baseline

Higher-order data modules to establish a baseline for transfer learning and domain adaption experiments.

BaselineDataModule

Bases: LightningDataModule

A higher-order data module that takes a RulDataModule. It provides the training and validation splits of the sub-dataset selected in the underlying data module but provides the test splits of all available subsets of the dataset. This makes it easy to evaluate the generalization of a supervised model on all sub-datasets.

Examples:

>>> import rul_datasets
>>> cmapss = rul_datasets.reader.CmapssReader(fd=1)
>>> dm = rul_datasets.RulDataModule(cmapss, batch_size=32)
>>> baseline_dm = rul_datasets.BaselineDataModule(dm)
>>> baseline_dm.prepare_data()
>>> baseline_dm.setup()
>>> train_fd1 = baseline_dm.train_dataloader()
>>> val_fd1 = baseline_dm.val_dataloader()
>>> test_fd1, test_fd2, test_fd3, test_fd4 = baseline_dm.test_dataloader()

__init__(data_module)

Create a new baseline data module from a RulDataModule.

It will provide a data loader of the underlying data module's training and validation splits. Additionally, it provides a data loader of the test split of all sub-datasets.

The data module keeps the configuration made in the underlying data module. The same configuration is then passed on to create RulDataModules for all sub-datasets, beside percent_fail_runs and percent_broken.

Parameters:

Name	Type	Description	Default
data_module	RulDataModule	the underlying RulDataModule	required

prepare_data(*args, **kwargs)

Download and pre-process the underlying data.

This calls the prepare_data function for all sub-datasets. 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 all splits as tensors into memory.

Parameters:

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

test_dataloader(*args, **kwargs)

Return data loaders for all sub-datasets.

Parameters:

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

Returns:

Type	Description
List[DataLoader]	The test dataloaders of all sub-datasets.

train_dataloader(*args, **kwargs)

See rul_datasets.core.RulDataModule.train_dataloader.

val_dataloader(*args, **kwargs)

See rul_datasets.core.RulDataModule.val_dataloader.