PyEI is a Python library for ecological inference. The target audience is the analyst with an interest in the phenomenon called Racially Polarized Voting.
Racially Polarized Voting is a legal concept developed through case law under the Voting Rights Act of 1965; its genesis is in the majority opinion of Thornburg v. Gingles (1982). Considered the “evidentiary linchpin” for vote dilution cases, RPV is a necessary, but not sufficient, condition that plaintiffs must satisfy for a valid claim.
Toward that end, ecological inference uses observed data (historical election results), pairing voting outcomes with demographic information for each precinct in a given polity, to infer voting patterns for each demographic group.
PyEI brings together a variety of ecological inference methods in one place and facilitates reporting and plotting results; quantifying the uncertainty associated with results under a given model; making comparisons between methods; and bringing relevant diagnostic tools to bear on ecological inference methods.
PyEI is relatively new and under active development, so expect rough edges and bugs -- and for additional features and documentation to be coming quickly!
You can install the latest release from PyPi
with:
pip install pyei
Or, install directly from GitHub for the most up-to-date (but potentially less stable) version:
pip install git+https://github.com/mggg/ecological-inference.git
If you would like to explore PyEI without installation, you can explore this interactive Colab notebook (just note that inference might be slow!)
Check out the intro notebooks and example notebooks for sample code that shows how to run and adjust the various models in PyEI on datesets.
If you are new to ecological inference generally, start with pyei/intro_notebooks/Introduction_toEI.ipynb
.
If you are familiar with ecological inference and want an overview of PyEI and how to use it (with examples), then start with intro_notebooks/PyEI_overview.ipynb
.
To explore EI's plotting functionality, check out pyei/intro_notebooks/Plotting_with_PyEI.ipynb
.
For more work with two-by-two examples, see in pyei/examples/santa_clara_demo.ipynb
.
For more work with r-by-c examples, see pyei/examples/santa_clara_demo_r_by_c.ipynb
.
For examples of model comparison and checking steps with PyEI, see pyei/examples/model_eval_and_comparison_demo.ipynb
.
Feel free to file an issue if you are running into trouble or if there is a feature you'd particularly like to see, and we will do our best to get to it!
Contributions are welcome!
Uses Python 3.10. After cloning the environment, you should be able to use either virtualenv
or conda
to run the code. The second (conda
) is probably easier for development, but virtualenv
is used for the project's CI.
Here is how to create and activate each environment. See the docs for more elaborate details:
virtualenv pyei_venv # create virtualenv
source pyei_venv/bin/activate # activate virtualenv
python -m pip install -U pip # upgrade pip
python -m pip install -e . # install project locally
python -m pip install -r requirements-dev.txt # install dev requirements
conda create --name pyei --channel conda-forge python=3.10 --file requirements.txt --file requirements-dev.txt # create conda environment and install requirements
conda activate pyei
pip install -e . #install project locally
After making changes, make sure everything works by running
./scripts/lint_and_test.sh
This will also run automatically when you make a pull request, so if you have trouble getting that to run, just open the PR, and we can help!
If you are using PyEI, please cite it as:
Knudson et al., (2021). PyEI: A Python package for ecological inference. Journal of Open Source Software, 6(64), 3397, https://doi.org/10.21105/joss.03397
BibTeX:
@article{Knudson2021,
doi = {10.21105/joss.03397},
url = {https://doi.org/10.21105/joss.03397},
year = {2021},
publisher = {The Open Journal},
volume = {6},
number = {64},
pages = {3397},
author = {Karin C. Knudson and Gabe Schoenbach and Amariah Becker},
title = {PyEI: A Python package for ecological inference},
journal = {Journal of Open Source Software}
}