import numpy as np


def getAP(conf, labels):
    assert len(conf) == len(labels)
    sortind = np.argsort(-conf)
    tp = labels[sortind] == 1
    fp = labels[sortind] != 1
    npos = np.sum(labels)

    fp = np.cumsum(fp).astype("float32")
    tp = np.cumsum(tp).astype("float32")
    rec = tp / npos
    prec = tp / (fp + tp)
    tmp = (labels[sortind] == 1).astype("float32")

    return np.sum(tmp * prec) / npos if npos > 0 else 1


def getClassificationMAP(confidence, labels):
    """ confidence and labels are of dimension n_samples x n_label """

    AP = []
    for i in range(np.shape(labels)[1]):
        AP.append(getAP(confidence[:, i], labels[:, i]))
    return 100 * sum(AP) / len(AP)