scvi.dataloaders.SemiSupervisedDataLoader#

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

DataLoader that supports semisupervised training.

Parameters:
adata_manager : AnnDataManager

AnnDataManager object that has been created via setup_anndata.

n_samples_per_label : int | NoneOptional[int] (default: None)

Number of subsamples for each label class to sample per epoch. By default, there is no label subsampling.

indices : List[int] | NoneOptional[List[int]] (default: None)

The indices of the observations 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#

check_worker_number_rationality()

resample_labels()

Resamples the labeled data.

subsample_labels()

Subsamples each label class by taking up to n_samples_per_label samples per class.

Attributes#

multiprocessing_context#

SemiSupervisedDataLoader.multiprocessing_context#

dataset#

SemiSupervisedDataLoader.dataset: Dataset[T_co]#

batch_size#

SemiSupervisedDataLoader.batch_size: Optional[int]#

num_workers#

SemiSupervisedDataLoader.num_workers: int#

pin_memory#

SemiSupervisedDataLoader.pin_memory: bool#

drop_last#

SemiSupervisedDataLoader.drop_last: bool#

timeout#

SemiSupervisedDataLoader.timeout: float#

sampler#

SemiSupervisedDataLoader.sampler: Union[Sampler, Iterable]#

prefetch_factor#

SemiSupervisedDataLoader.prefetch_factor: int#

Methods#

check_worker_number_rationality#

SemiSupervisedDataLoader.check_worker_number_rationality()#

resample_labels#

SemiSupervisedDataLoader.resample_labels()[source]#

Resamples the labeled data.

subsample_labels#

SemiSupervisedDataLoader.subsample_labels()[source]#

Subsamples each label class by taking up to n_samples_per_label samples per class.