immuneML.encodings.preprocessing package¶
Submodules¶
immuneML.encodings.preprocessing.FeatureScaler module¶
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class
immuneML.encodings.preprocessing.FeatureScaler.FeatureScaler[source]¶ Bases:
object-
SKLEARN_NORMALIZATION_TYPES= ['l1', 'l2', 'max']¶
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static
normalize(design_matrix, normalization_type: immuneML.analysis.data_manipulation.NormalizationType.NormalizationType)[source]¶ 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
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static
standard_scale(scaler, design_matrix, with_mean: bool = True)[source]¶ 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
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static
standard_scale_fit(scaler, design_matrix, with_mean: bool = True)[source]¶ 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
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