bode

This module is called BODE. Bayesian Optimal Design of Experiments

Bayesian Optimal Design of Experiments for Inferring the Expected Value of a Black-box Function, the following paper: https://arxiv.org/pdf/1807.09979.pdf

It needs the support of the following PYTHON packages.

1. pyDOE
2. GPy (version 1.9.2, mandatory)
3. matplotlib(version 2.0.0, best vizualization)
4. seaborn (version 0.7.1, best vizualization)
5. tqdm
6. emcee

To install the package do the following: pip install git+https://github.com/jhjellison/bode.git

or clone the repository and run python setup.py install.

Import the package like as follows: import bode

The simple examples samp_ex1.py, samp_ex2.py provide a self explanatory overview of using bode. This code works for estimating/inferring the expectation of a function (so the user would have to include that in their function object).

The user mainly needs to specify the objective function obj_func as an object, number of iterations (samples to be collected depending on the budget) max_it, number of designs of the discretized input space (for calculating the value of the EKLD criterion) X_design.

Note: The methodology should be used with the inputs transformed to [0, 1]^{d} cube and outputs roughly normalized to a standard normal.

For sequential design (one suggested design/experiment at a time): Running the code: the examples in the tests directory can be called from the command line with a set of arguments as follows: python tests/samp_ex1.py .

After each iteration a plot depicting the state of the function is generated for 1d problems, this can be controlled by a plots flag set to 0 or 1.

The original version of the code may be found at https://github.com/piyushpandita92/bode.git This fork is currently for quality of life improvments. In the future it aims to improve the running speed and make running 2 (and higher) dimensional datasets easier