yucenli/bnn-bo

Bayesian Neural Network Surrogates for Bayesian Optimization

A Study of Bayesian Neural Network Surrogates for Bayesian Optimization

Installation

Create a new conda environment:

conda env create -f environment.yml

Install the project:

pip install -e .

Running experiments

Each experiment requires a config json, and there many examples of config files in config.

To use the config file config/<name>.json, run the following command from the root folder

python main.py --config <name>

You can also include the --bg flag if you would like to redirect stderr and stdout to a different file and save the outputs.

python main.py --config <name> --bg

Code Organization

The Bayesian optimization loop is in main.py

models: the model code for each of the surrogate models we consider.

test_functions: objective functions for benchmark problems

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.

Adding logic to main.py

To add a new test function, we need to modify get_test_function in main.py. This function will parse the string specified in the config file by "test_function" and use it to initialize the test function.

Adding a New Surrogate Model

Defining the model

The core logic for each model lies in the model folder. To add a new surrogate model implementation, add a new class that extends the Model class (model.model). This class extends the botorch.model.model.Model class (documentation).

The two most important functions to implement are posterior, which computes the posterior at the specified points (BoTorch documentation), and fit_and_save, which fits the model to the queried points.

Adding logic to main.py

To run the new surrogate model and compare it with other models, we will need to modify the initialize_model function in main.py in order to parse the config file and initialize the model appropriately.