ALBO is an experimental application of Augmented Lagrangians for constrained Bayesian Optimization implemented in BoTorch

Toy problem from Gramacy et al. 2016

Simulation 1 from Gardner et al. 2014

Algorithm

See report draft for details

Installation

git clone https://github.com/stys/albo
cd albo
conda env create -f environment.yml
conda activate albo
pip install -e .

Usage

This package provides alternatives to ConstrainedMCObjective for constrained optimization with Ax and TorchModelBridge.

Step 1: choose one of available ALBO objectives, i.e. ClassicAlboMCObjective

from albo.objective import ClassicAlboMCObjective

Step 2: create an instance AlboAcquisitionFactory which implements the inner loop of ALBO algorithm and use it to combine ALBO objective with one of the common acquisition functions. Use qEI for explorative phase of optimization and then use qSR for a few steps to drill to the optimal point (usually at the border of the constraints).

from albo.acquisition import AlboAcquisitionFactory

acqf_constructor = AlboAcquisitionFactory(
    albo_objective_constructor=ClassicAlboMCObjective,
    acquisition_function_name=`qEI`,
    bounds=bounds,
    init_mults=init_mults,
    init_penalty_rate=penalty_rate,
    num_iter=num_iter_inner,
    num_restarts=1
)

Step 3: use this instance as a custom acquisition function constructor with TorchModelBridge

from ax.modelbridge.factory import get_botorch
from ax.models.torch.botorch_defaults import scipy_optimizer

model = get_botorch(
    experiment=experiment,
    search_space=search_space,
    data=exp.fetch_data(),
    acqf_constructor=acqf_constructor,
    acqf_optimizer=partial(scipy_optimizer, method="L-BFGS-B")
)

See examples of full optimization loops: