XPySom-dask

Self Organizing Maps with Dask Support

XPySom-dask is a dask version of the original XPySom project. The original project is a batched version of SOM algorithm, it can be easily transformed into a distributed version using Dask.

Installation

You can download XPySom-dask from PyPi:

pip install xpysom-dask

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-dask[cuda102]

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

pip3 install git+https://github.com/jcfaracco/xpysom-dask.git

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:

chunks = (4, 2)
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-dask import XPySom

import dask.array as da

from dask.distributed import Client, LocalCluster

client = Client(LocalCluster())

dask_data = da.from_array(data, chunks=chunks)

som = XPySom(6, 6, 4, sigma=0.3, learning_rate=0.5, use_dask=True, chunks=chunks) # initialization of 6x6 SOM
som.train(dask_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])

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.

Authors

Copyright (C) 2021 Julio Faracco