Bayesian Optimization Using Dragonfly

Dragonfly is a Python library that provides a flexible and scalable framework for implementing Bayesian optimization. It offers various features and algorithms for optimizing complex black box functions with a limited budget for function evaluations.

Implementation of Bayesian optimization in Dragonfly API

Here’s an overview of implementing Bayesian optimization using Dragonfly:

1. Defining the objective function: We start by defining the objective function that we want to optimize. The objective function represents the black box function we want to maximize or minimize. It takes input parameters and returns an objective value.

2. Defining the search space: We specify the search space for the input parameters of the objective function. The search space defines the range or constraints for each parameter. Dragonfly supports both continuous and categorical parameter types.

3. Creating the optimization domain: We create an optimization domain object in Dragonfly using the defined search space. The optimization domain encapsulates the search space and provides a unified interface for optimization algorithms.

4. Defining termination criteria: We define termination criteria to stop the optimization loop. This can be a maximum number of iterations, a budget of function evaluations, or a convergence threshold.

5. Retrieving the best configuration: We retrieve the value of the function and the corresponding features.

6. Analyzing and interpreting results: We analyze the optimization results, including the best configuration found, the corresponding objective value, and any additional insights or trade-offs discovered during the optimization process.

Dragonfly stands out as a versatile and adaptable framework tailored for the implementation of Bayesian optimization. This framework offers an intuitive, user-friendly interface and a modular structure, enabling users to finely customize essential components, such as the acquisition function, surrogate model, and optimization domain. This level of flexibility empowers practitioners to shape the optimization process to precisely align with the unique requirements and intricacies of their specific problem domains. With Dragonfly, we are not confined to a one-size-fits-all approach; instead, we have the freedom to craft a highly personalized and effective optimization strategy that delivers results tailored to our needs.

Implementing in code

Let’s look at an example implementation in Python: