rnaglib.tasks.ChemicalModification

class rnaglib.tasks.ChemicalModification(root, size_thresholds=(15, 500), **kwargs)

Residue-level binary classification task to predict whether a given residue is chemically modified.

__init__(root, size_thresholds=(15, 500), **kwargs)

Methods

__init__(root[, size_thresholds])
add_feature(feature[, feature_level, is_input])	Shortcut to RNADataset.add_feature
add_representation(representation)
add_rna_to_building_list(all_rnas, rna)
compute_distances()
compute_metrics(all_preds, all_probs, all_labels)
compute_one_metric(preds, probs, labels)
create_dataset_from_list(rnas)	Computes an RNADataset object from the
describe()	Get description of task dataset.
dummy_inference()
evaluate(model, loader)
from_zenodo()	Downloads the task dataset from Zenodo and loads it.
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([additional_metadata])	Initialize dictionary to hold key/value pairs to self.metadata.
load()	Load dataset and splits from disk.
post_process()	The most common post_processing steps to remove redundancy.
process()	Tasks must implement this method.
remove_redundancy()
remove_representation(representation_name)
set_datasets([recompute])	Sets the train, val and test datasets Call this each time you modify self.dataset.
set_loaders([recompute])	Sets 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_splitter	The splitter used if no other splitter is specified.
dummy_model
input_var
name
target_var
task_id	Task hash is a hash of all RNA ids and node IDs in the dataset