/minisom

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

Primary LanguagePython

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 Network able to convert complex, nonlinear statistical relationships between high-dimensional data items into simple geometric relationships on a low-dimensional display.

Installation

Just use pip:

pip install minisom

or download MiniSom to a directory of your choice and use the setup script:

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 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 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.

The weights of the network are randmly initialized by default. Two additional methods are provided to initialize the weights in a data driven fashion: random_weights_init and pca_weights_init.

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].

Export a SOM and load it again

A model can be saved using pickle as follows

import pickle
som = MiniSom(7, 7, 4)

# ...train the som here

# saving the some in the file som.p
with open('som.p', 'wb') as outfile:
    pickle.dump(som, outfile)

and can be loaded as follows

with open('som.p', 'rb') as infile:
    som = pickle.load(infile)

Note that if a lambda function is used to define the decay factor MiniSom will not be pickable anymore.

Examples

The code that produces the following figures is in this notebook: https://github.com/JustGlowing/minisom/blob/master/examples/examples.ipynb

  1. Iris flower dataset.

Iris example

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.

Each neuron is associated with one of the labels in the dataset with a specific degree.

  1. Color quantization

Color quantization example

  1. Images clustering

handwritten digitts 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:

handwritten digitts recognition

  1. Natural language processing

In this example each poem is associate with a cell in the map. The color represent the author. Check out the notebook in the examples for more details: https://github.com/JustGlowing/minisom/blob/master/examples/PoemsAnalysis.ipynb

Video tutorials

The following video tutorials made by the GeoEngineerings School show how to use MiniSom to build a fraud detection system:

Other tutorials

Who uses Minisom?

Compatibility notes

Minisom has been tested under Python 3.6.2.

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/.

License