# Source code for immuneML.encodings.preprocessing.FeatureScaler

```
from sklearn.preprocessing import normalize, binarize
from immuneML.analysis.data_manipulation.NormalizationType import NormalizationType
[docs]class FeatureScaler:
SKLEARN_NORMALIZATION_TYPES = ["l1", "l2", "max"]
[docs] @staticmethod
def standard_scale_fit(scaler, design_matrix, with_mean: bool = True):
"""
Scale to zero mean and unit variance on feature level
Args:
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
"""
scaled_design_matrix = FeatureScaler._optional_convert_to_dense(design_matrix, with_mean)
scaled_design_matrix = scaler.fit_transform(scaled_design_matrix)
return scaled_design_matrix
[docs] @staticmethod
def standard_scale(scaler, design_matrix, with_mean: bool = True):
"""
Scale to zero mean and unit variance on feature level
Args:
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
"""
scaled_design_matrix = FeatureScaler._optional_convert_to_dense(design_matrix, with_mean)
scaled_design_matrix = scaler.transform(scaled_design_matrix)
return scaled_design_matrix
@staticmethod
def _optional_convert_to_dense(design_matrix, with_mean: bool):
if with_mean and hasattr(design_matrix, "todense"):
scaled_design_matrix = design_matrix.todense()
else:
scaled_design_matrix = design_matrix
return scaled_design_matrix
[docs] @staticmethod
def normalize(design_matrix, normalization_type: NormalizationType):
"""
Normalize on example level so that the norm type applies to compute values like frequency
Args:
design_matrix: rows -> examples, columns -> features
normalization_type: l1, l2, max, binary, none
Returns:
normalized design matrix
"""
if normalization_type.name == "NONE":
normalized_design_matrix = design_matrix
elif normalization_type.name == "BINARY":
normalized_design_matrix = binarize(design_matrix)
elif normalization_type.value in FeatureScaler.SKLEARN_NORMALIZATION_TYPES:
normalized_design_matrix = normalize(design_matrix, norm=normalization_type.value, axis=1)
else:
raise NotImplementedError("Normalization type {} ({}) is not implemented.".format(normalization_type.name, normalization_type.value))
return normalized_design_matrix
```