riverarodrigoa / minisom

:red_circle: MiniSom is a minimalistic implementation of the Self Organizing Maps

MiniSom

Self Organizing Maps

MiniSom is a minimalistic and Numpy based implementation of the Self Organizing Maps (SOM). SOM is a type of Artificial Neural Networks able to convert complex, nonlinear statistical relationships between high-dimensional data items into simple geometric relationships on a low-dimensional display.

Installation

Download MiniSom to the directory of your choice, enter it and run (may require root privileges):

python setup.py install

How to use it

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

data = [[ 5.1  3.5  1.4  0.2],
        [ 4.9  3.   1.4  0.2],
        [ 4.7  3.2  1.3  0.2], # <-- single observation
        [ 4.6  3.1  1.5  0.2],
        [ 5.   3.6  1.4  0.2],
        [ 4.1  3.3  1.4  0.2],
        [ 4.2  3.2  1.2  0.2]]         

Then you can run MiniSom just as follows:

from minisom import MiniSom    
som = MiniSom(6, 6, 4, sigma=0.3, learning_rate=0.5) # initialization of 6x6 SOM
print "Training..."
som.train_random(data, 100) # trains the SOM with 100 iterations
print "...ready!"

MiniSom implements two types of training. The random training (implemented by the method train_random), where the model is trained picking random samples from your data, and the batch training (implemented by the method train_batch), where the samples are picked in the order they are stored.

A data driven initialization of the weights is also provided by the method random_weights_init which initializes the weights picking random samples from the data.

Using the trained SOM

After the training you will be able to

• Compute the coordinate assigned to an observation x on the map with the method winner(x).
• Compute the average distance map of the weights on the map with the method distance_map().
• Compute the number of times that each neuron have been considered winner for the observations of a new data set with the method activation_response(data).
• Compute the quantization error with the method quantization_error(data).

Vector quantization

The data can be quantized by assigning a code book (weights vector of the winning neuron) to each sample in data. This kind of vector quantization is implemented by the method quantization that can be called as follows:

qnt = som.quantization(data)

In this example we have that qnt[i] is the quantized version of data[i].

Examples

In examples/example_iris.py you can find an example that shows how to train MiniSom and visualize the result using the Iris flower dataset. Here is the result of the script:

For each observation we have a marker placed on the position of the winning neuron on the map. Each type of marker represents a class of the iris data. The average distance map of the weights is used as background (see the color bar on the right to associate the value).

In examples/example_digits.py there is an example of how to use MiniSom for images clustering. The example uses the scitkits-learn wrapper to the handwritten digits dataset. Here is one of the result that MiniSom can achieve in digits recognition:

The graph above represent each image with the handwritten digit it contains. The position corresponds to the position of the winning neuron for the image. Here we also have a version of this graphs that shows the original images:

In examples/example_color.py you can find an example of how to use MiniSom for color quantization. Here is one of the possible results:

(the examples require matplotlib for the visualization of the results).

Who uses Minisom?

• Óscar Clavería González, Enric Monte Moreno, Salvador Torra Porras. A self-organizing map analysis of survey-based agents׳ expectations before impending shocks for model selection: The case of the 2008 financial crisis. International Economics Volume 146, Pages 40–58. August 2016.
• Sameen Mansha, Faisal Kamiran, Asim Karim, Aizaz Anwar. A Self-Organizing Map for Identifying InfluentialCommunities in Speech-based Networks. Proceeding CIKM '16 Proceedings of the 25th ACM International on Conference on Information and Knowledge Management Pages 1965-1968. 2016.
• Sameen Mansha, Zaheer Babar, Faisal Kamiran, Asim Karim. Neural Network Based Association Rule Mining from Uncertain Data. Neural Information Processing Volume 9950 of the series Lecture Notes in Computer Science pp 129-136. 2016.
• Makiyama, Vitor Hirota, M. Jordan Raddick, and Rafael DC Santos. Text Mining Applied to SQL Queries: A Case Study for the SDSS SkyServer. 2nd Annual International Symposium on Information Management and Big Data. 2015.
• Remi Domingues. Machine Learning for Unsupervised Fraud Detection. Royal Institute of Technology School of Computer Science and Communication KTH CSC. 2015.
• Ivana Kajić, Guido Schillaci, Saša Bodiroža, Verena V. Hafner, Learning hand-eye coordination for a humanoid robot using SOMs. Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction Pages 192-193.

Compatibility notes

Minisom has been tested under python 2.7.3 and 3.2.3.

License

MiniSom by Giuseppe Vettigli is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/.