chi-som 1.0.3

Fast self-oranizing maps for cheminformatics using numba

ᵡ-SOM

ChemInformatics SOM Toolkit

ᵡ-SOM is a high-performance framework for training emergent self-organizing maps (ESOMs) with a specific focus on cheminformatics; including on-disc, low-latency data storage and a GUI.
It was specifically developed for visualising the chemical space of million-scale molecular datasets and for interactive exploration.

Installation

Currently, ChI-SOM is only available for Linux, and Windows using WSL2.

It can be installed directly from PyPI

pip install chi-som

For the CUDA compute backend, numba-cuda is required. On systems running CUDA, ChI-SOM can be installed with CUDA support via

pip install 'chi-som[cu12]'

for CUDA13 or

pip install 'chi-som[cu13]'

for CUDA12

Please refer to the numba-cuda documentation for more complex setups.

Documentation

Documentation for ChI-SOM is available at https://kochgroup.github.io/ChI-SOM/

Usage example

import numpy as np
import pandas as pd

from chisom import Som, start_chisom_viewer
from chisom.utils import decay_linear, lattice_size

data = np.random.random((600, 400))

# Set up with ESOM rules
n_datapoints, n_features = data.shape
rows, columns = lattice_size(n_datapoints)
SIGMA = rows // 2

# Create a SOM object
# The high and low parameters should be chosen according to the dataset values
som = Som(
    rows,
    columns,
    n_features,
    low=data.min(),
    high=data.max(),
)

N_EPOCHS = 30

# The training loop
for epoch in range(N_EPOCHS):
    # Calculate the current sigma and alpha values using decay functions
    current_sigma = decay_linear(epoch, SIGMA, total_iterations=N_EPOCHS)
    current_alpha = decay_linear(epoch, 0.8, total_iterations=N_EPOCHS)

    # Train one epoch
    som.train(data, epoch, current_sigma, current_alpha)

# Calculate the U-Matrix
umx = som.get_umatrix()

# Predict the best matching units and quantization errors for all data points
bmus, qe = som.predict(data)


# Using the GUI needs information to overlay on the datapoints
dataset = pd.DataFrame.from_dict(
    {"Type:": ["A"] * len(data)}
)

# Start the GUI
start_chisom_viewer(umx, bmus, dataset)

For instructions on how to train SOMs on large dataset using the PyTorch DataLoader interface, please refer to the How-To Guides section.

CAVEATS

• The Viewer will only work on a systems with a display attached. When running the application on a server via a remote shell and calling start_chisom_viewer this will usually lead to errors ("This application failed to start because no Qt platform plugin could be initialized"). As solutions to this are very setup dependend, the recommended approach for very large SOMs is to only train the SOM on a powerful remote machine and analyse the trained SOM with the GUI locally.
• This software may be considered to be in beta stage. While the user-facing API is expected to remain stable up to a 2.0 release, the internal API might change at any release and can not be considered stable.

Development Setup

ChI-SOM is developed, built, and packaged using Astral uv

To set up a development environment initalize with

uv sync

To build run

uv build

Meta

Authors: Johannes Kaminski, Oliver Koch @ AG Koch
Contact: j.kaminski[at]uni-muenster.de

ChI-SOM is distributed under the LGPLv3. See LICENCES for more information.