ezmsg.learn#

Machine learning modules for the ezmsg framework.

Note

This package is experimental and under active development.

Overview#

ezmsg-learn provides machine learning processing units designed for streaming signals in the ezmsg framework.

Modules include:

  • Linear models - Linear regression, SLDA, CCA, SGD

  • Non-linear models - Multi-layer perceptrons (MLP)

  • Dimensionality reduction - Incremental PCA and other decomposition methods

  • Utilities - Helper functions for ML workflows

Most modules support both:

  • Offline initialization with known weights

  • Online adaptation with streaming 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 directly from GitHub:

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

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

Dependencies#

The base install requires:

  • ezmsg - Core ezmsg framework

  • ezmsg-baseproc - Processor base classes

  • ezmsg-sigproc - Signal processing extensions

  • numpy - Numerical computing

  • scipy - Scientific computing

  • array-api-compat - Array API portability layer

Optional extras#

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

ezmsg.learn.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".

Quick Start#

For general ezmsg tutorials and guides, visit ezmsg.org.

Contents:

Indices and tables#