2cc9662005
This PR is meant the end the confusion around best_ntree_limit and unify model slicing. We have multi-class and random forests, asking users to understand how to set ntree_limit is difficult and error prone. * Implement the save_best option in early stopping. Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
60 lines
2.4 KiB
ReStructuredText
60 lines
2.4 KiB
ReStructuredText
##################
|
|
Callback Functions
|
|
##################
|
|
|
|
This document gives a basic walkthrough of callback function used in XGBoost Python
|
|
package. In XGBoost 1.3, a new callback interface is designed for Python package, which
|
|
provides the flexiblity 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:
|
|
|
|
.. code-block:: python
|
|
|
|
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,
|
|
metric_name='CustomErr',
|
|
data_name='Train')
|
|
|
|
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')
|
|
assert len(early_stop.stopping_history['Valid']['CustomErr']) == len(dump)
|
|
|
|
|
|
Defining your own callback
|
|
--------------------------
|
|
|
|
XGBoost provides an callback interface class: ``xgboost.callback.TrainingCallback``, user
|
|
defined callbacks should inherit this class and override corresponding methods. There's a
|
|
working example in `demo/guide-python/callbacks.py <https://github.com/dmlc/xgboost/tree/master/demo/guide-python/callbacks.py>`_
|