scvi.dataloaders.ConcatDataLoader#

class scvi.dataloaders.ConcatDataLoader(adata_manager, indices_list, shuffle=False, batch_size=128, data_and_attributes=None, drop_last=False, **data_loader_kwargs)[source]#

DataLoader that supports a list of list of indices to load.

Parameters
adata_manager : AnnDataManager

AnnDataManager object that has been created via setup_anndata.

indices_list : List[List[int]]

List where each element is a list of indices in the adata to load

shuffle : bool (default: False)

Whether the data should be shuffled

batch_size : Optional[int]

minibatch size to load each iteration

data_and_attributes : dict | NoneOptional[dict] (default: None)

Dictionary with keys representing keys in data registry (adata_manager.data_registry) and value equal to desired numpy loading type (later made into torch tensor). If None, defaults to all registered data.

data_loader_kwargs

Keyword arguments for DataLoader

Attributes table#

Methods table#

Attributes#

multiprocessing_context#

ConcatDataLoader.multiprocessing_context#

dataset#

ConcatDataLoader.dataset: torch.utils.data.dataset.Dataset[torch.utils.data.dataloader.T_co]#

batch_size#

ConcatDataLoader.batch_size: Optional[int]#

num_workers#

ConcatDataLoader.num_workers: int#

pin_memory#

ConcatDataLoader.pin_memory: bool#

drop_last#

ConcatDataLoader.drop_last: bool#

timeout#

ConcatDataLoader.timeout: float#

sampler#

ConcatDataLoader.sampler: Union[torch.utils.data.sampler.Sampler, Iterable]#

prefetch_factor#

ConcatDataLoader.prefetch_factor: int#

Methods#

check_worker_number_rationality#

ConcatDataLoader.check_worker_number_rationality()#