Surrogates

Surrogate models are used to model and estimate the unknown objective function of the DoE campaign. BayBE offers a diverse array of surrogate models, while also allowing for the utilization of custom models. All surrogate models are based upon the general Surrogate class. Some models even support transfer learning, as indicated by the supports_transfer_learning attribute.

Available Models

BayBE provides a comprehensive selection of surrogate models, empowering you to choose the most suitable option for your specific needs. The following surrogate models are available within BayBE:

• GaussianProcessSurrogate

• BayesianLinearSurrogate

• MeanPredictionSurrogate

• NGBoostSurrogate

• RandomForestSurrogate

Extracting the Model for Advanced Study

In principle, the surrogate model does not need to be a persistent object during Bayesian optimization since each iteration performs a new fit anyway. However, for advanced study, such as investigating the posterior predictions, acquisition functions or feature importance, it can be useful to diretly extract the current surrogate model.

For this, BayBE provides the get_surrogate method, which is available for the Campaign or for recommenders. Below an example of how to utilize this in conjunction with the popular SHAP package:

# Assuming we already have a campaign created and measurements added
data = campaign.measurements[[p.name for p in campaign.parameters]]
model = lambda x: campaign.get_surrogate().posterior(x).mean

# Apply SHAP
explainer = shap.Explainer(model, data)
shap_values = explainer(data)
shap.plots.bar(shap_values)

Current Scalarization Limitations

Currently, get_surrogate always returns the surrogate model with respect to the transformed target(s) / objective. This means that if you are using a SingleTargetObjective with a transformed target or a DesirabilityObjective, the model’s output will correspond to the transformed quantities and not the original untransformed target(s). If you are using the model for subsequent analysis this should be kept in mind.

Using Custom Models

BayBE goes one step further by allowing you to incorporate custom models based on the ONNX architecture. Note however that these cannot be retrained. For a detailed explanation on using custom models, refer to the comprehensive examples provided in the corresponding example folder.