`torch_adata._core`¶

Subpackages¶

Submodules¶

torch_adata._core._AnnDataset

Package Contents¶

Classes¶

LightningAnnDataModule

Attributes¶

`__module_name__`
`__doc__`
`__author__`
`__email__`
`__version__`

torch_adata._core.__module_name__ = __init__.py¶

torch_adata._core.__doc__ = core __init__ module.¶

torch_adata._core.__author__¶

torch_adata._core.__email__¶

torch_adata._core.__version__ = 0.0.20¶

class torch_adata._core.LightningAnnDataModule(adata=None, h5ad_path=None, batch_size=2000, num_workers=os.cpu_count(), train_val_split=[0.8, 0.2], n_predict=2000, use_key='X_pca', groupby='Time point', train_key='train', val_key='val', test_key='test', predict_key='predict', shuffle=True, silent=True, **kwargs)¶

Bases: pytorch_lightning.LightningDataModule

property properly_formatted_index¶

property adata¶

property cell_idx¶

property n_cells¶

property n_features¶

property n_dims¶

property data_keys¶

property init_train_adata¶

property train_adata¶

property val_adata¶

property test_adata¶

property predict_adata¶

property AnnDatasetKWARGS¶

property train_dataset¶

property val_dataset¶

property test_dataset¶

property predict_dataset¶

_format_adata_obs_index()¶

_configure_adata()¶: configures the property self.adata

configure_train_val_split()¶

subset_adata(key: str)¶

to_dataset(key: str) → torch.utils.data.Dataset¶: key funciton to transform adata -> torch.utils.data.Dataset

_return_loader(dataset_key)¶

prepare_data()¶

setup(stage)¶

train_dataloader()¶

val_dataloader()¶

test_dataloader()¶

predict_dataloader()¶

__repr__()¶