nirs4all.operators.augmentation.abc_augmenter module

class nirs4all.operators.augmentation.abc_augmenter.Augmenter(apply_on='samples', random_state=None, *, copy=True)[source]

Bases: TransformerMixin, BaseEstimator

Base class for data augmentation transformers.

abstractmethod augment(X, apply_on='samples')[source]

Perform data augmentation.

Parameters:

  • X (array-like) – Input data to augment.

  • apply_on (str) – The level at which augmentation is applied. Can be one of ‘samples’, ‘features’, ‘subsets’, or ‘global’. Defaults to ‘samples’.

Returns:

Augmented data.

Return type:

array-like

fit(X, y=None)[source]

Fit to data.

Parameters:

  • X (array-like) – Input data to fit.

  • y (array-like or None) – Target variable (unused).

Returns:

self – Returns the instance itself.

Return type:

object

fit_transform(X, y=None, **fit_params)[source]

Fit to data and transform it.

Parameters:

  • X (array-like) – Input data to fit and transform.

  • y (array-like or None) – Target variable (unused).

  • **fit_params (dict) – Additional fitting parameters (unused).

Returns:

Transformed data.

Return type:

array-like

transform(X)[source]

Transform the input data by applying data augmentation.

Parameters:

X (array-like) – Input data to transform.

Returns:

Transformed data after augmentation.

Return type:

array-like

class nirs4all.operators.augmentation.abc_augmenter.IdentityAugmenter(apply_on='samples', random_state=None, *, copy=True)[source]

Bases: Augmenter

An augmenter that returns the input data without any changes.

augment(X, _)[source]

Perform identity augmentation.

Parameters:

  • X (array-like) – Input data to augment.

  • _ (str) – Placeholder for unused parameter.

Returns:

Augmented data (same as input data).

Return type:

array-like