Austin R. Benson, Jason D. Lee, Bartek Rajwa, and David F. Gleich.
This code provides a MapReduce implementation of the large-scale near-separable nonnegative matrix factorizations described in Scalable methods for nonnegative matrix factorizations of near-separable tall-and-skinny matrices. The implementation uses Hadoop with Python streaming, supported by Dumbo and Feathers.
Given a data matrix X of size m x n, with m >> n and nonnegative entries, we are interested in a separable nonnegative matrix factorization:
X ~ X(:, K)H,
where X(:, K) is some permuted column subset of X with |K| columns, and H is |K| x n with nonnegative entries.
This code is provided under the BSD 2-Clause License, which can be found in the LICENSE file in the root directory, or at http://opensource.org/licenses/BSD-2-Clause.
If you use this code in a publication, please cite:
Austin R. Benson, Jason D. Lee, Bartek Rajwa, and David F. Gleich. Scalable methods for nonnegative matrix factorizations of near-separable tall-and-skinny matrices. In Proceedings of Neural Information Processing Systems (NIPS) 2014.
The heat transfer simulation data is available from the Open Source Data Cloud.
The flow cytometry data is in data/FC_40k.txt. This is the data matrix before taking the Kronecker product, which is what we analyze in the paper. See below for how to construct the Kronecker product from this data.
This code needs the following software to run:
After building Feathers, place the jar file in the head of the mrnmf directory.
This will let you run the examples in this documentation.
We assume that the matrix data records in HDFS are one of the following:
- a tab or space-separated string representing one row
- a list representing one row
- a list of lists representing multiple rows
- a numpy.ndarray type representing multiple rows
The main Dumbo script is RunNMF.py.
This is used to reduce the dimension of the problem.
This script supports computing any of the following:
- Gaussian projection: G * X, where G has Gaussian i.i.d. random entries
- TSQR: the R factor of X = QR
- Column l_1 norms: | X(:, i) |_1 for each column index i
These are all computed in one pass over the data. See the documentation in the file for more information. Here is an example command that does all three computatations:
dumbo start RunNMF.py -mat datamatrix -output proj.out -projsize 100 \
-libjar feathers.jar -reduce_schedule 40,1 -hadoop $HADOOP_INSTALL
The parameters are:
- datamatrix is the name of the matrix file in HDFS.
- projsize is the number of rows in G
- 40,1 means 40 reducers in the first pass and 1 reducer in the second pass
- $HADOOP_INSTALL is the location of Hadoop.
The main script for computing the extreme columns and the
coefficient matrix H is NMFProcessAlgorithms.py.
Code for reproducing the plots in the paper are in the plotting directory.
A small noisy r-separable matrix is available in SmallNoisySep_10k_10_4.txt.
The matrix has 10,000 rows, 10 columns, and a separation rank of 4 (r = 4),
and was generated with util_scripts/GenSyntheticSepSmall.py. The noise
level was 1e-3.
First, put the matrix in HDFS:
hadoop fs -put data/SmallNoisySep_10k_10_4.txt SmallNoisySep_10k_10_4.txt
Next, compute the Gaussian projection, R, and column norms:
dumbo start RunNMF.py -libjar feathers.jar -hadoop $HADOOP_INSTALL \
-projsize 12 -reduce_schedule 2,1 -mat SmallNoisySep_10k_10_4.txt -output small.out
Copy the data locally and look at the output.
dumbo cat small.out/GP -hadoop $HADOOP_INSTALL > small.out-proj.txt
cat small.out-proj.txt
dumbo cat small.out/QR -hadoop $HADOOP_INSTALL > small.out-qrr.txt
cat small.out-qrr.txt
dumbo cat small.out/colnorms -hadoop $HADOOP_INSTALL > small.out-colnorms.txt
cat small.out-colnorms.txt
Compute NMF with different algorithms:
python NMFProcessAlgorithms.py small.out-qrr.txt small.out-colnorms.txt 'SPA' 4
python NMFProcessAlgorithms.py small.out-qrr.txt small.out-colnorms.txt 'xray' 4
python NMFProcessAlgorithms.py small.out-proj.txt small.out-colnorms.txt 'GP' 4
We see that the noise level for SPA is near 1e-3, as expected. Since X-ray is greedy, it may not get all of the columns with the target separation rank set to 4:
python NMFProcessAlgorithms.py small.out-qrr.txt small.out-colnorms.txt 'xray' 5
We do not need to compute the Gaussian projection, R factor, and column factors.
The script RunNMF.py supports running only a subset of these operations.
We will continue using the data from the small example above. If we only wanted the R factor in the QR factorization:
dumbo start RunNMF.py -libjar feathers.jar -hadoop $HADOOP_INSTALL \
-reduce_schedule 2,1 -mat SmallNoisySep_10k_10_4.txt -output small.out \
-gp 0 -colnorms 0
The last two options specify to not do Gaussian projection or compute the column norms. Now, we see that only the R factor has been computed:
hadoop fs -ls small.out
Alternatively, we could just omit the QR computation:
dumbo start RunNMF.py -libjar feathers.jar -hadoop $HADOOP_INSTALL \
-projsize 10 -reduce_schedule 2,1 -mat SmallNoisySep_10k_10_4.txt -output small.out \
-qr 0
The computation of the R factor has been omitted:
hadoop fs -ls small.out
The original FC measurements are in data/FC_40k.txt.
We are interested in the Kronecker product of this matrix.
We form the Kronecker product with MapReduce.
First, copy the data to HDFS:
hadoop fs -put data/FC_40k.txt FC_40k.txt
Now, run the MapReduce job to form the Kronecker product:
dumbo start FC_kron.py -mat FC_40k.txt -output FC_kron.bseq -hadoop $HADOOP_INSTALL
The rows of the matrix are grouped with 1000 rows per record. With the Kronecker product on HDFS, we can do dimension reduction:
dumbo start RunNMF.py -libjar feathers.jar -hadoop $HADOOP_INSTALL \
-reduce_schedule 40,1 -mat FC_kron.bseq -output FC_data.out
The synthetic coefficient matrix used in the paper is in data/Hprime_20_200.txt.
We can generate (noisy) r-separable matrices using the script GenSyntheticSepLarge.py.
To generate a 200M x 200 matrix with r = 20 and noise level epsilon=1e-3:
hadoop fs -put data/Simple_1M.txt Simple_1M.txt
dumbo start GenSyntheticSepLarge.py -hadoop $HADOOP_INSTALL \
-m 200 -H 'data/Hprime_20_200.txt' -epsilone 1e-3 \
-mat Simple_1M.txt -output Noisy_200M_200_20.bseq
Please contact Austin Benson (arbenson@stanford.edu) for help with the code, bug reports, and feature requests.