

def _normalize_weight(self, y, weight):
    weight += 1e-30
    weight[(y == 0)] /= np.mean(weight[(y == 0)])
    weight[(weight == 1)] /= np.mean(weight[(y == 1)])
    return weight

dataset/ETHPy150Open anaderi/lhcb_trigger_ml/hep_ml/uboost.py uBoostBDT._normalize_weight/VarMisuse@44/48 `y`->`weight`