rnaglib.dataset_transforms.DefaultBasePairLoader

class rnaglib.dataset_transforms.DefaultBasePairLoader(dataset=None, data_path=None, batch_size=5, inner_batch_size=50, sampler_layers=2, neg_samples=1, num_workers=4, framework='dgl', **kwargs)

Dataloader that yields base pairs

Just a default edge base pair loader that deals with the splits

Parameters:

• dataset – A GraphDataset we want to loop over for base-pair prediction

• data_path – Optionnaly, we can use a data path to create a default GraphDataset

• batch_size – The desired batch size (number of whole graphs)

:param inner_batch_size:The desired inner batch size (number of sampled edge in a batched graph) :type sampler_layers: :param sampler_layers: The size of the neighborhood :type neg_samples: :param neg_samples: The number of negative sample to use per positive ones :type framework: :param framework: which learning framework to use :type num_workers: :param num_workers: The number of cores to use for loading

__init__(dataset=None, data_path=None, batch_size=5, inner_batch_size=50, sampler_layers=2, neg_samples=1, num_workers=4, framework='dgl', **kwargs)

Just a default edge base pair loader that deals with the splits

Parameters:

• dataset – A GraphDataset we want to loop over for base-pair prediction

• data_path – Optionnaly, we can use a data path to create a default GraphDataset

• batch_size – The desired batch size (number of whole graphs)

:param inner_batch_size:The desired inner batch size (number of sampled edge in a batched graph) :type sampler_layers: :param sampler_layers: The size of the neighborhood :type neg_samples: :param neg_samples: The number of negative sample to use per positive ones :type framework: :param framework: which learning framework to use :type num_workers: :param num_workers: The number of cores to use for loading

Methods

__init__([dataset, data_path, batch_size, ...])	Just a default edge base pair loader that deals with the splits
get_data()