IFE-Surrogate 0.2.5

A machine learning library intended for surrogate modeling tasks.

IFE Surrogate GP

A flexible and extensible library for Gaussian Processes, built with performance and modularity in mind.
Models can be trained via maximum likelihood estimation or by baysian inference.

Features

• High-performance kernels (JAX-compatible)
• Composable API for building custom models
• Multiple optimizers (Optax, Scipy, etc.)
• Built in Baysian inference of the models with NumPyro
• Automatic hyperparameter handling
• Built-in training workflows

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Installation

pip install ife_surrogate

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Usage

Quickstart

import ife_surrogate


dataset = np.load("some_data.npy", allow_pickle=True).item()
X, Y, f = dataset["X"], dataset["Y"], dataset["f"]

key = jr.key(seed=42)
(X_train, Y_train), (X_test, Y_test), _= train_test_split(
    X=X, Y=Y, f=f, 
    split=(0.9, 0.1, 0), 
    key= key
)

d = X_train.shape[1]
priors = {"lengthscale": Uniform(1e-0, 1e1), "power": Uniform(1, 2)}
kernel = kernels.Kriging(lengthscale=jnp.ones(d), power=jnp.ones(d), priors=priors)

model = models.WidebandGP(X_train, Y_train, kernel, f)

trainer = trainers.SwarmTrainer(number_iterations=200, number_particles=100)
trainer.train(model)

pred, var = model.predict(X_test)

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Documentation

• API Reference
• Tutorials
• Examples

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Key Components

• Kernels

  • Kriging
  • RBF
  • Matern
  • SumKernel
  • ProductKernel
  • Scale
  • RQ
  • Noise

• Models

  • WidebandGP
  • ScalerGP

• Trainers

  • OptaxTrainer
  • SwarmTrainer

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Roadmap

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License

Distributed under the MIT License. See LICENSE for more information.

References

The mathematical background and implementation of these models are based on the following publications:

Gaussian Process & Student-t Theory

• Gaussian Process Basics: Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press.
• Student-t Process (TP) Foundations: Shah, A., Wilson, A. G., & Ghahramani, Z. (2014). Student-t Processes as Alternatives to Gaussian Processes. Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS).

Wideband & Multi-output Modeling

• Wideband Architecture: Rezende, R. S., Hansen, J., Piwonski, A., & Schuhmann, R. (2024). Wideband Kriging for Multiobjective Optimization of a High-Voltage EMI Filter. IEEE Transactions on Electromagnetic Compatibility, 66(4), 1116–1124.
• Multi-output Separable GPs: Bilionis, I., Zabaras, N., Konomi, B. A., & Lin, G. (2013). Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification. Journal of Computational Physics, 241, 212–239.
• Vector-Valued Kernels: Alvarez, M. A., Rosasco, L., & Lawrence, N. D. (2012). Kernels for Vector-Valued Functions: A Review. [cite_start]Foundations and Trends in Machine Learning.

Inference & Software Stack

• JAX: Bradbury, J., et al. (2018). JAX: Composable transformations of Python+NumPy programs.
• NumPyro: Phan, D., Pradhan, N., & Jankowiak, M. (2019). Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro. arXiv preprint.
• Flax: Heek, J., et al. (2020). Flax: A neural network library and ecosystem for JAX.
• Optax: Babuschkin, I., et al. (2020). Optax: Composable gradient descent optimization for JAX.
• SciPy: Virtanen, P., et al. (2020). SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods.
• NUTS Sampler: Hoffman, M. D., & Gelman, A. (2014). The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research.

Acknowledgements