learn.pretrained_unsupervised has problems with non-standard data
Hi, I am running a script in which I try to train a model in the same fashion as is done in the second example given in the rnaglib examples. The steps are as follows:
- Firstly, I transform each graph in a repository of .cif files into a new annotated graph in .json format and transfer the .json files into their own repository. The transformation is accomplished using the
do_one()method in the
- Then, I create a GraphDataset object from this repository of .json files and a Loader object from the GraphDataset.
- Finally, I create an embedder and classifier model from the
model.pyscript and make it go through unsupervised and supervised training phases. However, I cannot make it through the unsupervised phase because the graph loader cannot do the node comparison operation because of the following error:
Looking into the problem, inside the
compare method in the
rnaglib.kernels.node_sim.py file, I print out the rings passed as input to this function and find out they are always
None, instead of being a list (as is the case for the preinstalled dataset). I don't know if it's an issue with the initial annotation, the transformation of the dataset into a data loader, or something else...
Any help is appreciated, notably an explanation about what the rings are and what they accomplish on a conceptual level.
Thank you, Séraphin