98e6a637cf96d77e9e309c1049f52387f11e8ed7,snorkel/utils.py,,matrix_conflicts,#Any#,98

Before Change

    
    B = L.copy()
    for row in range(0,B.shape[0]):
        if np.unique(B.getrow(row).data).size == 1:
            B.data[B.indptr[row]:B.indptr[row+1]] = 0
    return matrix_coverage(sparse_nonzero(B))

After Change

    B_nonzero = sparse_nonzero(B)
    B_abs = sparse_abs(L)
    with np.errstate(divide="ignore"): // Suppresses warning occuring when all labeles for candidate are 0
        return np.ravel(np.where(np.logical_or(np.mod(B.sum(axis=1),B_nonzero.sum(axis=1)) != 0,\
            B_abs.sum(axis=1) != sparse_abs(B.sum(axis=1)) ),1,0).T * B_nonzero / float(B.shape[0]))


def matrix_tp(L, labels):

Instances