Source code for immuneML.encodings.preprocessing.FeatureScaler

import pickle
from pathlib import Path

from sklearn.preprocessing import StandardScaler, normalize, binarize

from immuneML.analysis.data_manipulation.NormalizationType import NormalizationType
from immuneML.util.PathBuilder import PathBuilder

[docs]class FeatureScaler: SKLEARN_NORMALIZATION_TYPES = ["l1", "l2", "max"]
[docs] @staticmethod def standard_scale(scaler_file: Path, design_matrix, with_mean: bool = True): """ scale to zero mean and unit variance on feature level :param scaler_file: path to scaler file fitted on train set or where the resulting scaler file will be stored :param design_matrix: rows -> examples, columns -> features :param with_mean: whether to scale to zero mean or not (could lose sparsity if scaled) :return: scaled design matrix """ if with_mean and hasattr(design_matrix, "todense"): scaled_design_matrix = design_matrix.todense() else: scaled_design_matrix = design_matrix if scaler_file.is_file(): with'rb') as file: scaler = pickle.load(file) scaled_design_matrix = scaler.transform(scaled_design_matrix) else: scaler = StandardScaler(with_mean=with_mean) scaled_design_matrix = scaler.fit_transform(scaled_design_matrix) directory = scaler_file.parent PathBuilder.build_from_objects(directory) with'wb') as file: pickle.dump(scaler, file) return scaled_design_matrix
[docs] @staticmethod def normalize(design_matrix, normalization_type: NormalizationType): """ Normalize on example level so that the norm type applies Args: design_matrix: rows -> examples, columns -> features normalization_type: l1, l2, max, binary, none Returns: normalized design matrix """ if == "NONE": normalized_design_matrix = design_matrix elif == "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.value)) return normalized_design_matrix