MXMC

General

MXMCPy is an open source package that implements many existing multi-model Monte Carlo methods (MLMC, MFMC, ACV) for estimating statistics from expensive, high-fidelity models by leveraging faster, low-fidelity models for speedup.

Getting Started

Installation

MXMCPy can be easily installed using pip:

pip install mxmcpy

or conda:

conda install mxmcpy

Alternatively, the MXMCPy repository can be cloned:

git clone https://github.com/nasa/mxmcpy.git

and the dependencies can be installed manually as follows.

Dependencies

MXMCPy is intended for use with Python 3.x. MXMCPy requires installation of a few dependencies which are relatively common for optimization/numerical methods with Python:

• numpy
• scipy
• pandas
• matplotlib
• h5py
• pytorch
• pytest, pytest-mock (if the testing suite is to be run)

A requirements.txt file is included for easy installation of dependencies with pip or conda.

Installation with pip:

pip install -r requirements.txt

Installation with conda:

conda install --yes --file requirements.txt

Documentation

Sphynx is used for automatically generating API documentation for MXMCPy. The most recent build of the documentation can be found in the repository at: doc/index.html

Running Tests

An extensive unit test suite is included with MXMCPy to help ensure proper installation. The tests can be run using pytest on the tests directory, e.g., by running:

python -m pytest tests 

from the root directory of the repository.

Example Usage

The following code snippet shows the determination of an optimal sample allocation for three models with assumed costs and covariance matrix using the MFMC algorithm:

import numpy as np
from mxmc import Optimizer

model_costs = np.array([1.0, 0.05, 0.001])
covariance_matrix = np.array([[11.531, 11.523, 12.304],
                              [11.523, 11.518, 12.350],
                              [12.304, 12.350, 14.333]])

optimizer = Optimizer(model_costs, covariance_matrix)
opt_result = optimizer.optimize(algorithm="mfmc", target_cost=1000)

print("Optimal variance: ", opt_result.variance)
print("# samples per model: ", opt_result.allocation.get_number_of_samples_per_model())

For more detailed examples using MXMCPy including end-to-end construction of estimators, see the scripts in the examples directory.

Contributing

1. Fork it (https://github.com/nasa/mxmcpy/fork)
2. Create your feature branch (git checkout -b feature/fooBar)
3. Commit your changes (git commit -am 'Add some fooBar')
4. Push to the branch (git push origin feature/fooBar)
5. Create a new Pull Request

Versioning

Authors

• Geoffrey Bomarito
• James Warner
• Patrick Leser
• William Leser
• Luke Morrill

License

Notices: Copyright 2020 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. No copyright is claimed in the United States under Title 17, U.S. Code. All Other Rights Reserved.

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