sparse-som
Efficient Implementation of Self-Organizing Map for Sparse Input Data.
This program uses an algorithm especially intended for sparse data, which much faster than the classical one on very sparse datasets (time-complexity depend to non-zero values only).
Main features
- Highly optimized for sparse data (LIBSVM format).
- Support both online and batch SOM algorithms.
- Parallel batch implementation (OpenMP).
- OS independent.
- Python support.
Build
The simplest way to build the cli tools from the main directory : cd src && make all.
After the compilation terminates, the resulting executables may be found in the build directory.
GCC is reccomended, but you can use another compiler if you want. C++11 support is required. OpenMP support is required to take advantage of parallelism (sparse-bsom).
Install
No install required.
Python
To install the python version, simply run pip install sparse-som.
Usage
CLI
sparse-som
To use the online version :
Usage: sparse-som
-i infile input file at libsvm sparse format
-y nrows number of rows in the codebook
-x ncols number of columns in the codebook
[ -d dim ] force the dimension of codebook's vectors
[ -u ] one based column indices (default is zero based)
[ -N ] normalize the input vectors
[ -l cb ] load codebook from binary file
[ -o|O cb ] output codebook to filename (o:binary, O:text)
[ -c|C cl ] output classification (c:without counts, C:with counts)
[ -n neig ] neighborhood topology: 4=circ, 6=hexa, 8=rect (default 8)
[ -t n | -T e ] number of training iterations or epochs (epoch = nrows)
[ -r r0 -R rN ] radius at start and end (default r=(x+y)/2, R=0.5)
[ -a a0 -A aN ] learning rate at start and end (default a=0.5, A=1.e-37)
[ -H rCool ] radius cooling: 0=linear, 1=exponential (default 0)
[ -h aCool ] alpha cooling: 0=linear, 1=exponential (default 0)
[ -s stdCf ] sigma = radius * stdCf (default 0.3)
[ -v ] increase verbosity level (default 0, max 2)
sparse-bsom
To use the batch version :
Usage: sparse-bsom
-i infile input file at libsvm sparse format
-y nrows number of rows in the codebook
-x ncols number of columns in the codebook
[ -d dim ] force the dimension of codebook's vectors
[ -u ] one based column indices (default is zero based)
[ -N ] normalize the input vectors
[ -l cb ] load codebook from binary file
[ -o|O cb ] output codebook to filename (o:binary, O:text)
[ -c|C cl ] output classification (c:without counts, C:with counts)
[ -n neig ] neighborhood topology: 4=circ, 6=hexa, 8=rect (default 8)
[ -T epoc ] number of epochs (default 10)
[ -r r0 -R rN ] radius at start and end (default r=(x+y)/2, R=0.5)
[ -H rCool ] radius cooling: 0=linear, 1=exponential (default 0)
[ -s stdCf ] sigma = radius * stdCf (default 0.3)
[ -v ] increase verbosity level (default 0, max 2)
To control the number of threads used by OpenMP, set to OMP_NUM_THREADS variable to the desired value, for example :
OMP_NUM_THREADS=4 sparse-bsom ...
If undefined one thread per CPU is used.
Python
import numpy as np
from scipy.sparse import csr_matrix
from sklearn.datasets import load_digits
from sklearn.metrics import classification_report
from sparse_som import *
# Load some dataset
dataset = load_digits()
# convert to sparse CSR format
X = csr_matrix(dataset.data, dtype=np.float32)
# setup SOM dimensions
H, W = 12, 15 # Network height and width
_, N = X.shape # Nb. features (vectors dimension)
################ Simple usage ################
# setup SOM network
som = Som(H, W, N, topology.HEXA) # , verbose=True
print(som.nrows, som.ncols, som.dim)
# reinit the codebook (not needed)
som.codebook = np.random.rand(H, W, N).\
astype(som.codebook.dtype, copy=False)
# train the SOM
som.train(X)
# get bmus for the data
bmus = som.bmus(X)
################ Use classifier ################
# setup SOM classifier (using batch SOM)
cls = SomClassifier(BSom, H, W, N)
# train SOM, do calibration and predict labels
y = cls.fit_predict(X, labels=dataset.target)
print('Quantization Error: %2.4f' % cls.quant_error)
print('Topographic Error: %2.4f' % cls.topog_error)
print('='*50)
print(classification_report(dataset.target, y))Other examples are available in the python/examples directory.
Documentation
CLI
Files Format
Input files must be at LIBSVM format.
<label> <index1>:<value1> <index2>:<value2> ...
.
.
.
Each line contains an instance and is ended by a '\n' character. The pair <index>:<value> gives a feature (attribute) value: <index> is an integer starting from 0 and <value> is a real number. Indices must be in ASCENDING order. Labels in the file are only used for network calibration. If they are unknown, just fill the first column with any numbers.
Python documentation
The python documentation can be found at: http://sparse-som.readthedocs.io/en/latest/
API
The C++ API is not public yet, because things still may change.
How to cite this work
@InProceedings{melka-mariage:ijcci17,
author={Melka, Josu{\'e} and Mariage, Jean-Jacques},
title={Efficient Implementation of Self-Organizing Map for Sparse Input Data},
booktitle={Proceedings of the 9th International Joint Conference on Computational Intelligence: IJCCI},
volume={1},
month={November},
year={2017},
address={Funchal, Madeira, Portugal},
pages={54-63},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006499500540063},
isbn={978-989-758-274-5},
url={http://www.ai.univ-paris8.fr/~jmelka/IJCCI_2017_20.pdf}
}
@Inbook{Melka2019,
author = "Melka, Josu{\'e} and Mariage, Jean-Jacques",
editor = "Sabourin, Christophe and Merelo, Juan Julian and Madani, Kurosh and Warwick, Kevin",
title = "Adapting Self-Organizing Map Algorithm to Sparse Data",
bookTitle = "Computational Intelligence: 9th International Joint Conference, IJCCI 2017 Funchal-Madeira, Portugal, November 1-3, 2017 Revised Selected Papers",
year = "2019",
publisher = "Springer International Publishing",
address = "Cham",
pages = "139--161",
isbn = "978-3-030-16469-0",
doi = "10.1007/978-3-030-16469-0_8",
url = "https://doi.org/10.1007/978-3-030-16469-0_8"
}
