GPmp-contrib

gpmp-contrib provides higher-level workflows, models, and sequential design algorithms built on top of gpmp.

It is intended for practical Gaussian-process pipelines where you want to compose:

• model containers and ready-to-use Matérn model classes,
• sequential strategies for optimization / excursion / set inversion,
• diagnostics and visualization helpers,
• specialized procedures such as relaxed GP (reGP).

Highlights

• Model classes:
  • Model_ConstantMean_Maternp_ML
  • Model_ConstantMean_Maternp_REML
  • Model_ConstantMean_Maternp_REMAP
  • Model_Noisy_ConstantMean_Maternp_REML
• Sequential strategies:
  • grid-search based (SequentialStrategyGridSearch)
  • SMC-adaptive (SequentialStrategySMC)
  • BSS-style (SequentialStrategyBSS)
• Optimization / design modules:
  • expected improvement (gpmpcontrib.optim.expectedimprovement)
  • excursion set estimation (gpmpcontrib.optim.excursionset)
  • set inversion and Pareto utilities
• reGP utilities:
  • gpmpcontrib.regp

Package Layout

• gpmpcontrib/models/: preconfigured Matérn model classes
• gpmpcontrib/modelcontainer.py: multi-output container and wrappers
• gpmpcontrib/sequentialprediction.py: prediction/update orchestration
• gpmpcontrib/sequentialstrategy.py: sequential decision strategies
• gpmpcontrib/optim/: EI, excursion, set inversion, Pareto tools
• gpmpcontrib/regp/: relaxed GP utilities
• examples/: runnable scripts

Requirements

• Python >=3.9
• gpmp >= 0.9.34
• numpy, scipy>=1.12.0, matplotlib

Installation

Clone the repository:

git clone https://github.com/gpmp-dev/gpmp-contrib.git
cd gpmp-contrib

Install in dev mode:

pip install -e .

Quick Start

import gpmpcontrib as gpc

# See examples/ for full workflows
problem = gpc.ComputerExperiment(
    1,
    [[-1.0], [1.0]],
    single_function=lambda x: x**2,
)

Examples

The examples/ directory includes:

• model construction and prediction (example01 to example05)
• expected improvement optimization (example10, example11, example12)
• relaxed GP workflow (example20)
• excursion set estimation (example30, example31)
• set inversion workflows (example40, example41)

Authors

See AUTHORS.md.

How to Cite

If you use GPmp-contrib in your research, please cite it as follows:

@software{gpmpcontrib2026,
  author       = {Emmanuel Vazquez},
  title        = {GPmp-contrib},
  year         = {2026},
  url          = {https://github.com/gpmp-dev/gpmp-contrib},
  note         = {Version x.y},
}

Please update the version number as appropriate.

Copyright

Copyright (C) 2022-2026 CentraleSupelec

License

GPmp contrib is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

GPmp contrib is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with gpmp. If not, see http://www.gnu.org/licenses/.