Documentation | Installation | GitHub | Tutorials
mfbml provides provide a general Multi-Fidelity Bayesian Machine Learning framework. The developed MF-BML framework can be used to handle both data scarce and data rich data set scenario depending on the employed algorithm within the framework. The developed MF-BML framework doesn't restrict any algorithm, two configurations are recommended in this repo for handling data scarce and large data set problems respectively.
mfbml is a package that supports general multi-fidelity Bayesian machine learning. Two practical multi-fidelity Bayesian machine learning algorithms from the paper: 1) Kernel Ridge Regression + Linear Transfer-learning + Gaussian Process Regression (KRR-LR-GPR), implemented based on Numpy; 2) Deep Neural Network + Linear Transfer-learning + Bayesian Neural Network (DNN-LR-BNN), implemented based on Pytorch.
In the particular case of a research environment, `mfbml is designed to easily accommodate further developments, either by improving the already implemented methods or by including new numerical models and techniques.
Author:
- Jiaxiang Yi ([email protected])
Author affiliation:
- Delft University of Technology
arXiv (paper):
@misc{yi2024practicalmultifidelitymachinelearning,
title={Practical multi-fidelity machine learning: fusion of deterministic and Bayesian models},
author={Jiaxiang Yi and Ji Cheng and Miguel A. Bessa},
year={2024},
eprint={2407.15110},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2407.15110},
}
Installation
(1). git clone the repo to your local machine
https://github.com/JiaxiangYi96/mfbml.git
cd mfbml
(2) create a new conda environment with python version 3.10
conda create -n mfbml_env python=3.10
conda activate mfbml_env
(3). install dependencies first (a git repo mfpml with branch main and pytorch with cpu installation)
pip install -r requirements.txt
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
(4). go to the local folder where you cloned the repo, and pip install it with editable mode
pip install --verbose --no-build-isolation --editable .
Illustrative examples
-
Kernel Ridge Regression + Linear Transfer-learning + Gaussian Process Regression Notebook
-
Deep Neural Network + Linear Transfer-learning + Bayesian Neural Network Notebook
-
More illustrative examples shown in the paper can be found in the studies folder.
If you have any question, please raise an issue on GitHub or contact the developer
BSD 3-Clause License, Jiaxiang Yi
All rights reserved.
mfbml is a free and open-source repo published under BSD 3-Clause License.