scvi.dataloaders.AnnbatchDataModule

scvi.dataloaders.AnnbatchDataModule#

class scvi.dataloaders.AnnbatchDataModule(dataset, batch_size=None, batch_key=None, label_key=None, sample_key=None, unlabeled_category='Unknown', model_name='SCVI', train_size=1.0, categorical_covariate_keys=None, continuous_covariate_keys=None, var_names=None, layer=None, chunk_size=256, preload_nchunks=32, preload_to_gpu=True, shuffle=False, class_sampler_key=None, class_weights=None)[source]#

Bases: LightningDataModule

LightningDataModule for annbatch.Loader with full scvi-tools integration.

Parameters:

dataset (annbatch.Loader) – The Loader configured with a DatasetCollection.
batch_size (int, optional) – Not used directly — the Loader already handles batching internally.
batch_key (str, optional) – Column name in obs to use as batch identity.
label_key (str, optional) – Column name in obs to use as labels.
sample_key (str, optional) – Column name in obs to use as sample identity.
unlabeled_category (str, optional) – Value used for unlabeled cells. Default is "Unknown".
model_name (str, optional) – The scvi-tools model name. Default is "SCVI".
train_size (float, optional) – Fraction of data to use for training. Default is 1.0.
categorical_covariate_keys (list of str, optional) – Column names in obs to use as categorical covariates.
continuous_covariate_keys (list of str, optional) – Column names in obs to use as continuous covariates.

Attributes table#

`batch_labels`
`extra_categorical_covs`
`extra_continuous_covs`
`labels_mapping`
`n_batch`
`n_labels`
`n_obs`
`n_obs_per_sample`
`n_samples`
`n_vars`
`registry`
`samples_mapping`
`train_size`	Fraction of data to use for training.
`unlabeled_category`	String assigned to unlabeled cells.

Methods table#

`inference_dataloader`()	Dataloader for inference with `on_before_batch_transfer` applied.
`on_before_batch_transfer`(batch, dataloader_idx)	Convert an annbatch Loader batch to the dictionary required by scvi-tools.
`set_batch_size`([batch_size])	Update the annbatch sampler batch size used for train and validation.
`set_split`([train_size, validation_size, ...])	Configure disjoint train/validation ranges over the same annbatch collection.
`train_dataloader`()	Create the train data loader.
`val_dataloader`()	Create the validation data loader.

Attributes#

AnnbatchDataModule.batch_labels[source]#

AnnbatchDataModule.extra_categorical_covs[source]#

AnnbatchDataModule.extra_continuous_covs[source]#

AnnbatchDataModule.labels_mapping[source]#

AnnbatchDataModule.n_batch[source]#

AnnbatchDataModule.n_labels[source]#

AnnbatchDataModule.n_obs[source]#

AnnbatchDataModule.n_obs_per_sample[source]#

AnnbatchDataModule.n_samples[source]#

AnnbatchDataModule.n_vars[source]#

AnnbatchDataModule.registry[source]#

AnnbatchDataModule.samples_mapping[source]#

AnnbatchDataModule.train_size[source]#: Fraction of data to use for training.

AnnbatchDataModule.unlabeled_category[source]#: String assigned to unlabeled cells.

Methods#

AnnbatchDataModule.inference_dataloader()[source]#: Dataloader for inference with on_before_batch_transfer applied.

AnnbatchDataModule.on_before_batch_transfer(batch, dataloader_idx)[source]#

Convert an annbatch Loader batch to the dictionary required by scvi-tools.

Follows the same encoding pattern as TileDBDataModule and MappedCollectionDataModule.

AnnbatchDataModule.set_batch_size(batch_size=None)[source]#

Update the annbatch sampler batch size used for train and validation.

Return type:: None

AnnbatchDataModule.set_split(train_size=None, validation_size=None, shuffle_set_split=True, batch_size=None)[source]#: Configure disjoint train/validation ranges over the same annbatch collection.

AnnbatchDataModule.train_dataloader()[source]#: Create the train data loader.

AnnbatchDataModule.val_dataloader()[source]#: Create the validation data loader.