prefGP

prefGP is a Gaussian process based library for learning from preference and choice data. It is implemented using Jax and PyTorch. prefGP implements 9 models to learn from preference and choice data:

• Model 1: Consistent Preferences.
• Model 2: Just Noticeable Difference
• Model 3: Probit for Erroneous Preferences
• Model 4: Preferences with Gaussian noise error
• Model 5: Probit for Erroneous preferences as a classification problem
• Model 6: Thurstonian model for label preferences
• Model 7: Plackett-Luce model for label ordering data
• Model 8: Paired comparison for label preferences
• Model 9: Rational and Pseudo-rational models for choice data

Installation

Requirements:

• Python >= 3.11

Download the repository and then install

pip install -r requirements.txt

Example

The notebooks folder includes several ipython notebooks that demonstrate the use of prefGP. For more details about the models used in the examples, please see the below paper.

Citing Us

@article{prefGP2024,
  title = {A tutorial on learning from preferences and choices with Gaussian Processes},
  author = {Benavoli, Alessio and Azzimonti, Dario},
  journal = {arXiv preprint},
  year = {2024},
  eprint = {2403.11782},
  url = {https://arxiv.org/abs/2403.11782}
}

The Team

The library was developed by

• Dario Azzimonti (IDSIA)
• Alessio Benavoli (Trinity College Dublin)