p-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresets

This is the accompanying code repository for the AISTATS 2022 publication p-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresets by Alexander Munteanu, Simon Omlor and Christian Peters.

How to install

1. Clone the repository and navigate into the new directory

   - git clone https://github.com/cxan96/efficient-probit-regression 
   - cd efficient-probit-regression

2. Create and activate a new virtual environment

   python -m venv venv
   . ./venv/bin/activate

3. Install the package locally

   pip install -e .

4. To confirm that everything worked, install pytest and run the tests

   pip install pytest
   pytest

How to run the experiments

The scripts directory contains multiple python scripts that can be used to run the experiments. Just make sure, that everything is installed properly.

For example, to run the covertype experiments you can use the following command:

python scripts/run_experiments_covertype.py

How to recreate the plots

The plots can be recreated using the jupyter notebooks that can be found in the notebooks directory. Instructions on how to set up a jupyter environment can be found here.