nirs4all.operators.transforms.features module

class nirs4all.operators.transforms.features.CropTransformer(start: int = 0, end: int = None)

Bases: BaseEstimator, TransformerMixin

fit(X, y=None)

transform(X)

class nirs4all.operators.transforms.features.FlattenPreprocessing(sources: str | int | List[int] = 'all')

Bases: BaseEstimator, TransformerMixin

Flatten the preprocessing dimension of a 3D feature array.

Transforms a 3D array of shape (samples, preprocessings, features) into a 2D array of shape (samples, preprocessings * features) by horizontally concatenating all preprocessing views.

This is useful after feature_augmentation when you want to flatten multiple preprocessing views into a single feature vector for models that expect 2D input.

Parameters:

sources – Which sources to apply the flattening to. - “all” (default): Apply to all sources - List of indices: [0, 2] to apply only to sources 0 and 2 - Single int: Apply to only that source If a source is not in the list, it is passed through unchanged.

Example

>>> # Input: (100, 4, 2151) - 4 preprocessing views of 2151 features each
>>> flattener = FlattenPreprocessing()
>>> output = flattener.transform(X)
>>> # Output: (100, 8604) - 4 * 2151 = 8604 features

>>> # Apply only to specific sources
>>> flattener = FlattenPreprocessing(sources=[0, 2])
>>> # Only sources 0 and 2 will be flattened

Note

• If input is already 2D, it is returned unchanged.

• The transformer is stateless (fit does nothing).

fit(X, y=None)

Fit is a no-op for this transformer.

transform(X)

Flatten the preprocessing dimension.

Parameters:

X – Input array. Can be: - 2D array (samples, features): returned unchanged - 3D array (samples, preprocessings, features): flattened to 2D

Returns:

2D numpy array of shape (samples, preprocessings * features).

class nirs4all.operators.transforms.features.ResampleTransformer(num_samples: int)

Bases: BaseEstimator, TransformerMixin

fit(X, y=None)

transform(X)