Source code for skmultilearn.utils

import numpy as np
import scipy.sparse as sp

    "bsr": sp.bsr_matrix,
    "coo": sp.coo_matrix,
    "csc": sp.csc_matrix,
    "csr": sp.csr_matrix,
    "dia": sp.dia_matrix,
    "dok": sp.dok_matrix,
    "lil": sp.lil_matrix

[docs]def get_matrix_in_format(original_matrix, matrix_format): """Converts matrix to format Parameters ---------- original_matrix : np.matrix or scipy matrix or np.array of np. arrays matrix to convert matrix_format : string format Returns ------- matrix : scipy matrix matrix in given format """ if isinstance(original_matrix, np.ndarray): return SPARSE_FORMAT_TO_CONSTRUCTOR[matrix_format](original_matrix) if original_matrix.getformat() == matrix_format: return original_matrix return original_matrix.asformat(matrix_format)
def matrix_creation_function_for_format(sparse_format): if sparse_format not in SPARSE_FORMAT_TO_CONSTRUCTOR: return None return SPARSE_FORMAT_TO_CONSTRUCTOR[sparse_format]
[docs]def measure_per_label(measure, y_true, y_predicted): """ Return per label results of a scikit-learn compatible quality measure Parameters ---------- measure : callable scikit-compatible quality measure function y_true : sparse matrix ground truth y_predicted : sparse matrix the predicted result Returns ------- List[int or float] score from a given measure depending on what the measure returns """ return [ measure( y_true[:, i].toarray(), y_predicted[:, i].toarray() ) for i in range(y_true.shape[1]) ]