xgboost.callback.LearningRateScheduler Class Reference

Inheritance diagram for xgboost.callback.LearningRateScheduler:

Public Member Functions
None	__init__ (self, Union[Callable[[int], float], Sequence[float]] learning_rates)
bool	after_iteration (self, _Model model, int epoch, TrainingCallback.EvalsLog evals_log)
Public Member Functions inherited from xgboost.callback.TrainingCallback
_Model	before_training (self, _Model model)
_Model	after_training (self, _Model model)
bool	before_iteration (self, _Model model, int epoch, EvalsLog evals_log)

Data Fields
	learning_rates

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

Detailed Description

Callback function for scheduling learning rate.

.. versionadded:: 1.3.0

Parameters
----------

learning_rates :
    If it's a callable object, then it should accept an integer parameter
    `epoch` and returns the corresponding learning rate.  Otherwise it
    should be a sequence like list or tuple with the same size of boosting
    rounds.

Constructor & Destructor Documentation

__init__()

None xgboost.callback.LearningRateScheduler.__init__	(		self,
		Union[Callable[[int], float], Sequence[float]]	learning_rates
	)

Reimplemented from xgboost.callback.TrainingCallback.

Member Function Documentation

after_iteration()

bool xgboost.callback.LearningRateScheduler.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.

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

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