Callback Functions
This document gives a basic walkthrough ofcallback API used inXGBoost Python package. In XGBoost 1.3, a new callback interface is designed for Pythonpackage, which provides the flexibility of designing various extension for training.Also, XGBoost has a number of pre-defined callbacks for supporting early stopping,checkpoints etc.
Using builtin callbacks
By default, training methods in XGBoost have parameters likeearly_stopping_rounds andverbose/verbose_eval, when specified the training procedure will define thecorresponding callbacks internally. For example, whenearly_stopping_rounds isspecified,EarlyStopping callback is invokedinside iteration loop. You can also pass this callback function directly into XGBoost:
D_train=xgb.DMatrix(X_train,y_train)D_valid=xgb.DMatrix(X_valid,y_valid)# Define a custom evaluation metric used for early stopping.defeval_error_metric(predt,dtrain:xgb.DMatrix):label=dtrain.get_label()r=np.zeros(predt.shape)gt=predt>0.5r[gt]=1-label[gt]le=predt<=0.5r[le]=label[le]return'CustomErr',np.sum(r)# Specify which dataset and which metric should be used for early stopping.early_stop=xgb.callback.EarlyStopping(rounds=early_stopping_rounds,metric_name='CustomErr',data_name='Valid')booster=xgb.train({'objective':'binary:logistic','eval_metric':['error','rmse'],'tree_method':'hist'},D_train,evals=[(D_train,'Train'),(D_valid,'Valid')],feval=eval_error_metric,num_boost_round=1000,callbacks=[early_stop],verbose_eval=False)dump=booster.get_dump(dump_format='json')assertlen(early_stop.stopping_history['Valid']['CustomErr'])==len(dump)
Defining your own callback
XGBoost provides an callback interface class:TrainingCallback, user defined callbacks should inherit this class andoverride corresponding methods. There’s a working example inDemo for using and defining callback functions.