DynaBO

This is the implementation of our Neurips 2025 submission titled DynaBO: Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization. In the paper we propose a method to incorporate dynamic user feedback in the form of priors at runtime.

Install

To install and run our method, you need to execute the following steps:

1. Clone the repository with all additional dependencies using:

git clone --recursive https://github.com/automl/DynaBO.git 

2. Create a conda environment and activate it using:

conda create -n DynaBO python=3.10
conda activate DynaBO

3. Install the repo and all dependencies:

make install

Execution

Our experiments rely on the library. They therefore require either using a mysql or sqlite database. The process of using PyExperimenter is described in its documentation. To replicate our experiments you need to execute the following steps

1. Create gt_data needed for priors by running: dynabo/experiments/gt_experiments/exectue_gt.py for both mfbench and yahpogym. (As described in the paper, we executed one seed for one seed initially, and then only considered the learners classified as medium and hard.)
2. Create priors by running dynabo/data_processing/extract_gt_priors.py
3. Execute the baselines, dynabo, and πBO using the scripts located in dynabo/experiments. In our experiments ran slurm jobs utilizing the scripts in cluster_scripts.
4. Create plots using dynabo/plotting/plotting.py.