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Description:

ifBO 0.3.9

In-context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization
This repository contains the official code for our ICML 2024 paper. This main branch provides the Freeze-Thaw PFN surrogate (FT-PFN) surrogate model as a drop-in surrogate for multi-fidelity Bayesian Optimization loops. Along with the synthetic prior generation and training code. To reproduce experiments from the above paper version, please refer to the branch icml-2024.
To use the ifBO algorithm in practice, please refer to NePS, a package for hyperparameter optimization that maintains the latest, improved ifBO version (TBA, TODO).
Setup
conda create -n ifBO-env python=3.10 setuptools
conda activate ifBO-env
pip install -e .

Surrogate versions

Version
Identifier
Notes

0.0.1
ICML '24 submission
FT-PFN from ifBO, trained on LCNet curves, DPL power law, broke scaling law

Surrogate usage API
# To initialize the surrogate and load pretrained weights
from ifbo.surrogate import FTPFN

model = FTPFN()

NOTE: This creates a .model/ directory in the current working directory for the surrogate model. To have control over this specify a target_path: Path when initializing.
To cite:
If using our surrogate, code, experiment setup, kindly cite using:
@inproceedings{
rakotoarison-icml24,
title={In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization},
author={H. Rakotoarison and S. Adriaensen and N. Mallik and S. Garibov and E. Bergman and F. Hutter},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=VyoY3Wh9Wd}
}