回归中xgboost中的自定义目标函数

Question

我正在研究回归问题，我想在xgboost库中修改损失函数，这样我的预测永远不会低于实际值。我写了这段代码：

def custom_loss(preds, dtrain):
    labels = dtrain.get_label()
    df = preds - labels
    df = pd.DataFrame(df, columns=['val'])
    df['valg'] = df['val'].apply(lambda x: 10*abs(x) if x<0 else x)
    grad = df['valg'].as_matrix()
    return preds-labels, grad

这实质上意味着我想惩罚那些比实际价值更低的预测。但是，这不起作用，我的预测没有任何改进。任何人都可以帮我弄清楚我哪里出错了吗？ 感谢。

编辑： 整个python脚本 -

params = {"booster" : "gbtree",
      "eta": 0.20,
      "max_depth": 4,
      "subsample": 0.75,
      "colsample_bytree": 0.65,
      "silent": 1,
      "eval_metric": "rmse",
      }

num_round = 400

def custom_loss(preds, dtrain):
    labels = dtrain.get_label()
    df = preds - labels
    df = pd.DataFrame(df, columns=['val'])
    df['valg'] = df['val'].apply(lambda x: 5*abs(x) if x<0 else x)
    grad = df['valg'].as_matrix()
    return preds-labels, grad


dtrain = xgb.DMatrix(X_train.drop('price_act', axis=1), 
label=X_train['price_act'])
dtest = xgb.DMatrix(X_test.drop('price_act',axis=1), 
label=X_test['price_act'])

watchlist = [(dtrain,'train'), (dtest,'eval')]

bst = xgb.train(params, dtrain, num_round, watchlist, custom_loss)