Callback Functions

This document gives a basic walkthrough of callback API used in XGBoost Python package. In XGBoost 1.3, a new callback interface is designed for Python package, 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 like early_stopping_rounds and verbose/verbose_eval, when specified the training procedure will define the corresponding callbacks internally. For example, when early_stopping_rounds is specified, EarlyStopping callback is invoked inside 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.
def eval_error_metric(predt, dtrain: xgb.DMatrix):
    label = dtrain.get_label()
    r = np.zeros(predt.shape)
    gt = predt > 0.5
    r[gt] = 1 - label[gt]
    le = predt <= 0.5
    r[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,

booster = xgb.train(
    {'objective': 'binary:logistic',
     'eval_metric': ['error', 'rmse'],
     'tree_method': 'hist'}, D_train,
    evals=[(D_train, 'Train'), (D_valid, 'Valid')],

dump = booster.get_dump(dump_format='json')
assert len(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 and override corresponding methods. There’s a working example in Demo for using and defining callback functions.