glmstar

A Python implementation of the glmnet algorithm for fitting generalized linear models via penalized maximum likelihood. This package provides fast and efficient regularization paths for linear, logistic, multinomial, Poisson, and Cox regression models.

Features

• Multiple GLM Families: Support for Gaussian (linear), Binomial (logistic), Multinomial, Poisson, and Cox regression
• Elastic Net Regularization: Combines L1 (Lasso) and L2 (Ridge) penalties with mixing parameter α
• Fast Path Algorithms: Efficient computation of regularization paths using coordinate descent
• Cross-Validation: Built-in cross-validation for optimal λ selection
• Prediction Methods: Interpolation and prediction at arbitrary λ values
• sklearn Compatibility: Follows scikit-learn API conventions
• C++ Backend: High-performance C++ implementation with pybind11 bindings

Installation

Get the source

The C++ code depends on two git submodules, Eigen and glmnetpp, so clone recursively:

git clone --recursive https://github.com/jonathan-taylor/pyglmnet.git
cd pyglmnet

# Or, if already cloned without submodules
git submodule update --init --recursive

A C++17 compiler is required to build the extension modules.

Development install with uv (recommended)

uv sync          # editable install plus build and test tools (the `dev` group)
uv run pytest tests --test-size=small

The project uses meson-python, whose editable installs recompile the C++ extensions on import. For that reason pyproject.toml disables build isolation for glmstar under uv, so the build tools (meson, ninja, pybind11, ...) must live in the project environment -- uv sync takes care of this. To target an already-activated venv other than .venv, use uv sync --active.

If you prefer uv pip, install the build tools first:

uv pip install meson-python meson ninja pybind11 setuptools_scm
uv pip install -e .      # or `uv pip install .` for a regular install

Running uv pip install . without them fails with No module named 'mesonpy'.

Install with pip

pip install .

For an editable install with pip, again install the build tools into the environment and disable build isolation:

pip install meson-python meson ninja pybind11 setuptools_scm
pip install --no-build-isolation -e .

Troubleshooting

If the build fails with 'pybind11/pybind11.h' file not found, a stale meson build directory (e.g. build/cp310) is pointing at a temporary build environment that no longer exists. Delete it and reinstall:

rm -rf build/cp*
uv sync

Quick Start

import numpy as np
from glmnet import GaussNet, LogNet
from sklearn.datasets import make_regression, make_classification

# Linear Regression
X, y = make_regression(n_samples=100, n_features=20, n_informative=5, random_state=42)
fit_gaussian = GaussNet().fit(X, y)

# Plot coefficient paths
ax = fit_gaussian.coef_path_.plot()
ax.set_title('Linear Regression Coefficient Paths')

# Logistic Regression
X, y = make_classification(n_samples=100, n_features=20, n_informative=5, random_state=42)
fit_logistic = LogNet().fit(X, y)

# Cross-validation
cvfit = LogNet().fit(X, y)
_, cvpath = cvfit.cross_validation_path(X, y, cv=5)
ax = cvpath.plot(score='Binomial Deviance')

Available Models

• GaussNet: Linear regression (Gaussian family)
• LogNet: Logistic regression (Binomial family)
• MultiClassNet: Multinomial regression
• FishNet: Poisson regression
• MultiGaussNet: Multi-response linear regression
• CoxNet: Cox proportional hazards model

Key Features

Elastic Net Regularization

# Lasso (α=1, default)
fit_lasso = GaussNet(alpha=1.0).fit(X, y)

# Ridge (α=0)
fit_ridge = GaussNet(alpha=0.0).fit(X, y)

# Elastic Net (α=0.5)
fit_elastic = GaussNet(alpha=0.5).fit(X, y)

Cross-Validation

# Perform cross-validation
cvfit = GaussNet().fit(X, y)
_, cvpath = cvfit.cross_validation_path(X, y, cv=5)

# Get optimal lambda
lambda_min = cvpath.index_best['Mean Squared Error']
coef_min, intercept_min = cvfit.interpolate_coefs(lambda_min)

Prediction and Interpolation

# Predict at specific lambda values
predictions = fit_gaussian.predict(X_new, interpolation_grid=[0.1, 0.05])

# Interpolate coefficients
coef, intercept = fit_gaussian.interpolate_coefs(0.5)

Weights and Offsets

import pandas as pd

# Create DataFrame with response, weights, and offsets
Df = pd.DataFrame({
    'response': y,
    'weight': sample_weights,
    'offset': offsets
})

# Fit with weights and offsets
fit = GaussNet(response_id="response", 
               weight_id="weight", 
               offset_id="offset").fit(X, Df)

Dependencies

• Core: numpy, scipy, pandas, scikit-learn, joblib, statsmodels, coxdev, tqdm
• Build: meson-python, meson, ninja, pybind11, setuptools_scm
• Optional: matplotlib (plotting), pytest (tests)

Development

Building from Source

See Installation; uv sync builds the C++ extensions in place and rebuilds them automatically on import after source changes.

Running Tests

# Run all tests (or prefix with `uv run`)
pytest tests/

# Quicker subset
pytest tests --test-size=small

# Run specific test suites
pytest tests/paths/
pytest tests/compare_R/

Saved Results in the Docs

The calibration experiments in the docs' Inference chapter are too slow to run when the docs are built, so they are run locally and their results saved in docs/results/*.csv, each with a header recording the commit and the package versions it was computed with. To recompute all of them, commit your changes, then, from the top of the repository, with the development install (uv sync, or pip install -e . and the doc extra),

uv run python docs/experiments.py --all    # or: python docs/experiments.py --all
git add docs/results && git commit -m "docs: recompute the saved results"

python docs/experiments.py probit_regression recomputes one page. See docs/experiments.py for the conventions.

When changing versions (a glmstar release, a new lassoinf or other dependency pin, or the src/glmnetpp submodule), recompute these results, so that the docs show results from the code they describe.

Citation

If you use this package in your research, please cite:

Friedman, J., Hastie, T., & Tibshirani, R. (2010). 
Regularization paths for generalized linear models via coordinate descent. 
Journal of Statistical Software, 33(1), 1-22.

License

This project is licensed under the BSD-3-Clause License - see the LICENSE.txt file for details.

Authors

• Trevor Hastie (hastie@stanford.edu)
• Balasubramanian Narasimhan (naras@stanford.edu)
• Jonathan Taylor (jonathan.taylor@stanford.edu)

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

Acknowledgments

This package is based on the original R glmnet package by Jerome Friedman, Trevor Hastie, Rob Tibshirani, and others. The C++ implementation uses the Eigen library for linear algebra operations.