Neural active learning meets the partial monitoring framework

This repository contains the implementation of algorithms described in the paper "Neural active learning meets the partial monitoring framework", accepted at UAI 2024. This branch is a public-version with accessible sandbox code. The other branch of the project is the developpers branch.

Prerequisites

Before you begin, ensure you have met the following requirements:

• Python 3.8
• pip

Installation

Follow these steps to set up your environment and run the experiments:

1. Create a Virtual Environment:

   python -m venv env
   source env/bin/activate  
   

2. Install Dependencies:

   pip install -r requirements.txt

3. Load datasets

   python ./load_data.py

4. Run code and get started

The sandbox code is stored in the jupyter notebook ''experiments.ipynb'' for more advanced scripts (e.g. slurm scripts) please check the developpers branch of the project.

Installation Troubleshooting:

• Gurobi Alternative: If you prefer not to use Gurobi, you can use PULP as an alternative optimizer. To do this, install PULP using pip install pulp. We provide code 'geometry_gurobi.py' and 'geometry_pulp.py'.

Acknowledgements

Special thanks to Yikun Ban, Yuheng Zhang for the open source implementations neural active learning baselines. The codebase also leveraged and adapted game environments from Tanguy Urvoy's pmlib (https://github.com/TanguyUrvoy/pmlib).