rnaglib.tasks.InverseFolding¶
- class rnaglib.tasks.InverseFolding(root, splitter=None, **kwargs)[source]¶
-
Methods
__init__
(root[, splitter])evaluate
(model, loader)Evaluate model performance on nucleotide prediction task :type model: <module 'torch.nn' from '/home/docs/checkouts/readthedocs.org/user_builds/rnaglib/envs/latest/lib/python3.8/site-packages/torch/nn/__init__.py'> :param model: The model to evaluate :type loader: :param loader: Data loader to use
get_split_datasets
([recompute])get_split_loaders
([recompute])get_task_vars
()Define a FeaturesComputer object to set which input and output variables will be used in the task.
init_metadata
()Optionally adds some key/value pairs to self.metadata.
load
()Load dataset and splits from disk.
process
()Tasks must implement this method.
set_datasets
()Sets the train, val and test datasets Call this each time you modify
self.dataset
.set_loaders
(**dataloader_kwargs)Sets the dataloader properties.
split
(dataset)Calls the splitter and returns train, val, test splits.
write
()Save task data and splits to root.
Attributes
default_splitter
describe
Get description of task dataset, including dimensions needed for model initialization and other relevant statistics.
dummy_model
input_var
target_var
task_id
Task hash is a hash of all RNA ids and node IDs in the dataset