xgboost 自定义评价函数（metric）与目标函数

比赛得分公式如下：

其中，P为Precision , R为 Recall。

GBDT训练基于验证集评价，此时会调用评价函数，XGBoost的best_iteration和best_score均是基于评价函数得出。

评价函数：

input: preds和dvalid，即为验证集和验证集上的预测值，

return string 类型的名称和一个flaot类型的fevalerror值表示评价值的大小，其是以error的形式定义，即当此值越大是认为模型效果越差。

1 from sklearn.metrics import confusion_matrix
2 def customedscore(preds, dtrain):
3     label = dtrain.get_label()
4     pred = [int(i>=0.5) for i in preds]
5     confusion_matrixs = confusion_matrix(label, pred)
6     recall =float(confusion_matrixs[0][0]) / float(confusion_matrixs[0][1]+confusion_matrixs[0][0])
7     precision = float(confusion_matrixs[0][0]) / float(confusion_matrixs[1][0]+confusion_matrixs[0][0])
8     F = 5*precision* recall/(2*precision+3*recall)*100
9     return 'FSCORE',float(F)

应用：

训练时要传入参数：feval = customedscore,

 1    params = { 'silent': 1,  'objective': 'binary:logistic' , 'gamma':0.1,
 2         'min_child_weight':5,
 3         'max_depth':5,
 4         'lambda':10,
 5         'subsample':0.7,
 6         'colsample_bytree':0.7,
 7         'colsample_bylevel':0.7,
 8         'eta': 0.01,
 9         'tree_method':'exact'}
10     model = xgb.train(params, trainsetall, num_round,verbose_eval=10, feval = customedscore,maximize=False)

自定义目标函数，这个我没有具体使用

1 # user define objective function, given prediction, return gradient and second order gradient
2 # this is log likelihood loss
3 def logregobj(preds, dtrain):
4     labels = dtrain.get_label()
5     preds = 1.0 / (1.0 + np.exp(-preds))
6     grad = preds - labels
7     hess = preds * (1.0-preds)
8     return grad, hess

# training with customized objective, we can also do step by step training
# simply look at xgboost.py's implementation of train
bst = xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror)

参考：

https://github.com/dmlc/xgboost/blob/master/demo/guide-python/custom_objective.py

http://blog.csdn.net/lujiandong1/article/details/52791117