Question

我正在尝试将MAPE用作xgboost中的eval指标，但会得到奇怪的结果：

def xgb_mape(preds, dtrain):
   labels = dtrain.get_label()
   return('mape', np.mean(np.abs((labels - preds) / (labels+1))))

xgp = {"colsample_bytree": 0.9, 
   "min_child_weight": 24, 
   "subsample": 0.9, 
   "eta": 0.05, 
   "objective": "reg:linear", 
   "seed": 70}

cv = xgb.cv(params = xgp, 
        dtrain = xgb.DMatrix(train_set[cols_to_use], label=train_set.y),
        folds = KFold(n = len(train_set), n_folds=4, random_state = 707, shuffle=True),
        feval = xgb_mape,
        early_stopping_rounds=10,
        num_boost_round=1000,
        verbose_eval=10,
        maximize=False
        )

它返回：

[0]     train-mape:0.780683+0.00241932  test-mape:0.779896+0.0024619
[10]    train-mape:0.84939+0.0196102    test-mape:0.858054+0.0184669
[20]    train-mape:1.0778+0.0313676     test-mape:1.10751+0.0293785
[30]    train-mape:1.26066+0.0343771    test-mape:1.30707+0.0323237
[40]    train-mape:1.37713+0.0347438    test-mape:1.43339+0.030565
[50]    train-mape:1.45653+0.042433     test-mape:1.52176+0.0383677
[60]    train-mape:1.52268+0.0386395    test-mape:1.5909+0.0353497
[70]    train-mape:1.5636+0.0383622     test-mape:1.63482+0.0301809
[80]    train-mape:1.59408+0.0378158    test-mape:1.66748+0.0315529
[90]    train-mape:1.61712+0.0403532    test-mape:1.69134+0.0325177
[100]   train-mape:1.63028+0.0389446    test-mape:1.70578+0.0316045
[110]   train-mape:1.63556+0.0375842    test-mape:1.71153+0.031564
[120]   train-mape:1.63509+0.0393198    test-mape:1.7117+0.0320471

训练和测试结果随着maximize=False而增加，而且早期停止也无法正常工作。哪里出错？

UPD。将-1*添加到xgb_mape，它解决了问题。看起来maximize参数对自定义评估函数不起作用。

Answer 1

根据this xgboost example of implementing Average Precision metric，由于xgb优化器只会最小化，因此，如果您实现一个最大化的度量，则必须在其前面添加一个负号（-），如下所示：

def pr_auc_metric(y_predicted, y_true): return 'pr_auc', -skmetrics.average_precision_score(y_true.get_label(), y_predicted)

所以你会是：

def xgb_mape(preds, dtrain): labels = dtrain.get_label() return('mape', -np.mean(np.abs((labels - preds) / (labels+1))))

xgboost中的mape eval指标

1 个答案: