Multi-fidelity Gaussian process regression

Seemlessly perform multi-fidelity (and multi-objective) Gaussian process regression. This code is largely a wrapper for GPy implementing the multi-fidelity Gaussian process regression described in Perdikaris P., Raissi M., Damianou A., Lawrence N. D. and Karniadakis G. E. 2017Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling Proc. R. Soc. A.473 (paper, code), with some added functionality for heteroskedastic noise and multi-objective model structuring.

A multi-fidelity GPR $f_t(x)$ is defined by fitting a model to the provided data for a target fidelity level also conditioned on a GPR posterior $f_{*_{t-1}}(x)$ estimated from some other low-fidelity data:

$$f_t(x)=g_t\left(x, f_{*_{t-1}}(x)\right)$$

Getting Started

Installation

An environment file env.yaml is provided that contains the base dependencies. With conda installed:

$ conda env create --file env.yaml
$ conda activate mfGPR

With the environment active the package can then be installed:

$ git clone https://github.com/Ferg-Lab/mfGPR.git
$ cd ./mfGPR
$ pip install .

Usage

Example Jupyter notebooks demonstrating the functionality outlined here are available in the examples directory.

Simple GPR

Gaussian process regression models can be defined and trained by passing a dictionary containing the data intended to train the models. For example, for a simple model trained on some (X, Y) data pairs (optionally also including per-point uncertainties Y_std heteroskedastic noise, if specified):

from mfGPR.mfGPR import mfGPR

# define data
data = {'model': {'data':[X, Y]}}
#data = {'model': {'data':[X, Y], 'std':Y_std}} # or with heteroskedastic noise

# train model
models = mfGPR(data=data)

# predict on some test points
mean, std = models['model'].predict(X_test)

Multi-fidelity GPR

More complex multi-fidelity models can then be defined by specifying a conditioning models treated as the low-fidelity source model:

from mfGPR.mfGPR import mfGPR

# define data
data = {'low': {'data':[X_low, Y_low]},
        'mid': {'data': [X_mid, Y_mid], 'condition': 'mid'},
        'high': {'data': [X_high, Y_high], 'condition': 'mid'},
        }

# train model
models = mfGPR(data=data)

# predict on some test points
mean_low, std_low = models['low'].predict(X_test)
mean_mid, std_mid = models['mid'].predict(X_test)
mean_high, std_high = models['high'].predict(X_test)

# print graphical structure of the mfGPR
models

This structure corresponds to a three-level GPR with mid-level model conditioned on posterior of the GPR trained on the low data, and the high-level then conditioned on the posterior of the mid-level model. The _repr_html_ for models in a Jupyter notebook will generate a graphical representation of this model structure for simpler visualization of the hierarchical GPR structre:

Multi-fidelity & multi-objective GPR

Another functionality is a multi-objective structure where data from multible objective functions and data sources can be used to condition a higher fidelity model:

from mfGPR.mfGPR import mfGPR

# define data
data = {'low_0': {'data':[X_low_0, Y_low_0]},
        'low_1': {'data': [X_low_1, Y_low_1]},
        'mid': {'data': [X_mid, Y_mid], 'condition': ['low_0', 'low_1']},
        #'mid': {'data': [X_mid, Y_mid], 'condition': ['low_0', 'low_1'], 'theta':[0.5, 0.5]}, # with scalarization explicitly specified 
        'high': {'data': [X_high, Y_high], 'condition': 'mid'},
        }

# train model
models = mfGPR(data=data, n_splits=5, cv_discretization=11)

# predict on some test points
mean_low_0, std_low_0 = models['low_0'].predict(X_test)
mean_low_1, std_low_1 = models['low_1'].predict(X_test)
mean_mid, std_mid = models['mid'].predict(X_test)
mean_high, std_high = models['high'].predict(X_test)

# print graphical structure of the mfGPR
models

Operationally, this multi-objective definition performs a scalarization of the the low-fidelity functions and feeds the scalarized posterior as the low-fidelity posterior to the higher fidelity model conditioning on these two low-fidelity models. The scalarization can either be defined by passing a predefined scalarization as a theta key in the data dictionary, or if no theta key is provided cross-validation is performed to determine the optimal values of theta by performing n_splits-fold cross validation over a grid of possible theta values discretized according to cv_discretization.

Cross validation

Lastly, cross-validation can be peformed after the models have been fit by passing the 'cv':True in the data dictionary:

from mfGPR.mfGPR import mfGPR

# define data
data = {'low': {'data':[X_low, Y_low]},
        'mid': {'data': [X_mid, Y_mid], 'condition': 'mid'},
        'high': {'data': [X_high, Y_high], 'condition': 'mid', 'cv':True},
        'high_vanilla': {'data': [X_high, Y_high], 'cv':True},
        }

# train model
models = mfGPR(data=data)

# predict on some test points
mean_low, std_low = models['low'].predict(X_test)
mean_mid, std_mid = models['mid'].predict(X_test)
mean_high, std_high = models['high'].predict(X_test)
mean_high_vanilla, std_high_vanilla = models['high_vanilla'].predict(X_test)

# get cross validation MSE
high_cv_MSE = models['high']['cv_MSE']
high_vanilla_cv_MSE = models['high_vanilla']['cv_MSE']

# print graphical structure of the mfGPR
models

References

Multi-fidelity GPR construction:

@article{perdikaris2017nonlinear,
  title={Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling},
  author={Perdikaris, Paris and Raissi, Maziar and Damianou, Andreas and Lawrence, Neil D and Karniadakis, George Em},
  journal={Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences},
  volume={473},
  number={2198},
  pages={20160751},
  year={2017},
  publisher={The Royal Society Publishing}
}

Multi-objective scalarization

@article{knowles2006parego,
  title={ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems},
  author={Knowles, Joshua},
  journal={IEEE Transactions on Evolutionary Computation},
  volume={10},
  number={1},
  pages={50--66},
  year={2006},
  publisher={IEEE}
}

@inproceedings{paria2020flexible,
  title={A flexible framework for multi-objective bayesian optimization using random scalarizations},
  author={Paria, Biswajit and Kandasamy, Kirthevasan and P{\'o}czos, Barnab{\'a}s},
  booktitle={Uncertainty in Artificial Intelligence},
  pages={766--776},
  year={2020},
  organization={PMLR}
}

Our paper applying this functionality for molecular design with Bayesian optimization

@article{shmilovich2022hybrid,
  title={Hybrid computational--experimental data-driven design of self-assembling $\pi$-conjugated peptides},
  author={Shmilovich, Kirill and Panda, Sayak Subhra and Stouffer, Anna and Tovar, John D and Ferguson, Andrew L},
  journal={Digital Discovery},
  volume={1},
  number={4},
  pages={448--462},
  year={2022},
  publisher={Royal Society of Chemistry}
}

Copyright

Copyright (c) 2023, Kirill Shmilovich

Acknowledgements

Project based on the Computational Molecular Science Python Cookiecutter version 1.1.