This is not an official Google Product.
Code for NVP model over Graphs. Only Preliminary results yet. Thanks Yujia Li for providing the code for parsing data files.
Requirements:
- Tensorflow (Please see [http://www.tensorflow.org] for how to install/upgrade)
- Numpy (Please see [http://www.numpy.org])
- Tensor2Tensor (Please see [https://github.com/tensorflow/tensor2tensor] for how to install Tensor2Tensor)
- Scikit-Learn (Please see [http://scikit-learn.org/stable/developers/advanced_installation.html] for more details)
Download the Processed versions of the Zinc and ChEMBL datasets from the links below. Again, Credits to Yujia Li for helping us with these datasets.
- ChEMBL: ftp://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_23/chembl_23_chemreps.txt.gz\
- Zinc: https://github.com/mkusner/grammarVAE/tree/master/data
In order to run the model, download the data from the sources provided above and run it as
python trainer.py --exp_dir=<directory for experiments> --batch_size=100 --dataset=<zinc/max20> --train_steps=50000 --sample=<True/False whether run sampling or training> --use_BN=<True/False> --only_nodes=False --perturb_data=<True/False> --use_edge_features=<True/False>\
Some important points to take care of:
- Please set --sample=True, if you want to start from some samples in the latent space and transform them into actual graphs.
- Perturbing data in the data space can be done by setting the flag --perturb_data=True.
- The code currently defaults to using beta prior for the adjcacency matrix as well as the node features.
- Use the flag --use_edge_features=True to indicate that you also want to generate edge features apart from the adjacency matrix.
Maintained by Kevin Swersky. Work done by Aviral Kumar, Kevin Swersky (kswersky@google.com) and Jimmy Ba (jimmylb@google.com). For any questions, please contact Kevin Swersky (Github username: kswersky)