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.
- pyDOE
- GPy (version 1.9.2, mandatory)
- matplotlib(version 2.0.0, best vizualization)
- seaborn (version 0.7.1, best vizualization)
- tqdm
- emcee
To install the package do the following: pip install git+git://github.com/piyushpandita92/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.
More documentation to follow. If you have any questions about pydes, contact me at piyush100078@gmail.com .