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This is a constrained global optimization package built upon bayesian inference and gaussian process, that attempts to find the maximum value of an unknown function in as few iterations as possible. This technique is particularly suited for optimization of high cost functions, situations where the balance between exploration and exploitation is important. More detailed information, other advanced features, and tips on usage/implementation can be found in the examples folder. Follow the basic tour notebook to learn how to use the package's most important features. Take a look at the advanced tour notebook to learn how to make the package more flexible, how to deal with categorical parameters, how to use observers, and more. Explore the options exemplifying the balance between exploration and exploitation and how to control it. Explore the domain reduction notebook to learn more about how search can be sped up by dynamically changing parameters' bounds.
Features
- Bayesian optimization works by constructing a posterior distribution of functions
- As you iterate over and over, the algorithm balances its needs of exploration and exploitation taking into account what it knows about the target function
- At each step a Gaussian Process is fitted to the known samples (points previously explored), and the posterior distribution,
- This process is designed to minimize the number of steps required to find a combination of parameters that are close to the optimal combination
- Bayesian Optimization is most adequate for situations where sampling the function to be optimized is a very expensive endeavor
- This is a function optimization package, therefore the first and most important ingredient is, of course, the function to be optimized
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