immuneML.encodings.preprocessing package

Submodules

immuneML.encodings.preprocessing.FeatureScaler module

class immuneML.encodings.preprocessing.FeatureScaler.FeatureScaler

Bases: object

SKLEARN_NORMALIZATION_TYPES = ['l1', 'l2', 'max']

static normalize(design_matrix, normalization_type: NormalizationType)

Normalize on example level so that the norm type applies to compute values like frequency

Parameters:

• design_matrix – rows -> examples, columns -> features

• normalization_type – l1, l2, max, binary, none

Returns:

normalized design matrix

static standard_scale(scaler, design_matrix, with_mean: bool = True)

Scale to zero mean and unit variance on feature level

Parameters:

• scaler – already fitted scaler object that has function transform

• design_matrix – rows -> examples, columns -> features

• with_mean – whether to scale to zero mean or not (could lose sparsity if scaled)

Returns:

scaled design matrix

static standard_scale_fit(scaler, design_matrix, with_mean: bool = True)

Scale to zero mean and unit variance on feature level

Parameters:

• scaler – scaler object that has function fit_transform

• design_matrix – rows -> examples, columns -> features

• with_mean – whether to scale to zero mean or not (could lose sparsity if scaled)

Returns:

scaled design matrix

Module contents