scvi.dataloaders.AnnDataLoader#
- class scvi.dataloaders.AnnDataLoader(adata_manager, shuffle=False, indices=None, batch_size=128, sampler=None, data_and_attributes=None, drop_last=False, iter_ndarray=False, **data_loader_kwargs)[source]#
DataLoader for loading tensors from AnnData objects.
- Parameters
adata_manager (
AnnDataManager
) –AnnDataManager
object with a registered AnnData object.shuffle (
bool
(default:False
)) – Whether the data should be shuffledindices (
Union
[Sequence
[int
],Sequence
[bool
],None
] (default:None
)) – The indices of the observations in the adata to loadbatch_size (
int
(default:128
)) – minibatch size to load each iterationsampler (
Optional
[Sampler
] (default:None
)) – Defines the strategy to draw samples from the dataset. Can be any Iterable with __len__ implemented. If specified, shuffle must not be specified. By default, we use a custom sampler that is designed to get a minibatch of data with one call to __getitem__.data_and_attributes (
Union
[List
[str
],Dict
[str
,dtype
],None
] (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) or list of such keys. A list can be used to subset to certain keys in the event that more tensors than needed have been registered. IfNone
, defaults to all registered data.iter_ndarray (
bool
(default:False
)) – Whether to iterate over numpy arrays instead of torch tensorsdata_loader_kwargs – Keyword arguments for
DataLoader
Attributes table#
Methods table#
Attributes#
- AnnDataLoader.dataset: Dataset[T_co]#
- AnnDataLoader.batch_size: Optional[int]#
- AnnDataLoader.num_workers: int#
- AnnDataLoader.pin_memory: bool#
- AnnDataLoader.drop_last: bool#
- AnnDataLoader.timeout: float#
- AnnDataLoader.sampler: Union[Sampler, Iterable]#
- AnnDataLoader.pin_memory_device: str#
- AnnDataLoader.prefetch_factor: Optional[int]#