Pareto Embeddings

Pareto is a Python library for Pareto-embeddings, which are used in subset choice modeling using machine learning. This repository contains the code and resources required to use and extend Pareto-embeddings in your own projects.

Features

Pareto-embeddings offer unique advantages for practicioners in choice modeling:

• Native Support for Subset Choices: Unlike traditional approaches that primarily focus on discrete (top-1) choices, Pareto-embeddings natively support subset choices, making them ideal for a broader range of choice modeling applications.

• Multidimensional Utility Embeddings: Alternatives are embedded into a higher-dimensional utility space, representing them as a rich partial order rather than a linear order. This multidimensional approach captures the complexities and nuances of real-world choice scenarios, providing a more detailed and accurate representation.

• Custom Loss Functions: Our learning algorithm utilizes custom differentiable loss functions designed to learn monotone utility dimensions. This allows for flexible and effective training tailored to specific choice modeling tasks.

Installation

Prerequisites

• Python 3.7 or higher

Installation Steps

1. Clone the repository:

   git clone https://github.com/kiudee/pareto-embeddings.git
   cd pareto-embeddings

2. Install the dependencies using Poetry:

   poetry install

3. (Optional) Install additional dependencies for PostgreSQL support:

   poetry install --extras "pgsql"

4. (Optional) Install additional dependencies for experimental features:

   poetry install --extras "exp"

Usage

Running Experiments

To run an experiment, use the provided run_experiment script:

poetry run run_experiment

Fetching Jobs

To fetch a job, use the fetch_job script:

poetry run fetch_job

How to Cite Us

If you use Pareto in your research, please cite our paper:

@InProceedings{10.1007/978-3-030-58285-2_30,
author="Pfannschmidt, Karlson
and H{\"u}llermeier, Eyke",
editor="Schmid, Ute
and Kl{\"u}gl, Franziska
and Wolter, Diedrich",
title="Learning Choice Functions via Pareto-Embeddings",
booktitle="KI 2020: Advances in Artificial Intelligence",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="327--333",
abstract="We consider the problem of learning to choose from a given set of objects, where each object is represented by a feature vector. Traditional approaches in choice modelling are mainly based on learning a latent, real-valued utility function, thereby inducing a linear order on choice alternatives. While this approach is suitable for discrete (top-1) choices, it is not straightforward how to use it for subset choices. Instead of mapping choice alternatives to the real number line, we propose to embed them into a higher-dimensional utility space, in which we identify choice sets with Pareto-optimal points. To this end, we propose a learning algorithm that minimizes a differentiable loss function suitable for this task. We demonstrate the feasibility of learning a Pareto-embedding on a suite of benchmark datasets.",
isbn="978-3-030-58285-2"
}

License

This project is licensed under the Apache-2.0 License. See the LICENSE file for details.

Contact Information

For any questions or feedback, please contact Karlson Pfannschmidt at [email protected].