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Subject [CONF] Apache Mahout > Dimensional Reduction
Date Thu, 07 Apr 2011 16:52:00 GMT
Space: Apache Mahout (
Page: Dimensional Reduction (

Change Comment:
Added example using Amazon EMR

Edited by Timothy Potter:
Matrix algebra underpins the way many Big Data algorithms and data structures are composed:
full-text search can be viewed as doing matrix multiplication of the term-document matrix
by the query vector (giving a vector over documents where the components are the relevance
score), computing co-occurrences in a collaborative filtering context (people who viewed X
also viewed Y, or ratings-based CF like the Netflix Prize contest) is taking the squaring
the user-item interaction matrix, calculating users who are k-degrees separated from each
other in a social network or web-graph can be found by looking at the k-fold product of the
graph adjacency matrix, and the list goes on (and these are all cases where the linear structure
of the matrix is preserved!)

Each of these examples deal with cases of matrices which tend to be tremendously large (often
millions to tens of millions to hundreds of millions of rows or more, by sometimes a comparable
number of columns), but also rather sparse. Sparse matrices are nice in some respects: dense
matrices which are 10^7 on a side would have 100 trillion non-zero entries! But the sparsity
is often problematic, because any given two rows (or columns) of the matrix may have zero
overlap. Additionally, any machine-learning work done on the data which comprises the rows
has to deal with what is known as "the curse of dimensionality", and for example, there are
too many columns to train most regression or classification problems on them independently.

One of the more useful approaches to dealing with such huge sparse data sets is the concept
of dimensionality reduction, where a lower dimensional space of the original column (feature)
space of your data is found / constructed, and your rows are mapped into that subspace (or
sub-manifold).  In this reduced dimensional space, "important" components to distance between
points are exaggerated, and unimportant ones washed away, and additionally, sparsity of your
rows is traded for drastically reduced dimensional, but dense "signatures". While this loss
of sparsity can lead to its own complications, a proper dimensionality reduction can help
reveal the most important features of your data, expose correlations among your supposedly
independent original variables, and smooth over the zeroes in your correlation matrix.

One of the most straightforward techniques for dimensionality reduction is the matrix decomposition:
singular value decomposition, eigen decomposition, non-negative matrix factorization, etc.
In their truncated form these decompositions are an excellent first approach toward linearity
preserving unsupervised feature selection and dimensional reduction. Of course, sparse matrices
which don't fit in RAM need special treatment as far as decomposition is concerned. Parallelizable
and/or stream-oriented algorithms are needed.

h1. Singular Value Decomposition

Currently implemented in Mahout (as of 0.3, the first release with MAHOUT-180 applied), are
two scalable implementations of SVD, a stream-oriented implementation using the Asymmetric
Generalized Hebbian Algorithm outlined in Genevieve Gorrell & Brandyn Webb's paper ([Gorrell
and Webb 2005|]); and there is a [Lanczos
|] implementation, both single-threaded, and
in the o.a.m.math.decomposer.lanczos package (math module), as a hadoop map-reduce (series
of) job(s) in o.a.m.math.hadoop.decomposer package (core module). Coming soon: stochastic

h2. Lanczos

The Lanczos algorithm is designed for eigen-decomposition, but like any such algorithm, getting
singular vectors out of it is immediate (singular vectors of matrix A are just the eigenvectors
of A^t * A or A * A^t).  Lanczos works by taking a starting seed vector *v* (with cardinality
equal to the number of columns of the matrix A), and repeatedly multiplying A by the result:
*v'* = A.times(*v*) (and then subtracting off what is proportional to previous *v'*'s, and
building up an auxiliary matrix of projections).  In the case where A is not square (in general:
not symmetric), then you actually want to repeatedly multiply A*A^t by *v*: *v'* = (A * A^t).times(*v*),
or equivalently, in Mahout, A.timesSquared(*v*) (timesSquared is merely an optimization: by
changing the order of summation in A*A^t.times(*v*), you can do the same computation as one
pass over the rows of A instead of two).

After *k* iterations of *v_i* = A.timesSquared(*v_(i-1)*), a *k*-by-*k* tridiagonal matrix
has been created (the auxiliary matrix mentioned above), out of which a good (often extremely
good) approximation to *k* of the singular values (and with the basis spanned by the *v_i*,
the *k* singular *vectors* may also be extracted) of A may be efficiently extracted.  Which
*k*?  It's actually a spread across the entire spectrum: the first few will most certainly
be the largest singular values, and the bottom few will be the smallest, but you have no guarantee
that just because you have the n'th largest singular value of A, that you also have the (n-1)'st
as well.  A good rule of thumb is to try and extract out the top 3k singular vectors via Lanczos,
and then discard the bottom two thirds, if you want primarily the largest singular values
(which is the case for using Lanczos for dimensional reduction).

h3. Parallelization Stragegy

Lanczos is "embarassingly parallelizable": matrix multiplication of a matrix by a vector may
be carried out row-at-a-time without communication until at the end, the results of the intermediate
matrix-by-vector outputs are accumulated on one final vector.  When it's truly A.times(*v*),
the final accumulation doesn't even have collision / synchronization issues (the outputs are
individual separate entries on a single vector), and multicore approaches can be very fast,
and there should also be tricks to speed things up on Hadoop.  In the asymmetric case, where
the operation is A.timesSquared(*v*), the accumulation does require synchronization (the vectors
to be summed have nonzero elements all across their range), but delaying writing to disk until
Mapper close(), and remembering that having a Combiner be the same as the Reducer, the bottleneck
in accumulation is nowhere near a single point.

h3. Mahout usage

The Mahout DistributedLanzcosSolver is invoked by the <MAHOUT_HOME>/bin/mahout svd command.
This command takes the following arguments (which can be reproduced by just entering the command
with no arguments):

Job-Specific Options:                                                           
  --input (-i) input                      Path to job input directory.          
  --output (-o) output                    The directory pathname for output.    
  --numRows (-nr) numRows                 Number of rows of the input matrix    
  --numCols (-nc) numCols                 Number of columns of the input matrix 
  --rank (-r) rank                        Desired decomposition rank (note:     
                                          only roughly 1/4 to 1/3 of these will 
                                          have the top portion of the spectrum) 
  --symmetric (-sym) symmetric            Is the input matrix square and        
  --cleansvd (-cl) cleansvd               Run the EigenVerificationJob to clean 
                                          the eigenvectors after SVD            
  --maxError (-err) maxError              Maximum acceptable error              
  --minEigenvalue (-mev) minEigenvalue    Minimum eigenvalue to keep the vector 
  --inMemory (-mem) inMemory              Buffer eigen matrix into memory (if   
                                          you have enough!)                     
  --help (-h)                             Print out help                        
  --tempDir tempDir                       Intermediate output directory         
  --startPhase startPhase                 First phase to run                    
  --endPhase endPhase                     Last phase to run                     

The short form invocation may be used to perform the SVD on the input data: 
  <MAHOUT_HOME>/bin/mahout svd \
  --input (-i) <Path to input matrix> \   
  --output (-o) <The directory pathname for output> \   
  --numRows (-nr) <Number of rows of the input matrix> \   
  --numCols (-nc) <Number of columns of the input matrix> \
  --rank (-r) <Desired decomposition rank> \
  --symmetric (-sym) <Is the input matrix square and symmetric>    

The --input argument is the location on HDFS where a SequenceFile<Writable,VectorWritable>
(preferably SequentialAccessSparseVectors instances) lies which you wish to decompose.  Each
vector of which has --numcols entries.  --numRows is the number of input rows and is used
to properly size the matrix data structures.

After execution, the --output directory will have a file named "rawEigenvectors" containing
the raw eigenvectors. As the DistributedLanczosSolver sometimes produces "extra" eigenvectors,
whose eigenvalues aren't valid, and also scales all of the eigenvalues down by the max eignenvalue
(to avoid floating point overflow), there is an additional step which spits out the nice correctly
scaled (and non-spurious) eigenvector/value pairs. This is done by the "cleansvd" shell script
step (c.f. EigenVerificationJob).

If you have run he short form svd invocation above and require this "cleaning" of the eigen/singular
output you can run "cleansvd" as a separate command:
  <MAHOUT_HOME>/bin/mahout cleansvd \
  --eigenInput <path to raw eigenvectors> \
  --corpusInput <path to corpus> \
  --output <path to output directory> \
  --maxError <maximum allowed error. Default is 0.5> \
  --minEigenvalue <minimum allowed eigenvalue. Default is 0.0> \
  --inMemory <true if the eigenvectors can all fit into memory. Default false>

The --corpusInput is the input path from the previous step, --eigenInput is the output from
the previous step (<output>/rawEigenvectors), and --output is the desired output path
(same as svd argument). The two "cleaning" params are --maxError - the maximum allowed 1-cosAngle(v,
A.timesSquared(v)), and --minEigenvalue.  Eigenvectors which have too large error, or too
small eigenvalue are discarded.  Optional argument: --inMemory, if you have enough memory
on your local machine (not on the hadoop cluster nodes!) to load all eigenvectors into memory
at once (at least 8 bytes/double * rank * numCols), then you will see some speedups on this
cleaning process.

After execution, the --output directory will have a file named "cleanEigenvectors" containing
the clean eigenvectors. 

These two steps can also be invoked together by the svd command by using the long form svd
  <MAHOUT_HOME>/bin/mahout svd \
  --input (-i) <Path to input matrix> \   
  --output (-o) <The directory pathname for output> \   
  --numRows (-nr) <Number of rows of the input matrix> \   
  --numCols (-nc) <Number of columns of the input matrix> \
  --rank (-r) <Desired decomposition rank> \
  --symmetric (-sym) <Is the input matrix square and symmetric> \  
  --cleansvd "true"   \
  --maxError <maximum allowed error. Default is 0.5> \
  --minEigenvalue <minimum allowed eigenvalue. Default is 0.0> \
  --inMemory <true if the eigenvectors can all fit into memory. Default false>

After execution, the --output directory will contain two files: the "rawEigenvectors" and
the "cleanEigenvectors".

TODO: also allow exclusion based on improper orthogonality (currently computed, but not checked
against constraints).

h3. Example: SVD of ASF Mail Archives on Amazon Elastic MapReduce

This section walks you through a complete example of running the Mahout SVD job on Amazon
Elastic MapReduce cluster and then preparing the output to be used for clustering. This example
was developed as part of the effort to benchmark Mahout's clustering algorithms using a large
document set (see [MAHOUT-588|]). Specifically,
we use the ASF mail archives that have been parsed and converted to the Hadoop SequenceFile
format (block-compressed) and saved to a public S3 folder: s3://asf-mail-archives/mahout-0.4/sequence-files.
Overall, there are 6,094,444 key-value pairs in 283 files taking around 5.7GB of disk.

The bulk of the content for this section was extracted from the Mahout user mailing list,
see: [Need a little help with using SVD|]

Note: Some of this work is due in part to credits donated by the Amazon Elastic MapReduce

h5. 1. Launch EMR Cluster

For a detailed explanation of the steps involved in launching an Amazon Elastic MapReduce
cluster for running Mahout jobs, please read the "Building Vectors for Large Document Sets"
section of [Mahout on Elastic MapReduce|].

In the remaining steps below, remember to replace JOB_ID with the Job ID of your EMR cluster.

h5. 2. Load Mahout 0.5+ JAR into S3

These steps were created with the mahout-0.5-SNAPSHOT because they rely on the patch for [MAHOUT-639|]

h5. 3. Copy TFIDF Vectors into HDFS

Before running your SVD job on the vectors, you need to copy them from S3 to your EMR cluster's

elastic-mapreduce --jar s3://elasticmapreduce/samples/distcp/distcp.jar \
  --arg s3n://ACCESS_KEY:SECRET_KEY@asf-mail-archives/mahout-0.4/sparse-1-gram-stem/tfidf-vectors
  --arg /asf-mail-archives/mahout/sparse-1-gram-stem/tfidf-vectors \
  -j JOB_ID

h5. 4. Run the SVD Job

Now you're ready to run the SVD job on the vectors stored in HDFS:

elastic-mapreduce --jar s3://BUCKET/mahout-examples-0.5-SNAPSHOT-job.jar \
  --main-class org.apache.mahout.driver.MahoutDriver \
  --arg svd \
  --arg -i --arg /asf-mail-archives/mahout/sparse-1-gram-stem/tfidf-vectors \
  --arg -o --arg /asf-mail-archives/mahout/svd \
  --arg --rank --arg 100 \
  --arg --numCols --arg 20444 \
  --arg --numRows --arg 6076937 \
  --arg --cleansvd --arg "true" \
  -j JOB_ID

This will run 100 iterations of the LanczosSolver SVD job to produce 87 eigenvectors in:


Only 87 eigenvectors were produced because of the cleanup step, which removes any duplicate
eigenvectors caused by convergence issues and numeric overflow and any that don't appear to
be "eigen" enough (ie, they don't satisfy the eigenvector criterion with high enough fidelity).
- Jake Mannix

h5. 5. Transform your TFIDF Vectors into Mahout Matrix

The tfidf vectors created by the seq2sparse job are SequenceFile<Text,VectorWritable>.
The Mahout RowId job transforms these vectors into a matrix form that is a SequenceFile<IntWritable,VectorWritable>
and a SequenceFile<IntWritable,Text> (where the original one is the join of these new
ones, on the new int key).

elastic-mapreduce --jar s3://BUCKET/mahout-examples-0.5-SNAPSHOT-job.jar \
  --main-class org.apache.mahout.driver.MahoutDriver \
  --arg rowid \
  --arg -Dmapred.input.dir=/asf-mail-archives/mahout/sparse-1-gram-stem/tfidf-vectors \
  --arg -Dmapred.output.dir=/asf-mail-archives/mahout/sparse-1-gram-stem/tfidf-matrix \
  -j JOB_ID

This is not a distributed job and will only run on the master server in your EMR cluster.
The job produces the following output:


where docIndex is the SequenceFile<IntWritable,Text> and matrix is SequenceFile<IntWritable,VectorWritable>.

h5. 6. Transpose the Matrix

Our ultimate goal is to multiply the TFIDF vector matrix times our SVD eigenvectors. For the
mathematically inclined, from the rowid job, we now have an m x n matrix T (m=6076937, n=20444).
The SVD eigenvector matrix E is p x n (p=87, n=20444). So to multiply these two matrices,
I need to transpose E so that the number of columns in T equals the number of rows in E (i.e.
E^T is n x p) the result of the matrixmult would give me an m x p matrix (m=6076937, p=87).

However, in practice, computing the matrix product of two matrices as a map-reduce job is
efficiently done as a map-side join on two row-based matrices with the same number of rows,
and the columns are the ones which are different.  In particular, if you take a matrix X which
is represented as a set of numRowsX rows, each of which has numColsX, and another matrix with
numRowsY == numRowsX, each of which has numColsY (!= numColsX), then by summing the outer-products
of each of the numRowsX pairs of vectors, you get a matrix of with numRowsZ == numColsX, and
numColsZ == numColsY (if you instead take the reverse outer product of the vector pairs, you
can end up with the transpose of this final result, with numRowsZ == numColsY, and numColsZ
== numColsX). - Jake Mannix

Thus, we need to transpose the matrix using Mahout's Transpose Job:

elastic-mapreduce --jar s3://BUCKET/mahout-examples-0.5-SNAPSHOT-job.jar \
  --main-class org.apache.mahout.driver.MahoutDriver \
  --arg transpose \
  --arg -i --arg /asf-mail-archives/mahout/sparse-1-gram-stem/tfidf-matrix/matrix \
  --arg --numRows --arg 6076937 \
  --arg --numCols --arg 20444 \
  -j JOB_ID

This job requires the patch to [MAHOUT-639|]

The job creates the following output:


The number is computed internally by Mahout, which makes it hard to script the matrixmult
job, see [MAHOUT-655|]

h5. 7. Transpose Eigenvectors

If you followed Jake's explanation in step 6 above, then you know that we also need to transpose
the eigenvectors:

elastic-mapreduce --jar s3://BUCKET/mahout-examples-0.5-SNAPSHOT-job.jar \
  --main-class org.apache.mahout.driver.MahoutDriver \
  --arg transpose \
  --arg -i --arg /asf-mail-archives/mahout/svd/cleanEigenvectors \
  --arg --numRows --arg 87 \
  --arg --numCols --arg 20444 \
  -j JOB_ID

The job creates the following output:


h5. 8. Matrix Multiplication

Lastly, we need to multiply the transposed vectors using Mahout's matrixmult job:

elastic-mapreduce --jar s3://BUCKET/mahout-examples-0.5-SNAPSHOT-job.jar \
  --main-class org.apache.mahout.driver.MahoutDriver \
  --arg matrixmult \
  --arg --numRowsA --arg 20444 \
  --arg --numColsA --arg 6076937 \
  --arg --numRowsB --arg 20444 \
  --arg --numColsB --arg 87 \
  --arg --inputPathA --arg /asf-mail-archives/mahout/sparse-1-gram-stem/tfidf-matrix/transpose-##
  --arg --inputPathB --arg /asf-mail-archives/mahout/svd/transpose-## \
  -j JOB_ID

Notice that you need to know the name of the transpose output directory to supply the input
paths. Hopefully this will be resolved soon.

h1. Resources


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