This is the source code to go with the book Bayesian Optimization in Action by me, published by Manning.
Bayesian Optimization in Action teaches you how to build Bayesian optimization systems from the ground up. This book transforms state-of-the-art research into usable techniques that you can easily put into practice, all fully illustrated with useful code samples. In it, you’ll hone your understanding of Bayesian optimization through engaging examples—from forecasting the weather, to finding the optimal amount of sugar for coffee, and even deciding if someone is psychic! Along the way, you’ll explore scenarios for when there are multiple objectives, when each decision has its own cost, and when feedback is in the form of pairwise comparisons. With this collection of techniques, you’ll be ready to find the optimal solution for everything from transport and logistics to cancer treatments.
Please use requirements.txt to create a Python environment of your own and install the necessary packages.
Other versions of the required packages could still work but might give slightly different results.
Code is implemented in Jupyter notebooks and is organized by chapters to follow the discussions in the text.