A modular interface for surrogate models and tools
Project description
Introduction to surmise
surmise is a Python package that is designed to provide a surrogate model interface for calibration, uncertainty quantification, and sensitivity analysis.
Dependencies
surmise is built with numpy and scipy, with an optional scikit-learn dependency.
Please refer to the [project] and [project.optional-dependencies] sections of pyproject.toml for details.
Installation
From the command line, use one of the following commands to install surmise:
pip install surmise pip install surmise[scikit-learn] # to include scikit-learn in installation pip install surmise[all] # to include all optional dependencies
The package scikit-learn is required by specific methods as stated above. These packages can be installed along with surmise via the commands listed.
The list of available .whl files can be found under PyPI-wheel. If a wheel file for your preferred platform is not listed, surmise has to be built from source, which requires extra dependencies:
git clone https://github.com/bandframework/surmise/ cd surmise python -m pip install --upgrade pip pip install build Cython pip install scikit-learn (optional, required by full test suite) python -m build --wheel pip install dist/surmise-<version info>.whl
Testing
Testing of surmise can be performed after cloning the repository. The test suite requires the pytest, pytest-cov, and scikit-learn packages to be installed. These packages can be installed via:
pip install pytest pytest-cov scikit-learn
The full test suite can then be run from the tests/ directory of the source distribution by the command:
cd /path/to/surmise/tests/ python -m pytest # (no coverage report) python -m pytest --cov # (with console coverage report)
Access to the test suite with specific testing options are available in the tests directory. To see a complete list of options, run:
./path/to/surmise/tests/run-tests.sh -h
Documentation
The documentation is stored in docs/ and is hosted at Read the Docs.
Users and developers that would like to generate the documentation locally are encouraged to use tox, which automatically creates a dedicated, fully-functioning virtual environment for the task. Refer to the online developer’s guide (or docs/tox.rst) for help setting up tox and using it to generate documentation.
Citation:
Please use the following to cite surmise in a publication:
@techreport{surmise2024,
author = {Matthew Plumlee and \"Ozge S\"urer and Stefan M. Wild and Moses Y-H. Chan},
title = {{surmise 0.3.0} Users Manual},
institution = {NAISE},
number = {Version 0.3.0},
year = {2024},
url = {https://surmise.readthedocs.io}
}
Examples
We provide examples in the examples/ directory to illustrate the basic usage of surmise.
In addition, for a gentle introduction of emulation and calibration using Gaussian processes, visit surmise Jupyter notebook.
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