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:

Multi-output Modeling¶

Depending on the use case at hand, it may be necessary to model multiple output variables simultaneously. However, not all surrogate types natively provide (joint) predictive distributions for more than one variable, as indicated by their supports_multi_output attribute.

In multi-output contexts, it may therefore be necessary to assemble several single-output surrogates into a composite model to build a joint predictive model from independent components for each output. BayBE provides two convenient mechanisms to achieve this, both built upon the CompositeSurrogate class:

Surrogate Replication¶

The simplest way to construct a multi-output surrogate is to replicate a given single-output model architecture for each of the existing output dimensions.

To replicate a given surrogate, you can either call its replicate() method or use the CompositeSurrogate.from_replication() convenience constructor:

from baybe.surrogates import CompositeSurrogate, GaussianProcessSurrogate

composite_a = GaussianProcessSurrogate().replicate()
composite_b = CompositeSurrogate.from_replication(GaussianProcessSurrogate())

assert composite_a == composite_b

However, there are very few cases where such an explicit conversion is required. Because using a single-output surrogate model in a multi-output context would trivially fail, and because BayBE cares deeply about its users’ lives, it automatically performs this conversion for you behind the scenes:

Auto-Replication

When using a single-output surrogate model in a multi-output context, BayBE automatically replicates the surrogate on the fly.

The consequence of the above is that you can use the same model object regardless of the modeling context and its multi-output capabilities.

There is one notable exception where an explicit replication may still make sense: if you want to bypass the existing multi-output mechanics of a surrogate that is inherently multi-output compatible.

Composite Surrogates¶

An alternative to surrogate replication is to manually assemble your CompositeSurrogate. This can be useful if you want to

use the same model architecture but with different settings for each output or
use different architectures for the outputs to begin with.

from baybe.surrogates import (
    CompositeSurrogate,
    GaussianProcessSurrogate,
    RandomForestSurrogate,
)

surrogate = CompositeSurrogate(
    {
        "target_a": GaussianProcessSurrogate(),
        "target_b": RandomForestSurrogate(),
    }
)

A noticeable difference to the replication approach is that manual assembly requires the exact set of target variables to be known at the time the object is created.

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 directly 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.