Deeply learning 3D-aware node embeddings for RNA secondary structure graphs.

Learn unsupervised node embeddings using method from https://arxiv.org/abs/1905.12265

To train model on RNA graphs in ./directory:

First preprocess graphs (compute angles and one-hot features from the rna_classes features) by

python data_processing/process_graphs.py -i [directory] -o [preprocessed_dir]

Then learn embeddings using context prediction by running

python pretrain_context.py --train_dir [preprocessed_dir] 

Default values for context prediction hyperparams are K=1, r1 = 1, r2=2. (K,r1,r2) can be changed by adding arguments

python train.py --train_dir [preprocessed_dir] --K ... --r1 ... --r2 ...

To compute embeddings for graphs in ./directory and save them to a new dir, run

python embeddings.py -i [directory] -o [write_directory]

An example of how to use a saved model to warm-start embeddings with the pretrained embeddings is given in tasks/train_mg.py