rnaglib.tasks.InverseFolding

class rnaglib.tasks.InverseFolding(root, splitter=None, **kwargs)[source]
__init__(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