numbasom 0.0.2

A Fast Self-Organizing Map Python Library Implemented in Numba

Welcome to NumbaSOM

A fast Self-Organizing Map Python library implemented in Numba.

This is a fast and simple to use SOM library. It utilizes online training (one data point at the time) rather than batch training. The implemented topologies are a simple 2D lattice or a torus.

How to Install

The installation is available at PyPI. Simply type:

pip install numbasom

How to use

A Self-Organizing Map is often used to show the underlying structure in data. To show how to use the library, we will train it on 200 random 3-dimensional vectors (so we can render them as colors):

import numpy as np
from numbasom import SOM

Create 200 random colors

data = np.random.random([200,3])

Initialize the library

We initalize a large map with 20 rows and 40 columns. The default topology is a 2D lattice. We can also train it on a torus by setting is_torus=True

som = SOM(som_size=(50,100), is_torus=False)

Train the SOM

We will adapt the lattice by iterating 1000 times through our data points. If we set ìs_scaled=False, data will be normalized before training.

lattice = som.train(data, num_iterations=10000, is_scaled=True)

SOM training took: 0.343299 seconds.

We can display a number of lattice cells to make sure they are 3-dimensional vectors

lattice[1::6,1]

array([[0.84320274, 0.30492829, 0.41450252],
       [0.74273113, 0.24442997, 0.46775752],
       [0.7079431 , 0.18936966, 0.3849527 ],
       [0.67390875, 0.13944585, 0.1926078 ],
       [0.511751  , 0.04086609, 0.17727931],
       [0.40736032, 0.21150301, 0.0357543 ],
       [0.35872757, 0.28562641, 0.0832515 ],
       [0.16635622, 0.08152816, 0.38226892],
       [0.05055562, 0.05010728, 0.34043282]])

The shape of the lattice is (20, 40, 3) as expected

lattice.shape

(50, 100, 3)

Visualizing the lattice

Since our lattice is made of 3-dimensional vectors, we can represent it as a lattice of colors.

import matplotlib.pyplot as plt

plt.imshow(lattice)
plt.show()

Compute U-matrix

Since the most of the data will not be 3-dimensional, we can use the U-matrix (unified distance matrix by Alfred Ultsch) to visualise the map and the clusters emerging on it.

from numbasom import u_matrix

um = u_matrix(lattice)

um.shape

(50, 100)

Plot U-matrix

The library contains a function plot_u_matrix that can help visualise it.

from numbasom import plot_u_matrix

plot_u_matrix(um, fig_size=(6.2,6.2))