lightning_uq_box.datamodules

Toy Datamodules

class lightning_uq_box.datamodules.ToyHeteroscedasticDatamodule(x_min: int | float = -4, x_max: int | float = 4, n_points: int = 250, batch_size: int = 100, test_fraction: float = 0.2, val_fraction: float = 0.1, calib_fraction: float = 0.4, generate_y: ~collections.abc.Callable = <function noisy_sine>, noise_seed: int = 42, split_seed: int = 42, invert: bool = False)

Implement Toy Dataset with heteroscedastic noise.

__init__(x_min: int | float = -4, x_max: int | float = 4, n_points: int = 250, batch_size: int = 100, test_fraction: float = 0.2, val_fraction: float = 0.1, calib_fraction: float = 0.4, generate_y: ~collections.abc.Callable = <function noisy_sine>, noise_seed: int = 42, split_seed: int = 42, invert: bool = False) → None

Define a heteroscedastic toy regression dataset.

Inspired by (https://mapie.readthedocs.io/en/latest/examples_regression/ 1-quickstart/plot_heteroscedastic_1d_data.html#sphx-glr-examples- regression-1-quickstart-plot-heteroscedastic-1d-data-py)

Split n_points data points into train, validation and test set. We provide the following arrays: X_<type>, Y_<type>, where <type> is one of:

• all: train + val + test

• train: all - test - val

• test: see test_fraction

• val: validation, see val_fraction

• calib: calibration, see calib_fraction

• gtext: noise-free ground truth on extended x-axis

X and Y have shape (n_points_{type}, 1).

Parameters:

• x_min – Minimum value of x range

• x_max – Maximum value of x range

• n_points – Number of train + test + validation points

• batch_size – batch size for data loader

• test_fraction – fraction of n_points for test set

• val_fraction – fraction of n_points for validation set

• calib_fraction – fraction of n_points for calibration set will be split from validation set and necessary for conformal prediction

• generate_y – ground truth function with noise option, must have signature f(x, noise: bool)

• noise_seed – random seed for x points positions and y noise

• split_seed – random seed for train/test/val split

• invert – whether to model the inverse problem, swaps X and Y in the DataLoader and variables

train_dataloader() → DataLoader

Return train dataloader.

val_dataloader() → DataLoader

Return val dataloader.

calib_dataloader() → DataLoader

Return calibration dataloader.

test_dataloader() → DataLoader

Return test dataloader.

gt_dataloader() → DataLoader

Return test dataloader.

Image Regression Datamodules

class lightning_uq_box.datamodules.ToyImageRegressionDatamodule(batch_size: int = 10)

Toy Image Regression Datamodule for Testing.

__init__(batch_size: int = 10) → None

Initialize a new instance of Toy Image Regression Datamodule.

Parameters:

batch_size – batch size

train_dataloader() → DataLoader

Return Train Dataloader.

val_dataloader() → DataLoader

Return Val Dataloader.

calib_dataloader() → DataLoader

Return Calib Dataloader.

test_dataloader() → DataLoader

Return Test Dataloader.