Welcome to pyGAM’s documentation!¶

pyGAM is a package for building Generalized Additive Models in Python, with an emphasis on modularity and performance. The API will be immediately familiar to anyone with experience of scikit-learn or scipy.

Installation¶

pyGAM is on pypi, and can be installed using pip:

pip install pygam

Or via conda-forge, however this is typically less up-to-date:

conda install -c conda-forge pyGAM

You can install the bleeding edge from github using flit. First clone the repo, cd into the main directory and do:

pip install flit
flit install

Optional¶

To speed up optimization on large models with constraints, it helps to have scikit-sparse installed because it contains a slightly faster, sparse version of Cholesky factorization. The import from scikit-sparse references nose, so you’ll need that too.

The easiest way is to use Conda:

conda install -c conda-forge scikit-sparse nose

More information is available in the scikit-sparse docs.

Dependencies¶

pyGAM is tested on Python 2.7 and 3.6 and depends on NumPy, SciPy, and progressbar2 (see requirements.txt for version information).

Optional: scikit-sparse.

In addtion to the above dependencies, the datasets submodule relies on Pandas.

Citing pyGAM¶

Servén D., Brummitt C. (2018). pyGAM: Generalized Additive Models in Python. Zenodo. DOI: 10.5281/zenodo.1208723

Contact¶

To report an issue with pyGAM please use the issue tracker.

License¶

GNU General Public License v3.0

Getting Started¶

If you’re new to pyGAM, read the Tour of pyGAM for an introduction to the package.