def get_sensitivity_matrix(label, debug=True):
    '\n    Given a tag like 4: (B-PER), return the sensitivity matrix\n    :param label:\n    :return:\n    '
    avg_for_label = (data.tag_contributions[label] / data.tag_counts[label])
    sum_other_counts = 0
    sum_other_contributions = np.zeros((10, 50))
    for l in data.tag_counts:
        if ((l != label) and (l != 0)):
            sum_other_counts += data.tag_counts[l]
            sum_other_contributions += data.tag_contributions[l]
    avg_for_others = (sum_other_contributions / sum_other_counts)
    s_ij = (avg_for_label - avg_for_others)
    s_ij_label = s_ij[label]
    return s_ij_label
Given a tag like 4: (B-PER), return the sensitivity matrix
:param label:
:return:
def get_sensitivity_matrix(label, debug=True):
    '\n    Given a tag like 4: (B-PER), return the sensitivity matrix\n    :param label:\n    :return:\n    '
    avg_for_label = (data.tag_contributions[label] / data.tag_counts[label])
    sum_other_counts = 0
    sum_other_contributions = np.zeros((10, 50))
    for l in data.tag_counts:
        if ((l != label) and (l != 0)):
            sum_other_counts += data.tag_counts[l]
            sum_other_contributions += data.tag_contributions[l]
    avg_for_others = (sum_other_contributions / sum_other_counts)
    s_ij = (avg_for_label - avg_for_others)
    s_ij_label = s_ij[label]
    return s_ij_label