lightgbm.callback Namespace Reference

Data Structures
class	EarlyStopException

Functions
	_format_eval_result (value, show_stdv=True)
	print_evaluation (period=1, show_stdv=True)
	record_evaluation (eval_result)
	reset_parameter (**kwargs)
	early_stopping (stopping_rounds, verbose=True)

Variables
	CallbackEnv

Detailed Description

Callbacks library.

Function Documentation

_format_eval_result()

lightgbm.callback._format_eval_result (   value,   show_stdv = True  )

protected

Format metric string.

early_stopping()

lightgbm.callback.early_stopping	(		stopping_rounds,
			verbose = True
	)

Create a callback that activates early stopping.

Note
----
Activates early stopping.
The model will train until the validation score stops improving.
Validation score needs to improve at least every ``early_stopping_rounds`` round(s)
to continue training.
Requires at least one validation data and one metric.
If there's more than one, will check all of them. But the training data is ignored anyway.

Parameters
----------
stopping_rounds : int
   The possible number of rounds without the trend occurrence.
verbose : bool, optional (default=True)
    Whether to print message with early stopping information.

Returns
-------
callback : function
    The callback that activates early stopping.

print_evaluation()

lightgbm.callback.print_evaluation	(		period = 1,
			show_stdv = True
	)

Create a callback that prints the evaluation results.

Parameters
----------
period : int, optional (default=1)
    The period to print the evaluation results.
show_stdv : bool, optional (default=True)
    Whether to show stdv (if provided).

Returns
-------
callback : function
    The callback that prints the evaluation results every ``period`` iteration(s).

record_evaluation()

lightgbm.callback.record_evaluation

(

eval_result

)

Create a callback that records the evaluation history into ``eval_result``.

Parameters
----------
eval_result : dict
   A dictionary to store the evaluation results.

Returns
-------
callback : function
    The callback that records the evaluation history into the passed dictionary.

reset_parameter()

lightgbm.callback.reset_parameter

(

**

kwargs

)

Create a callback that resets the parameter after the first iteration.

Note
----
The initial parameter will still take in-effect on first iteration.

Parameters
----------
**kwargs : value should be list or function
    List of parameters for each boosting round
    or a customized function that calculates the parameter in terms of
    current number of round (e.g. yields learning rate decay).
    If list lst, parameter = lst[current_round].
    If function func, parameter = func(current_round).

Returns
-------
callback : function
    The callback that resets the parameter after the first iteration.

Variable Documentation

CallbackEnv

lightgbm.callback.CallbackEnv

Initial value:

=  collections.namedtuple(
    "LightGBMCallbackEnv",
    ["model",
     "params",
     "iteration",
     "begin_iteration",
     "end_iteration",
     "evaluation_result_list"])