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