This code is implemented according to paper "Scalable and Parallel Deep Bayesian Optimization on Attributed Graphs", accepted by the IEEE Transactions on Neural Networks and Learning Systems (TNNLS). DGBO/PDGBO methods can deal with attributed graphs. It prevents the cubical complexity of the GPs by adopting a deep graph neural network to surrogate black-box functions, and can scale linearly with the number of observations. Applications include molecular discovery and urban road network design.
Two deep surrogate models are contained in this code, i.e., "DGBO_GC-batch" is the DGBO with Graph Convolution (DGBO_{GC}), and "DGBO_GN-batch" is the DGBO with Graph Networks (DGBO_{GN}).
If you want to run this code, you should ensure that you have installed the following packages:
tensorFlow
spicy
pickle
numpy
emcee
networkx
#for graphNet
graph_nets
sonnet
#optional packages:
sklearn
bayes_opt ##via "pip install bayesian-optimization" to install. The website of this package is https://github.com/fmfn/BayesianOptimization
(Please see the "dependency_packages.md" in their respective folders to find the detailed versions of these packages.)
After you installed all dependency packages, choose and go into "DGBO_GC-batch" or "DGBO_GN-batch" folder, and then you can run DGBO with the default setting as:
$$ python DGBO.py --run=True
, or you can see the help message by running as:
$$ python DGBO.py -h
Note: If you try the zinc dataset, you should run “genConvMolFeatures.py” in “rdkit_preprocessing/” to convert SMILES strings to attributed graphs including xxx-attr.pkl, xxx-graph.pkl, and xxx-label.pkl.