Installation guide¶

elapid is accessible from pypi:

pip install elapid

It's also accessible from conda:

conda install -c conda-forge elapid

This should suffice for most linux/mac users, as there are builds available for most of the dependencies (numpy, sklearn, glmnet, geopandas, rasterio).

Windows installs can be more difficult. There are two primary challenges you may face: installing the key geospatial dependencies, and installing glmnet.

You can avoid both of them by using Windows Subsystem for Linux, which creates a linux environment on your Windows machine. If you're using an older version of Windows, see the options below.

Resolving geospatial dependencies¶

With conda¶

If you have conda installed, use that to install elapid's dependencies:

conda create -n elapid python=3.8 -y
conda activate elapid # or just `activate elapid` on windows
conda install -y -c conda-forge geopandas rasterio rtree scikit-learn tqdm
pip install elapid

Installing on Windows without conda¶

You can get Windows builds of several key geospatial packages using pipwin, which installs wheels from an unofficial source:

pip install wheel pipwin
pipwin install numpy
pipwin install pandas
pipwin install shapely
pipwin install gdal
pipwin install fiona
pipwin install pyproj
pipwin install six
pipwin install rtree
pip install geopandas
pip install elapid

Installing glmnet¶

glmnet is optional. When it's importable, elapid uses it to fit the Maxent logistic regression (matching the R maxnet reference); when it isn't, elapid falls back to sklearn.

Caveat — installing glmnet via pip is currently broken. The python wrapper hasn't been released since 2020. Its setup.py predates PEP 517 and fails to build under modern installers (pip, uv, etc.), and no wheels are published on PyPI. The only working install paths are:

conda-forge (recommended). The conda-forge glmnet build is current on Python 3.8–3.11 (no 3.12+ build at time of writing):

conda install -c conda-forge glmnet

The elapid dev-glmnet pixi environment. Equivalent to (1) but managed for you alongside the rest of the dev tooling:

pixi run -e dev-glmnet test

Manual Fortran build. Install gfortran + setuptools and run pip install --no-build-isolation glmnet. Brittle; not recommended.

If glmnet isn't installable on your target Python, elapid still works — the sklearn solver path covers the same modeling surface with slightly different regularization handling (see An important consideration below).

An important consideration¶

To simplify installing and working with elapid, you can install the package and fit Maxent models with or without glmnet. This is handled by fitting the maximum likelihood model with either glmnet or with sklearn.

The results are similar, but not the same. This is largely because of differences in how regularization is handled.

glmnet can handle arrays of regularization terms, which allows fine-scale control over the potential importance of different features. There are a series of calibrated defaults used by elapid, which were originally defined in maxnet but have been updated in opinionated ways.

You can typically just assign a single value for regularization scores with sklearn.

The differences are relatively small. When comparing models fit in maxnet to elapid models, the level of agreement was r2 = 0.91 for glmnet models, r2 = 0.85 for sklearn models.

Still, I recommend users try their best to install glmnet if you're interested in maintaining fidelity to the other family of Maxent tools.