XPySom

Self Organizing Maps

XPySom is a minimalistic implementation of the Self Organizing Maps (SOM) that can seamlessly leverage vector/matrix operations made available on Numpy or CuPy, resulting in an efficient implementation for both multi-core CPUs and GP-GPUs. XPySom has been realized as a quite invasive modification to the MiniSom code available at: https://github.com/JustGlowing/minisom.git.

SOM is a type of Artificial Neural Network able to convert complex, nonlinear statistical relationships between high-dimensional data items into simple geometric relationships on a low-dimensional display.

Installation

You can download XPySom from PyPi:

pip install xpysom

By default, dependencies for GPU execution are not downloaded. You can also specify a CUDA version to automatically download also those requirements. For example, for CUDA Toolkit 10.2 you would write:

pip install xpysom[cuda102]

Alternatively, you can manually install XPySom. Download XPySom to a directory of your choice and use the setup script:

git clone https://github.com/Manciukic/xpysom.git
python setup.py install

How to use it

The module interface is similar to MiniSom. In the following only the basics of the usage are reported, for an overview of all the features, please refer to the original MiniSom examples you can refer to: https://github.com/JustGlowing/minisom/tree/master/examples (you can find the same examples also in this repository but they have not been updated yet).

In order to use XPySom you need your data organized as a Numpy matrix where each row corresponds to an observation or as list of lists like the following:

data = [[ 0.80,  0.55,  0.22,  0.03],
        [ 0.82,  0.50,  0.23,  0.03],
        [ 0.80,  0.54,  0.22,  0.03],
        [ 0.80,  0.53,  0.26,  0.03],
        [ 0.79,  0.56,  0.22,  0.03],
        [ 0.75,  0.60,  0.25,  0.03],
        [ 0.77,  0.59,  0.22,  0.03]]      

Then you can train XPySom just as follows:

from xpysom import XPySom    
som = XPySom(6, 6, 4, sigma=0.3, learning_rate=0.5) # initialization of 6x6 SOM
som.train(data, 100) # trains the SOM with 100 iterations

You can obtain the position of the winning neuron on the map for a given sample as follows:

som.winner(data[0])

By default, XPySom executes on the GPU if available (and required packages are correctly installed). You can override this behaviour by passing the xp parameter to XPySom set to the package you want to use (only Numpy and Cupy have been tested, but in theory any Numpy-compliant package would work).

from xpysom import XPySom   
import numpy as np

som = XPySom(6, 6, 4, sigma=0.3, learning_rate=0.5, xp=np)

Differences with MiniSom

• The batch SOM algorithm is used (instead of the online used in MiniSom). Therefore, use only train to train the SOM, train_random and train_batch are not present.
• decay_function input parameter is no longer a function but one of 'linear', 'exponential', 'asymptotic'. As a consequence of this change, sigmaN and learning_rateN have been added as input parameters to represent the values at the last iteration.
• New input parameter std_coeff, used to calculate gaussian exponent denominator d = 2*std_coeff**2*sigma**2. Default value is 0.5 (as in Somoclu, which is different from MiniSom original value sqrt(pi)).
• New input parameter xp (default = cupy module). Back-end to use for computations.
• New input parameter n_parallel to set size of the mini-batch (how many input samples to elaborate at a time).
• Hexagonal grid support is experimental and is significantly slower than rectangular grid.

Additional documentation

A publication about the design and performance of XPySom has been accepted for presentation at the IEEE 32nd International Symposium on Computer Architecture and High Performance Computing:

• Riccardo Mancini, Antonio Ritacco, Giacomo Lanciano and Tommaso Cucinotta. "XPySom: High-Performance Self-Organizing Maps," IEEE 32nd International Symposium on Computer Architecture and High Performance Computing, September 8-11, 2020. Porto, Portugal (turned to a virtual on-line event due to the Covid-19 emergency).

TODO

• Update examples in examples/
• Improve hexagonal grid support

Compatibility notes

XPySom has been tested under Python 3.7.6 with CuPy 7.4.0 or Numpy 1.18.1.

License

XPySom Copyright (C) 2020 Riccardo Mancini

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

XPySom is a modification of the original MiniSom by Giuseppe Vettigli, which is licensed under the Creative Commons Attribution 3.0 Unported License and can be found at the following link.