));
Path btPathGlob = new Path(btPath, BtJob.OUTPUT_BT + "-*");
ABtDenseOutJob.run(conf,
inputPath,
btPathGlob,
pcaMeanPath,
sqPath,
sbPath,
qPath,
ablockRows,
minSplitSize,
k,
p,
abtBlockHeight,
reduceTasks,
broadcast);
btPath = new Path(outputPath, String.format("Bt-job-%d", i + 1));
BtJob.run(conf,
inputPath,
qPath,
pcaMeanPath,
btPath,
minSplitSize,
k,
p,
outerBlockHeight,
i == q - 1 ? Math.min(1000, reduceTasks) : reduceTasks,
broadcast,
labelType,
i == q - 1);
sbPath = new Path(btPath, BtJob.OUTPUT_SB + "-*");
sqPath = new Path(btPath, BtJob.OUTPUT_SQ + "-*");
}
DenseSymmetricMatrix bbt =
SSVDHelper.loadAndSumUpperTriangularMatricesAsSymmetric(new Path(btPath,
BtJob.OUTPUT_BBT
+ "-*"), conf);
// convert bbt to something our eigensolver could understand
assert bbt.columnSize() == k + p;
/*
* we currently use a 3rd party in-core eigensolver. So we need just a
* dense array representation for it.
*/
Matrix bbtSquare = new DenseMatrix(k + p, k + p);
bbtSquare.assign(bbt);
// MAHOUT-817
if (pcaMeanPath != null) {
Vector sq = SSVDHelper.loadAndSumUpVectors(sqPath, conf);
Vector sb = SSVDHelper.loadAndSumUpVectors(sbPath, conf);
Matrix mC = sq.cross(sb);
bbtSquare.assign(mC, Functions.MINUS);
bbtSquare.assign(mC.transpose(), Functions.MINUS);
Matrix outerSq = sq.cross(sq);
outerSq.assign(Functions.mult(xisquaredlen));
bbtSquare.assign(outerSq, Functions.PLUS);
}
EigenDecomposition eigen = new EigenDecomposition(bbtSquare);
Matrix uHat = eigen.getV();
svalues = eigen.getRealEigenvalues().clone();
svalues.assign(Functions.SQRT);
// save/redistribute UHat
fs.mkdirs(uHatPath);
DistributedRowMatrixWriter.write(uHatPath =
new Path(uHatPath, "uhat.seq"), conf, uHat);
// save sigma.
SSVDHelper.saveVector(svalues,
svPath = new Path(svPath, "svalues.seq"),
conf);
UJob ujob = null;
if (computeU) {
ujob = new UJob();
ujob.run(conf,
new Path(btPath, BtJob.OUTPUT_Q + "-*"),
uHatPath,
svPath,
uPath,
k,
reduceTasks,
labelType,
OutputScalingEnum.NOSCALING);
// actually this is map-only job anyway
}
UJob uhsjob = null;
if (cUHalfSigma) {
uhsjob = new UJob();
uhsjob.run(conf,
new Path(btPath, BtJob.OUTPUT_Q + "-*"),
uHatPath,
svPath,
uHalfSigmaPath,
k,
reduceTasks,
labelType,
OutputScalingEnum.HALFSIGMA);
}
UJob usjob = null;
if (cUSigma) {
usjob = new UJob();
usjob.run(conf,
new Path(btPath, BtJob.OUTPUT_Q + "-*"),
uHatPath,
svPath,
uSigmaPath,
k,
reduceTasks,
labelType,
OutputScalingEnum.SIGMA);
}
VJob vjob = null;
if (computeV) {
vjob = new VJob();
vjob.run(conf,
new Path(btPath, BtJob.OUTPUT_BT + "-*"),
pcaMeanPath,
sqPath,
uHatPath,
svPath,
vPath,
k,
reduceTasks,
OutputScalingEnum.NOSCALING);
}
VJob vhsjob = null;
if (cVHalfSigma) {
vhsjob = new VJob();
vhsjob.run(conf,
new Path(btPath, BtJob.OUTPUT_BT + "-*"),
pcaMeanPath,
sqPath,
uHatPath,
svPath,
vHalfSigmaPath,
k,
reduceTasks,
OutputScalingEnum.HALFSIGMA);
}
VJob vsjob = null;
if (cVSigma) {
vsjob = new VJob();
vsjob.run(conf,
new Path(btPath, BtJob.OUTPUT_BT + "-*"),
pcaMeanPath,
sqPath,
uHatPath,
svPath,
vSigmaPath,
k,
reduceTasks,
OutputScalingEnum.SIGMA);
}
if (ujob != null) {
ujob.waitForCompletion();
this.uPath = uPath.toString();
}
if (uhsjob != null) {
uhsjob.waitForCompletion();
this.uHalfSigmaPath = uHalfSigmaPath.toString();
}
if (usjob != null) {
usjob.waitForCompletion();
this.uSigmaPath = uSigmaPath.toString();
}
if (vjob != null) {
vjob.waitForCompletion();
this.vPath = vPath.toString();
}
if (vhsjob != null) {
vhsjob.waitForCompletion();
this.vHalfSigmaPath = vHalfSigmaPath.toString();
}
if (vsjob != null) {
vsjob.waitForCompletion();
this.vSigmaPath = vSigmaPath.toString();
}
} catch (InterruptedException exc) {
throw new IOException("Interrupted", exc);
} catch (ClassNotFoundException exc) {
throw new IOException(exc);
} finally {
IOUtils.close(closeables);
}
}
enum OutputScalingEnum {
NOSCALING, SIGMA, HALFSIGMA
}
}
