arrayloaders.io.ClassificationDataModule

class arrayloaders.io.ClassificationDataModule(adata_train, adata_val, label_column, train_dataloader_kwargs=None, val_dataloader_kwargs=None, n_chunks=8, dask_scheduler='threads')

Bases: LightningDataModule

A LightningDataModule for classification tasks using arrayloaders.io.DaskDataset.

Parameters:

  • adata_train (AnnData | None) – anndata.AnnData object containing the training data.

  • adata_val (AnnData | None) – anndata.AnnData object containing the validation data.

  • label_column (str) – Name of the column in obs that contains the target values.

  • train_dataloader_kwargs (default: None) – Additional keyword arguments passed to the torch DataLoader for the training dataset.

  • val_dataloader_kwargs (default: None) – Additional keyword arguments passed to the torch DataLoader for the validation dataset.

  • n_chunks (int, default: 8) – Number of chunks of the underlying dask.array to load at a time. Loading more chunks at a time can improve performance and randomness, but increases memory usage.

  • dask_scheduler (Literal['synchronous', 'threads'], default: 'threads') – The Dask scheduler to use for parallel computation. Use “synchronous” for single-threaded execution or “threads” for multithreaded execution.

Examples

>>> from arrayloaders.io.datamodules import ClassificationDataModule
>>> from arrayloaders.io.dask_loader import read_lazy_store
>>> adata_train = read_lazy_store("path/to/train/store", obs_columns=["label"])
>>> adata_train.obs["y"] = adata_train.obs["label"].cat.codes.to_numpy().astype("i8")
>>> datamodule = ClassificationDataModule(
...     adata_train=adata_train,
...     adata_val=None,
...     label_column="label",
...     train_dataloader_kwargs={
...         "batch_size": 2048,
...         "drop_last": True,
...         "num_workers": 4
...     },
... )
>>> train_loader = datamodule.train_dataloader()

Attributes

CHECKPOINT_HYPER_PARAMS_KEY = 'datamodule_hyper_parameters'
CHECKPOINT_HYPER_PARAMS_NAME = 'datamodule_hparams_name'
CHECKPOINT_HYPER_PARAMS_TYPE = 'datamodule_hparams_type'
property hparams: AttributeDict | MutableMapping

The collection of hyperparameters saved with save_hyperparameters(). It is mutable by the user. For the frozen set of initial hyperparameters, use hparams_initial.

Returns:

Mutable hyperparameters dictionary

property hparams_initial: AttributeDict

The collection of hyperparameters saved with save_hyperparameters(). These contents are read-only. Manual updates to the saved hyperparameters can instead be performed through hparams.

Returns:

AttributeDict – immutable initial hyperparameters

name: Optional[str] = None

Class methods

classmethod from_datasets(train_dataset=None, val_dataset=None, test_dataset=None, predict_dataset=None, batch_size=1, num_workers=0, **datamodule_kwargs)

Create an instance from torch.utils.data.Dataset.

Parameters:

  • train_dataset (Dataset | Iterable[Dataset] | None, default: None) – Optional dataset or iterable of datasets to be used for train_dataloader()

  • val_dataset (Dataset | Iterable[Dataset] | None, default: None) – Optional dataset or iterable of datasets to be used for val_dataloader()

  • test_dataset (Dataset | Iterable[Dataset] | None, default: None) – Optional dataset or iterable of datasets to be used for test_dataloader()

  • predict_dataset (Dataset | Iterable[Dataset] | None, default: None) – Optional dataset or iterable of datasets to be used for predict_dataloader()

  • batch_size (int, default: 1) – Batch size to use for each dataloader. Default is 1. This parameter gets forwarded to the __init__ if the datamodule has such a name defined in its signature.

  • num_workers (int, default: 0) – Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process. Number of CPUs available. This parameter gets forwarded to the __init__ if the datamodule has such a name defined in its signature.

  • **datamodule_kwargs (Any) – Additional parameters that get passed down to the datamodule’s __init__.

Return type:

LightningDataModule

Methods

train_dataloader()
val_dataloader()
state_dict()

Called when saving a checkpoint, implement to generate and save datamodule state.

Return type:

dict[str, Any]

Returns:

A dictionary containing datamodule state.

load_state_dict(state_dict)

Called when loading a checkpoint, implement to reload datamodule state given datamodule state_dict.

Parameters:

state_dict (dict[str, Any]) – the datamodule state returned by state_dict.

Return type:

None

on_exception(exception)

Called when the trainer execution is interrupted by an exception.

Return type:

None

prepare_data()

Use this to download and prepare data. Downloading and saving data with multiple processes (distributed settings) will result in corrupted data. Lightning ensures this method is called only within a single process, so you can safely add your downloading logic within. :rtype: None

Warning

DO NOT set state to the model (use setup instead) since this is NOT called on every device

Example:

def prepare_data(self):
    # good
    download_data()
    tokenize()
    etc()

    # bad
    self.split = data_split
    self.some_state = some_other_state()

In a distributed environment, prepare_data can be called in two ways (using prepare_data_per_node)

  1. Once per node. This is the default and is only called on LOCAL_RANK=0.

  2. Once in total. Only called on GLOBAL_RANK=0.

Example:

# DEFAULT
# called once per node on LOCAL_RANK=0 of that node
class LitDataModule(LightningDataModule):
    def __init__(self):
        super().__init__()
        self.prepare_data_per_node = True


# call on GLOBAL_RANK=0 (great for shared file systems)
class LitDataModule(LightningDataModule):
    def __init__(self):
        super().__init__()
        self.prepare_data_per_node = False

This is called before requesting the dataloaders:

model.prepare_data()
initialize_distributed()
model.setup(stage)
model.train_dataloader()
model.val_dataloader()
model.test_dataloader()
model.predict_dataloader()
setup(stage)

Called at the beginning of fit (train + validate), validate, test, or predict. This is a good hook when you need to build models dynamically or adjust something about them. This hook is called on every process when using DDP.

Parameters:

stage (str) – either 'fit', 'validate', 'test', or 'predict'

Return type:

None

Example:

class LitModel(...):
    def __init__(self):
        self.l1 = None

    def prepare_data(self):
        download_data()
        tokenize()

        # don't do this
        self.something = else

    def setup(self, stage):
        data = load_data(...)
        self.l1 = nn.Linear(28, data.num_classes)
teardown(stage)

Called at the end of fit (train + validate), validate, test, or predict.

Parameters:

stage (str) – either 'fit', 'validate', 'test', or 'predict'

Return type:

None

test_dataloader()

An iterable or collection of iterables specifying test samples.

For more information about multiple dataloaders, see this section.

For data processing use the following pattern: :rtype: Any

However, the above are only necessary for distributed processing.

Warning

do not assign state in prepare_data

Note

Lightning tries to add the correct sampler for distributed and arbitrary hardware. There is no need to set it yourself.

Note

If you don’t need a test dataset and a test_step(), you don’t need to implement this method.

predict_dataloader()

An iterable or collection of iterables specifying prediction samples.

For more information about multiple dataloaders, see this section.

It’s recommended that all data downloads and preparation happen in prepare_data().

Note

Lightning tries to add the correct sampler for distributed and arbitrary hardware There is no need to set it yourself.

Return type:

Any

Returns:

A torch.utils.data.DataLoader or a sequence of them specifying prediction samples.

transfer_batch_to_device(batch, device, dataloader_idx)

Override this hook if your DataLoader returns tensors wrapped in a custom data structure.

The data types listed below (and any arbitrary nesting of them) are supported out of the box:

  • torch.Tensor or anything that implements .to(...)

  • list

  • dict

  • tuple

For anything else, you need to define how the data is moved to the target device (CPU, GPU, TPU, …).

Note

This hook should only transfer the data and not modify it, nor should it move the data to any other device than the one passed in as argument (unless you know what you are doing). To check the current state of execution of this hook you can use self.trainer.training/testing/validating/predicting so that you can add different logic as per your requirement.

Parameters:

  • batch (Any) – A batch of data that needs to be transferred to a new device.

  • device (device) – The target device as defined in PyTorch.

  • dataloader_idx (int) – The index of the dataloader to which the batch belongs.

Return type:

Any

Returns:

A reference to the data on the new device.

Example:

def transfer_batch_to_device(self, batch, device, dataloader_idx):
    if isinstance(batch, CustomBatch):
        # move all tensors in your custom data structure to the device
        batch.samples = batch.samples.to(device)
        batch.targets = batch.targets.to(device)
    elif dataloader_idx == 0:
        # skip device transfer for the first dataloader or anything you wish
        pass
    else:
        batch = super().transfer_batch_to_device(batch, device, dataloader_idx)
    return batch

See also

  • move_data_to_device()

  • apply_to_collection()

on_before_batch_transfer(batch, dataloader_idx)

Override to alter or apply batch augmentations to your batch before it is transferred to the device.

Note

To check the current state of execution of this hook you can use self.trainer.training/testing/validating/predicting so that you can add different logic as per your requirement.

Parameters:

  • batch (Any) – A batch of data that needs to be altered or augmented.

  • dataloader_idx (int) – The index of the dataloader to which the batch belongs.

Return type:

Any

Returns:

A batch of data

Example:

def on_before_batch_transfer(self, batch, dataloader_idx):
    batch['x'] = transforms(batch['x'])
    return batch

See also

on_after_batch_transfer(batch, dataloader_idx)

Override to alter or apply batch augmentations to your batch after it is transferred to the device.

Note

To check the current state of execution of this hook you can use self.trainer.training/testing/validating/predicting so that you can add different logic as per your requirement.

Parameters:

  • batch (Any) – A batch of data that needs to be altered or augmented.

  • dataloader_idx (int) – The index of the dataloader to which the batch belongs.

Return type:

Any

Returns:

A batch of data

Example:

def on_after_batch_transfer(self, batch, dataloader_idx):
    batch['x'] = gpu_transforms(batch['x'])
    return batch

See also

save_hyperparameters(*args, ignore=None, frame=None, logger=True)

Save arguments to hparams attribute.

Parameters:

  • args (Any) – single object of dict, NameSpace or OmegaConf or string names or arguments from class __init__

  • ignore (Sequence[str] | str | None, default: None) – an argument name or a list of argument names from class __init__ to be ignored

  • frame (FrameType | None, default: None) – a frame object. Default is None

  • logger (bool, default: True) – Whether to send the hyperparameters to the logger. Default: True

Return type:

None

Example::
>>> from lightning.pytorch.core.mixins import HyperparametersMixin
>>> class ManuallyArgsModel(HyperparametersMixin):
...     def __init__(self, arg1, arg2, arg3):
...         super().__init__()
...         # manually assign arguments
...         self.save_hyperparameters('arg1', 'arg3')
...     def forward(self, *args, **kwargs):
...         ...
>>> model = ManuallyArgsModel(1, 'abc', 3.14)
>>> model.hparams
"arg1": 1
"arg3": 3.14

>>> from lightning.pytorch.core.mixins import HyperparametersMixin
>>> class AutomaticArgsModel(HyperparametersMixin):
...     def __init__(self, arg1, arg2, arg3):
...         super().__init__()
...         # equivalent automatic
...         self.save_hyperparameters()
...     def forward(self, *args, **kwargs):
...         ...
>>> model = AutomaticArgsModel(1, 'abc', 3.14)
>>> model.hparams
"arg1": 1
"arg2": abc
"arg3": 3.14

>>> from lightning.pytorch.core.mixins import HyperparametersMixin
>>> class SingleArgModel(HyperparametersMixin):
...     def __init__(self, params):
...         super().__init__()
...         # manually assign single argument
...         self.save_hyperparameters(params)
...     def forward(self, *args, **kwargs):
...         ...
>>> model = SingleArgModel(Namespace(p1=1, p2='abc', p3=3.14))
>>> model.hparams
"p1": 1
"p2": abc
"p3": 3.14

>>> from lightning.pytorch.core.mixins import HyperparametersMixin
>>> class ManuallyArgsModel(HyperparametersMixin):
...     def __init__(self, arg1, arg2, arg3):
...         super().__init__()
...         # pass argument(s) to ignore as a string or in a list
...         self.save_hyperparameters(ignore='arg2')
...     def forward(self, *args, **kwargs):
...         ...
>>> model = ManuallyArgsModel(1, 'abc', 3.14)
>>> model.hparams
"arg1": 1
"arg3": 3.14