Active learning: iteratively expanding the training set

Active learning (AL), also called sequential training, is the iterative choice of additional training samples after the initial training of a surrogate model. The new samples can be chosen in an explorative manner or by exploiting available data and properties of the surrogate.

Warning

The active learning methods are currently being reworked. This should not change the function call Engine.train_sequential(), but will change the associated class structures.

UML diagram for the classes and functions used in active learning in BayesValidRox.

In BayesValidRox AL is realized by additional properties of the bayesvalidrox.surrogate_models.exp_designs.ExpDesigns and bayesvalidrox.surrogate_models.engine.Engine classes without any changes to the surrogate model.

Exploration, exploitation and tradeoff

Exploration methods choose the new samples in a space-filling manner, while exploitation methods make use of available data or properties of the surrogate models, such as the estimated surrogate standard deviation. Exploration methods in BayesValidRox include random or latin-hypercube sampling, voronoi sampling, choice based on leave-one-out cross validation or dual-annealing. Exploitation can be set to Bayesian designs, such as Bayesian3 Active Learning, or variance-based designs.

The tradeoff between exploration and exploitation is defined by tradeoff-schemes, such as an equal split, epsilon-decreaseing or adaptive schemes.

Example

We take the engine from Training surrogate models and change the settings to perform sequential training.