xgboost 如何自定义评价函数

def tpr_weight_funtion(y_true,y_predict):
    d = pd.DataFrame()
    d['prob'] = list(y_predict)
    d['y'] = list(y_true)
    d = d.sort_values(['prob'], ascending=[0])
    y = d.y
    PosAll = pd.Series(y).value_counts()[1]
    NegAll = pd.Series(y).value_counts()[0]
    pCumsum = d['y'].cumsum()
    nCumsum = np.arange(len(y)) - pCumsum + 1
    pCumsumPer = pCumsum / PosAll
    nCumsumPer = nCumsum / NegAll
    TR1 = pCumsumPer[abs(nCumsumPer-0.001).idxmin()]
    TR2 = pCumsumPer[abs(nCumsumPer-0.005).idxmin()]
    TR3 = pCumsumPer[abs(nCumsumPer-0.01).idxmin()]
    return 0.4 * TR1 + 0.3 * TR2 + 0.3 * TR3
def myFeval(preds, xgbtrain):
    label = xgbtrain.get_label()
    score=tpr_weight_funtion(label, preds)
    return 'myFeval',score

 model = xgb.train(params, xgb_train_data,num_boost_round=2000,evals= [(xgb_train_data, 'train'),(xgb_val_data, 'valid')],early_stopping_rounds=100,verbose_eval=500,feval = myFeval)  

在末尾加上一句 feval = myFeval