Dataloading now also loads the bond types, since it stores only the bond types for the edges present, this shouldn't take that much more memory.
A new encoder, which now predicts the node identity as well as the bond types of the original molecule.
To use with semi supervised net, simply replace the decoder type used to initialize SemiSupervisedNet
decoder_with_edge = pinot.generative.decoder.EdgeDecoder <--- Use the new type of decoder
representation = SequentialMix(architecture)
net = SemiSupervisedNet(
representation=representation,
decoder=decoder_with_edge, <---- And replace it here
output_regressor=output_regressor,
embedding_dim=embedding_dim,
generative_hidden_dim=generative_hidden_dim,
unsup_scale=1 #float(len(train_labeled))/len(unlabeled), # <------ if unsup_scale = 0., reduces to supervised model
)
It seems to bring marginal performance to ESOL
Expepriment setting:
architecture = [GraphConv, 128, GraphConv, 128]
No unlabeled data
Dataloading now also loads the bond types, since it stores only the bond types for the edges present, this shouldn't take that much more memory.
A new encoder, which now predicts the node identity as well as the bond types of the original molecule.
To use with semi supervised net, simply replace the decoder type used to initialize
SemiSupervisedNetIt seems to bring marginal performance to ESOL
Expepriment setting:
architecture = [GraphConv, 128, GraphConv, 128] No unlabeled data