rnaglib.tasks.gRNAde

class rnaglib.tasks.gRNAde(size_thresholds=(15, 300), **kwargs)

This class is a subclass of InverseFolding and is used to train a model on the gRNAde dataset.

Task type: multi-class classification Task level: residue-level

Parameters:

size_thresholds (tuple[int]) – range of RNA sizes to keep in the task dataset(default (15, 500))

__init__(size_thresholds=(15, 300), **kwargs)

Methods

__init__([size_thresholds])
add_feature(feature[, feature_level, is_input])	Add a feature to the dataset.
add_representation(representation)	Add a representation transform to the dataset.
add_rna_to_building_list(all_rnas, rna)	Add an RNA to the building list.
compute_distances()	Compute similarity distances between RNAs in the dataset.
compute_metrics(all_preds, all_probs, all_labels)	Evaluate model performance on nucleotide prediction task.
compute_one_metric(preds, unfiltered_preds, ...)	Compute classification metrics for a single set of predictions.
create_dataset_from_list(rnas)	Compute an RNADataset object from the lists touched in add_rna_to_building_list.
describe()	Get description of task dataset.
dummy_inference()	Run dummy inference on the test dataset.
evaluate(model, loader)	Evaluate model performance on a dataset.
from_scratch(size_thresholds)	Create task dataset from scratch.
from_zenodo()	Download the task dataset from Zenodo and load it.
get_split_datasets([recompute])	Get train, validation, and test datasets.
get_split_loaders([recompute])	Get train, validation, and test dataloaders.
get_task_vars()	Specifies the FeaturesComputer object of the tasks which defines the features which have to be added to the RNAs (graphs) and nucleotides (graph nodes)
init_metadata([additional_metadata])	Initialize dictionary to hold key/value pairs to self.metadata.
load()	Load dataset and splits from disk.
post_process()	The task-specific post processing steps to remove redundancy and compute distances which will be used by the splitters.
process()
remove_redundancy()	Remove redundant RNAs from the dataset based on similarity.
remove_representation(representation_name)	Remove a representation transform from the dataset.
set_datasets([recompute])	Set the train, val and test datasets.
set_loaders([recompute])	Set the dataloader properties.
split(dataset)	Calls the splitter and returns train, val, test splits.
to_csv(path)	Write a single CSV with all task data.
write()	Save task data and splits to root.

Attributes

default_metric
default_splitter	Returns the splitting strategy to be used for this specific task.
dummy_model	Get a dummy model for testing purposes.
input_var
name
nucs
target_var
task_id	Task hash is a hash of all RNA ids and node IDs in the dataset.
version