Benchmark Functions

Source: Li, Yucen Lily, Tim GJ Rudner, and Andrew Gordon Wilson. "A study of bayesian neural network surrogates for bayesian optimization." arXiv preprint arXiv:2305.20028 (2023).

Installation

Install the project:

pip install -e .

Adding a New Test Function

Defining the function

Our library supports any test function which extends the BaseTestProblem class defined in BoTorch (documentation). This class requires an implementation of the evaluate_true method, which takes in X values and returns the value of the objective function at those values.

For example, in order to specify the objective function $y = x^2$, we can define the following class:

class Toy(BaseTestProblem):
    dim = 1

    def evaluate_true(self, X: Tensor) -> Tensor:
        return torch.pow(X, 2)

Many of the test function we use in the library are defined in the test_functions folder, or directly imported from BoTorch.