ssl

A module with higher-order data modules for semi-supervised learning.

SemiSupervisedDataModule

Bases: LightningDataModule

A higher-order data module used for semi-supervised learning with a labeled data module and an unlabeled one. It makes sure that both data modules come from the same sub-dataset.

Examples:

>>> import rul_datasets
>>> fd1 = rul_datasets.CmapssReader(fd=1, window_size=20, percent_fail_runs=0.5)
>>> fd1_complement = fd1.get_complement(percent_broken=0.8)
>>> labeled = rul_datasets.RulDataModule(fd1, 32)
>>> unlabeled = rul_datasets.RulDataModule(fd1_complement, 32)
>>> dm = rul_datasets.SemiSupervisedDataModule(labeled, unlabeled)
>>> dm.prepare_data()
>>> dm.setup()
>>> train_ssl = dm.train_dataloader()
>>> val = dm.val_dataloader()
>>> test = dm.test_dataloader()

__init__(labeled, unlabeled)

Create a new semi-supervised data module from a labeled and unlabeled RulDataModule.

The both data modules are checked for compatability (seeRulDataModule). These checks include that the fd match between them.

Parameters:

Name	Type	Description	Default
labeled	RulDataModule	The data module of the labeled dataset.	required
unlabeled	RulDataModule	The data module of the unlabeled dataset.	required

prepare_data(*args, **kwargs)

Download and pre-process the underlying data.

This calls the prepare_data function for source and target domain. All previously completed preparation steps are skipped. It is called automatically by pytorch_lightning and executed on the first GPU in distributed mode.

Parameters:

Name	Type	Description	Default
*args	Any	Passed down to each data module's prepare_data function.	()
**kwargs	Any	Passed down to each data module's prepare_data function..	{}

setup(stage=None)

Load labeled and unlabeled data into memory.

Parameters:

Name	Type	Description	Default
stage	Optional[str]	Passed down to each data module's setup function.	None

test_dataloader(*args, **kwargs)

Create a data loader of the labeled test data.

Parameters:

Name	Type	Description	Default
*args	Any	Ignored. Only for adhering to parent class interface.	()
**kwargs	Any	Ignored. Only for adhering to parent class interface.	{}

Returns:

Type	Description
DataLoader	The labeled test data loader.

train_dataloader(*args, **kwargs)

Create a data loader of an AdaptionDataset using labeled and unlabeled.

The data loader is configured to shuffle the data. The pin_memory option is activated to achieve maximum transfer speed to the GPU.

Parameters:

Name	Type	Description	Default
*args	Any	Ignored. Only for adhering to parent class interface.	()
**kwargs	Any	Ignored. Only for adhering to parent class interface.	{}

Returns:

Type	Description
DataLoader	The training data loader

val_dataloader(*args, **kwargs)

Create a data loader of the labeled validation data.

Parameters:

Name	Type	Description	Default
*args	Any	Ignored. Only for adhering to parent class interface.	()
**kwargs	Any	Ignored. Only for adhering to parent class interface.	{}

Returns:

Type	Description
DataLoader	The labeled validation data loader.