xgboost.callback.EarlyStopping Class Reference

Inheritance diagram for xgboost.callback.EarlyStopping:

Public Member Functions
None	__init__ (self, int rounds, Optional[str] metric_name=None, Optional[str] data_name=None, Optional[bool] maximize=None, Optional[bool] save_best=False, float min_delta=0.0)
_Model	before_training (self, _Model model)
bool	after_iteration (self, _Model model, int epoch, TrainingCallback.EvalsLog evals_log)
_Model	after_training (self, _Model model)
Public Member Functions inherited from xgboost.callback.TrainingCallback
bool	before_iteration (self, _Model model, int epoch, EvalsLog evals_log)

Data Fields
	data
	metric_name
	rounds
	save_best
	maximize
	starting_round
	current_rounds

Protected Member Functions
bool	_update_rounds (self, _Score score, str name, str metric, _Model model, int epoch)

Protected Attributes
	_min_delta

Additional Inherited Members
Static Public Attributes inherited from xgboost.callback.TrainingCallback
	EvalsLog = Dict[str, Dict[str, _ScoreList]]

Detailed Description

Callback function for early stopping

.. versionadded:: 1.3.0

Parameters
----------
rounds :
    Early stopping rounds.
metric_name :
    Name of metric that is used for early stopping.
data_name :
    Name of dataset that is used for early stopping.
maximize :
    Whether to maximize evaluation metric.  None means auto (discouraged).
save_best :
    Whether training should return the best model or the last model.
min_delta :

    .. versionadded:: 1.5.0

    Minimum absolute change in score to be qualified as an improvement.

Examples
--------

.. code-block:: python

    es = xgboost.callback.EarlyStopping(
        rounds=2,
        min_delta=1e-3,
        save_best=True,
        maximize=False,
        data_name="validation_0",
        metric_name="mlogloss",
    )
    clf = xgboost.XGBClassifier(tree_method="hist", device="cuda", callbacks=[es])

    X, y = load_digits(return_X_y=True)
    clf.fit(X, y, eval_set=[(X, y)])

Constructor & Destructor Documentation

__init__()

None xgboost.callback.EarlyStopping.__init__	(		self,
		int	rounds,
		Optional[str]	metric_name = None,
		Optional[str]	data_name = None,
		Optional[bool]	maximize = None,
		Optional[bool]	save_best = False,
		float	min_delta = 0.0
	)

Reimplemented from xgboost.callback.TrainingCallback.

Member Function Documentation

after_iteration()

bool xgboost.callback.EarlyStopping.after_iteration	(		self,
		_Model	model,
		int	epoch,
		TrainingCallback.EvalsLog	evals_log
	)

Run after each iteration.  Return True when training should stop.

Reimplemented from xgboost.callback.TrainingCallback.

after_training()

_Model xgboost.callback.EarlyStopping.after_training	(		self,
		_Model	model
	)

Run after training is finished.

Reimplemented from xgboost.callback.TrainingCallback.

before_training()

_Model xgboost.callback.EarlyStopping.before_training	(		self,
		_Model	model
	)

Run before training starts.

Reimplemented from xgboost.callback.TrainingCallback.

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The documentation for this class was generated from the following file:

• External/xgboost/python-package/xgboost/callback.py