quicksom 0.0.3

Self Organizing Maps efficient implementation using PyTorch

Self-Organizing Map

PyTorch implementation of a Self-Organizing Map. The implementation makes possible the use of a GPU if available for faster computations. It follows the scikit package semantics for training and usage of the model.

Requirements and setup

The SOM object requires torch installed.

It has dependencies in numpy, scipy and scikit-learn and scikit-image. The MD application requires pymol to load the trajectory that is not included in the dependencies

To set up the project, install pytorch and run :

pip install quicksom

SOM object interface

The SOM object can be created using any grid size, with a optional periodic topology. One can also choose optimization parameters such as the number of epochs to train or the batch size

To use it, we include three scripts to fit a SOM, to optionally build the clusters manually with a gui and to predict cluster affectations for new data points

quicksom_fit -h
quicksom_gui -h
quicksom_predict -h

The SOM object is also importable from python scripts to use directly in your analysis pipelines.

import pickle
import numpy
import torch
from quicksom.som import SOM

# Get data
device = 'cuda' if torch.cuda.is_available() else 'cpu'
X = numpy.load('contact_desc.npy')
X = torch.from_numpy(X)
X = X.float()
X = X.to(device)

# Create SOM object and train it, then dump it as a pickle object
m, n = 100, 100
dim = X.shape[1]
niter = 5
batch_size = 100
som = SOM(m, n, dim, niter=niter, device=device)
learning_error = som.fit(X, batch_size=batch_size)
som.to_device('cpu')
pickle.dump(som, open('som.pickle', 'wb'))

# Usage on the input data, predicted_clusts is an array of length n_samples with clusters affectations 
som = pickle.load(open('som.pickle', 'rb'))
som.to_device(device)
predicted_clusts, errors = som.predict_cluster(X)

Input dataset:

Umatrix:

Data projection:

Cluster projection:

Cluster affectation: