vaesimca 0.4.1

Implementation of VAE-SIMCA one class classification method

VAESIMCA — one class classifier based on Variational Autoencoders and data driven SIMCA approach

The package vaesimca implements a method for creating one-class classification (OCC) models (also known as anomaly detectors or novelty detectors) using Variational Autoencoders (VAE). The overall idea is based on another OCC method, DD-SIMCA, and hence can be considered as a adaptation of the DD-SIMCA approach using VAE for data decomposition. The theoretical background and practical examples for the vaesimca methods are described in this paper, please use it for citation. The paper is freely available to everyone via open access option, it is recommended to read it first and then come back and try the examples from the demo notebook.

Although the method we proposed is versatile, the package implements VAESIMCA method for analysis of images. It can be installed from PyPI using pip or any other package manager compatible with PyPI, e.g.:

pip3 install vaesimca

It requires numpy, scipy, torch, torchvision, pandas, torch_lr_finder and matplotlib, which will be automatically installed as dependencies.

Getting started

Use Jupyter notebook demo.ipynb in order to get started. To run the examples from this notebook you need to download zip file with simulated dataset (it is also used for illustration of the method in the paper). Here is direct link to the archive with the dataset.

Simply download the dataset and unzip it to your working directory, where you have the notebook, and follow the guides. CSV files can be downloaded from GitHub as well.

Releases

0.4.1 (2/6/2025)

• added possibility to load data from CSV files.
• added learning rate finder option.
• see demo.ipynb for all details.

0.3.7

• fixed a bug in saving state dictionary of the best model during training loop.

Reference

A. Petersen, S. Kucheryavskiy, VAE-SIMCA — Data-driven method for building one class classifiers with variational autoencoders, Chemometrics and Intelligent Laboratory Systems, 256, 2025, 105276, DOI: 10.1016/j.chemolab.2024.105276.