MPS is a general and flexible framework adaptive goal oriented design of experiments.
In adaptive design of experiments (DoE), one wishes to design a sequence of experiments and collect data so as to achieve a desired goal. While there are many algorithms for specialised settings for adaptive DoE (such as optimisation, active learning, level set estimation etc.), MPS aims to provide a general framework that encompasses a broad variety of problems, including those mentioned above. To do so, one must specifiy their goal via a reward function. For more details, see our paper.
This library is compatible with Python2 (>= 2.7) and Python3 (>= 3.5) and has been tested on Linux and macOS platforms.
This library can be installed via the following commands.
$ git clone https://github.com/kirthevasank/mps
$ cd mps
$ python setup.py installTesting the installation:
Once done, you may test the installation by importing mps in the python shell.
$ python
$ import mpsGetting started:
To help get started,
we have provided a few example scripts in the
examples directory.
Simply cd examples and run the script using python,
e.g. python al_linear_rbf.py.
Research and development of the methods in this package were funded by the Toyota Research Institute, Accelerated Materials Design & Discovery (AMDD) program.
If you use any part of this code in your work, please cite our ICML 2019 paper.
@inproceedings{kandasamy2019myopic,
title={Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments},
author={Kandasamy, Kirthevasan and Neiswanger, Willie and Zhang, Reed and Krishnamurthy,
Akshay and Schneider, Jeff and Poczos, Barnabas},
booktitle={International Conference on Machine Learning},
pages={3222--3232},
year={2019}
}
This software is released under the MIT license. For more details, please refer LICENSE.txt.
For questions, please email kandasamy@cs.cmu.edu.
"Copyright 2018-2019 Kirthevasan Kandasamy"