surmise 0.2.0

A modular interface for surrogate models and tools

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 build for Python 3.8 or above, with the following dependencies:

• numpy>=1.18.3

• scipy>=1.7

• scikit-learn>=1.2.0

Installation

From the command line, use the following command to install surmise:

pip install surmise

Alternatively, the source code can be downloaded to the local folder, and the package can be installed from the .tar file.

Testing

The test suite requires the pytest and pytest-cov packages to be installed and can be run from the tests/ directory of the source distribution by running:

./run-tests.sh

Further options are available for testing. To see a complete list of options, run:

./run-tests.sh -h

Coverage reports are produced under the relevant directory only if all tests are used.

Documentation

The documentation is stored in docs/ and is compiled with the Sphinx Python documentation generator. It is written in the reStructuredText format. The documentation is hosted at Read the Docs.

To compile the documentation, first ensure that Sphinx and its dependencies are installed. To install Sphinx and/or ensure compatibility of dependencies, run make from a terminal within the docs/ directory:

cd docs
make

To generate documentation, run command make html from a terminal within the docs/ directory:

(cd docs)
make html

The HTML files are then stored in docs/_build/html.

Citation:

• Please use the following to cite surmise in a publication:

@techreport{surmise2023,
  author      = {Matthew Plumlee and \"Ozge S\"urer and Stefan M. Wild and Moses Y-H. Chan},
  title       = {{surmise 0.2.0} Users Manual},
  institution = {NAISE},
  number      = {Version 0.2.0},
  year        = {2023},
  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.