ezmsg-learn

This repository contains a Python package with modules for machine learning (ML)-related processing in the ezmsg framework. As ezmsg is intended primarily for processing unbounded streaming signals, so are the modules in this repo.

If you are only interested in offline analysis without concern for reproducibility in online applications, then you should probably look elsewhere.

Processing units include dimensionality reduction, linear regression, and classification that can be initialized with known weights, or adapted on-the-fly with incoming (labeled) data.

Installation

The base install is NumPy-only. The machine-learning backends are optional extras, so a deployment that uses only the lightweight processors does not pay for a PyTorch or scikit-learn install:

pip install ezmsg-learn              # numpy-only processors
pip install "ezmsg-learn[sklearn]"   # + pandas, river, scikit-learn
pip install "ezmsg-learn[torch]"     # + torch
pip install "ezmsg-learn[all]"       # everything

Or install the latest development version:

pip install "git+https://github.com/ezmsg-org/ezmsg-learn@dev#egg=ezmsg-learn[all]"

Importing a module whose backend is not installed raises an ImportError naming the extra to install.

Dependencies

Base (pip install ezmsg-learn) — ezmsg, ezmsg-baseproc, ezmsg-sigproc, numpy, scipy, array-api-compat.

Extra	Adds	Covers
(none)	—	process.ssr, process.flatten, process.seqseqsampler, process.refit_kalman, model.cca, model.refit_kalman
sklearn	pandas, river, scikit-learn	process.adaptive_linear_regressor, process.linear_regressor, process.sgd, process.slda, process.sklearn, dim_reduce.*
torch	torch	process.base, process.torch, process.rnn, process.transformer, process.mlp_old, model.mlp, model.rnn, model.transformer
all	both of the above	everything, including all collection.sample_adapt_regressor backends

collection.sample_adapt_regressor imports its backend lazily, so it needs only the extra for the model_type in use — and none at all for model_type="kalman".

Development

We use uv for development.

1. Install uv if not already installed.
2. Fork this repository and clone your fork locally.
3. Open a terminal and cd to the cloned folder.
4. Run uv sync to create a .venv and install dependencies.
5. (Optional) Install pre-commit hooks: uv run pre-commit install
6. After making changes, run the test suite: uv run pytest tests

License

MIT License - see LICENSE for details.