# coding: utf-8
"""Callbacks library."""
from collections import OrderedDict
from dataclasses import dataclass
from functools import partial
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union
from .basic import (
Booster,
_ConfigAliases,
_LGBM_BoosterEvalMethodResultType,
_LGBM_BoosterEvalMethodResultWithStandardDeviationType,
_log_info,
_log_warning,
)
if TYPE_CHECKING:
from .engine import CVBooster
__all__ = [
"EarlyStopException",
"early_stopping",
"log_evaluation",
"record_evaluation",
"reset_parameter",
]
_EvalResultDict = Dict[str, Dict[str, List[Any]]]
_EvalResultTuple = Union[
_LGBM_BoosterEvalMethodResultType,
_LGBM_BoosterEvalMethodResultWithStandardDeviationType,
]
_ListOfEvalResultTuples = Union[
List[_LGBM_BoosterEvalMethodResultType],
List[_LGBM_BoosterEvalMethodResultWithStandardDeviationType],
]
class EarlyStopException(Exception):
"""Exception of early stopping.
Raise this from a callback passed in via keyword argument ``callbacks``
in ``cv()`` or ``train()`` to trigger early stopping.
"""
def __init__(self, best_iteration: int, best_score: _ListOfEvalResultTuples) -> None:
"""Create early stopping exception.
Parameters
----------
best_iteration : int
The best iteration stopped.
0-based... pass ``best_iteration=2`` to indicate that the third iteration was the best one.
best_score : list of (eval_name, metric_name, eval_result, is_higher_better) tuple or (eval_name, metric_name, eval_result, is_higher_better, stdv) tuple
Scores for each metric, on each validation set, as of the best iteration.
"""
super().__init__()
self.best_iteration = best_iteration
self.best_score = best_score
# Callback environment used by callbacks
@dataclass
class CallbackEnv:
model: Union[Booster, "CVBooster"]
params: Dict[str, Any]
iteration: int
begin_iteration: int
end_iteration: int
evaluation_result_list: Optional[_ListOfEvalResultTuples]
def _format_eval_result(value: _EvalResultTuple, show_stdv: bool) -> str:
"""Format metric string."""
if len(value) == 4:
return f"{value[0]}'s {value[1]}: {value[2]:g}"
elif len(value) == 5:
if show_stdv:
return f"{value[0]}'s {value[1]}: {value[2]:g} + {value[4]:g}" # type: ignore[misc]
else:
return f"{value[0]}'s {value[1]}: {value[2]:g}"
else:
raise ValueError("Wrong metric value")
class _LogEvaluationCallback:
"""Internal log evaluation callable class."""
def __init__(self, period: int = 1, show_stdv: bool = True) -> None:
self.order = 10
self.before_iteration = False
self.period = period
self.show_stdv = show_stdv
def __call__(self, env: CallbackEnv) -> None:
if self.period > 0 and env.evaluation_result_list and (env.iteration + 1) % self.period == 0:
result = "\t".join([_format_eval_result(x, self.show_stdv) for x in env.evaluation_result_list])
_log_info(f"[{env.iteration + 1}]\t{result}")
[docs]def log_evaluation(period: int = 1, show_stdv: bool = True) -> _LogEvaluationCallback:
"""Create a callback that logs the evaluation results.
By default, standard output resource is used.
Use ``register_logger()`` function to register a custom logger.
Note
----
Requires at least one validation data.
Parameters
----------
period : int, optional (default=1)
The period to log the evaluation results.
The last boosting stage or the boosting stage found by using ``early_stopping`` callback is also logged.
show_stdv : bool, optional (default=True)
Whether to log stdv (if provided).
Returns
-------
callback : _LogEvaluationCallback
The callback that logs the evaluation results every ``period`` boosting iteration(s).
"""
return _LogEvaluationCallback(period=period, show_stdv=show_stdv)
class _RecordEvaluationCallback:
"""Internal record evaluation callable class."""
def __init__(self, eval_result: _EvalResultDict) -> None:
self.order = 20
self.before_iteration = False
if not isinstance(eval_result, dict):
raise TypeError("eval_result should be a dictionary")
self.eval_result = eval_result
def _init(self, env: CallbackEnv) -> None:
if env.evaluation_result_list is None:
raise RuntimeError(
"record_evaluation() callback enabled but no evaluation results found. This is a probably bug in LightGBM. "
"Please report it at https://github.com/microsoft/LightGBM/issues"
)
self.eval_result.clear()
for item in env.evaluation_result_list:
if len(item) == 4: # regular train
data_name, eval_name = item[:2]
else: # cv
data_name, eval_name = item[1].split()
self.eval_result.setdefault(data_name, OrderedDict())
if len(item) == 4:
self.eval_result[data_name].setdefault(eval_name, [])
else:
self.eval_result[data_name].setdefault(f"{eval_name}-mean", [])
self.eval_result[data_name].setdefault(f"{eval_name}-stdv", [])
def __call__(self, env: CallbackEnv) -> None:
if env.iteration == env.begin_iteration:
self._init(env)
if env.evaluation_result_list is None:
raise RuntimeError(
"record_evaluation() callback enabled but no evaluation results found. This is a probably bug in LightGBM. "
"Please report it at https://github.com/microsoft/LightGBM/issues"
)
for item in env.evaluation_result_list:
if len(item) == 4:
data_name, eval_name, result = item[:3]
self.eval_result[data_name][eval_name].append(result)
else:
data_name, eval_name = item[1].split()
res_mean = item[2]
res_stdv = item[4] # type: ignore[misc]
self.eval_result[data_name][f"{eval_name}-mean"].append(res_mean)
self.eval_result[data_name][f"{eval_name}-stdv"].append(res_stdv)
[docs]def record_evaluation(eval_result: Dict[str, Dict[str, List[Any]]]) -> Callable:
"""Create a callback that records the evaluation history into ``eval_result``.
Parameters
----------
eval_result : dict
Dictionary used to store all evaluation results of all validation sets.
This should be initialized outside of your call to ``record_evaluation()`` and should be empty.
Any initial contents of the dictionary will be deleted.
.. rubric:: Example
With two validation sets named 'eval' and 'train', and one evaluation metric named 'logloss'
this dictionary after finishing a model training process will have the following structure:
.. code-block::
{
'train':
{
'logloss': [0.48253, 0.35953, ...]
},
'eval':
{
'logloss': [0.480385, 0.357756, ...]
}
}
Returns
-------
callback : _RecordEvaluationCallback
The callback that records the evaluation history into the passed dictionary.
"""
return _RecordEvaluationCallback(eval_result=eval_result)
class _ResetParameterCallback:
"""Internal reset parameter callable class."""
def __init__(self, **kwargs: Union[list, Callable]) -> None:
self.order = 10
self.before_iteration = True
self.kwargs = kwargs
def __call__(self, env: CallbackEnv) -> None:
new_parameters = {}
for key, value in self.kwargs.items():
if isinstance(value, list):
if len(value) != env.end_iteration - env.begin_iteration:
raise ValueError(f"Length of list {key!r} has to be equal to 'num_boost_round'.")
new_param = value[env.iteration - env.begin_iteration]
elif callable(value):
new_param = value(env.iteration - env.begin_iteration)
else:
raise ValueError(
"Only list and callable values are supported "
"as a mapping from boosting round index to new parameter value."
)
if new_param != env.params.get(key, None):
new_parameters[key] = new_param
if new_parameters:
if isinstance(env.model, Booster):
env.model.reset_parameter(new_parameters)
else:
# CVBooster holds a list of Booster objects, each needs to be updated
for booster in env.model.boosters:
booster.reset_parameter(new_parameters)
env.params.update(new_parameters)
[docs]def reset_parameter(**kwargs: Union[list, Callable]) -> Callable:
"""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 callable
List of parameters for each boosting round
or a callable 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 callable func, parameter = func(current_round).
Returns
-------
callback : _ResetParameterCallback
The callback that resets the parameter after the first iteration.
"""
return _ResetParameterCallback(**kwargs)
class _EarlyStoppingCallback:
"""Internal early stopping callable class."""
def __init__(
self,
stopping_rounds: int,
first_metric_only: bool = False,
verbose: bool = True,
min_delta: Union[float, List[float]] = 0.0,
) -> None:
if not isinstance(stopping_rounds, int) or stopping_rounds <= 0:
raise ValueError(f"stopping_rounds should be an integer and greater than 0. got: {stopping_rounds}")
self.order = 30
self.before_iteration = False
self.stopping_rounds = stopping_rounds
self.first_metric_only = first_metric_only
self.verbose = verbose
self.min_delta = min_delta
self.enabled = True
self._reset_storages()
def _reset_storages(self) -> None:
self.best_score: List[float] = []
self.best_iter: List[int] = []
self.best_score_list: List[_ListOfEvalResultTuples] = []
self.cmp_op: List[Callable[[float, float], bool]] = []
self.first_metric = ""
def _gt_delta(self, curr_score: float, best_score: float, delta: float) -> bool:
return curr_score > best_score + delta
def _lt_delta(self, curr_score: float, best_score: float, delta: float) -> bool:
return curr_score < best_score - delta
def _is_train_set(self, ds_name: str, eval_name: str, env: CallbackEnv) -> bool:
"""Check, by name, if a given Dataset is the training data."""
# for lgb.cv() with eval_train_metric=True, evaluation is also done on the training set
# and those metrics are considered for early stopping
if ds_name == "cv_agg" and eval_name == "train":
return True
# for lgb.train(), it's possible to pass the training data via valid_sets with any eval_name
if isinstance(env.model, Booster) and ds_name == env.model._train_data_name:
return True
return False
def _init(self, env: CallbackEnv) -> None:
if env.evaluation_result_list is None or env.evaluation_result_list == []:
raise ValueError("For early stopping, at least one dataset and eval metric is required for evaluation")
is_dart = any(env.params.get(alias, "") == "dart" for alias in _ConfigAliases.get("boosting"))
if is_dart:
self.enabled = False
_log_warning("Early stopping is not available in dart mode")
return
# validation sets are guaranteed to not be identical to the training data in cv()
if isinstance(env.model, Booster):
only_train_set = len(env.evaluation_result_list) == 1 and self._is_train_set(
ds_name=env.evaluation_result_list[0][0],
eval_name=env.evaluation_result_list[0][1].split(" ")[0],
env=env,
)
if only_train_set:
self.enabled = False
_log_warning("Only training set found, disabling early stopping.")
return
if self.verbose:
_log_info(f"Training until validation scores don't improve for {self.stopping_rounds} rounds")
self._reset_storages()
n_metrics = len({m[1] for m in env.evaluation_result_list})
n_datasets = len(env.evaluation_result_list) // n_metrics
if isinstance(self.min_delta, list):
if not all(t >= 0 for t in self.min_delta):
raise ValueError("Values for early stopping min_delta must be non-negative.")
if len(self.min_delta) == 0:
if self.verbose:
_log_info("Disabling min_delta for early stopping.")
deltas = [0.0] * n_datasets * n_metrics
elif len(self.min_delta) == 1:
if self.verbose:
_log_info(f"Using {self.min_delta[0]} as min_delta for all metrics.")
deltas = self.min_delta * n_datasets * n_metrics
else:
if len(self.min_delta) != n_metrics:
raise ValueError("Must provide a single value for min_delta or as many as metrics.")
if self.first_metric_only and self.verbose:
_log_info(f"Using only {self.min_delta[0]} as early stopping min_delta.")
deltas = self.min_delta * n_datasets
else:
if self.min_delta < 0:
raise ValueError("Early stopping min_delta must be non-negative.")
if self.min_delta > 0 and n_metrics > 1 and not self.first_metric_only and self.verbose:
_log_info(f"Using {self.min_delta} as min_delta for all metrics.")
deltas = [self.min_delta] * n_datasets * n_metrics
# split is needed for "<dataset type> <metric>" case (e.g. "train l1")
self.first_metric = env.evaluation_result_list[0][1].split(" ")[-1]
for eval_ret, delta in zip(env.evaluation_result_list, deltas):
self.best_iter.append(0)
if eval_ret[3]: # greater is better
self.best_score.append(float("-inf"))
self.cmp_op.append(partial(self._gt_delta, delta=delta))
else:
self.best_score.append(float("inf"))
self.cmp_op.append(partial(self._lt_delta, delta=delta))
def _final_iteration_check(self, env: CallbackEnv, eval_name_splitted: List[str], i: int) -> None:
if env.iteration == env.end_iteration - 1:
if self.verbose:
best_score_str = "\t".join([_format_eval_result(x, show_stdv=True) for x in self.best_score_list[i]])
_log_info(
"Did not meet early stopping. " f"Best iteration is:\n[{self.best_iter[i] + 1}]\t{best_score_str}"
)
if self.first_metric_only:
_log_info(f"Evaluated only: {eval_name_splitted[-1]}")
raise EarlyStopException(self.best_iter[i], self.best_score_list[i])
def __call__(self, env: CallbackEnv) -> None:
if env.iteration == env.begin_iteration:
self._init(env)
if not self.enabled:
return
if env.evaluation_result_list is None:
raise RuntimeError(
"early_stopping() callback enabled but no evaluation results found. This is a probably bug in LightGBM. "
"Please report it at https://github.com/microsoft/LightGBM/issues"
)
# self.best_score_list is initialized to an empty list
first_time_updating_best_score_list = self.best_score_list == []
for i in range(len(env.evaluation_result_list)):
score = env.evaluation_result_list[i][2]
if first_time_updating_best_score_list or self.cmp_op[i](score, self.best_score[i]):
self.best_score[i] = score
self.best_iter[i] = env.iteration
if first_time_updating_best_score_list:
self.best_score_list.append(env.evaluation_result_list)
else:
self.best_score_list[i] = env.evaluation_result_list
# split is needed for "<dataset type> <metric>" case (e.g. "train l1")
eval_name_splitted = env.evaluation_result_list[i][1].split(" ")
if self.first_metric_only and self.first_metric != eval_name_splitted[-1]:
continue # use only the first metric for early stopping
if self._is_train_set(
ds_name=env.evaluation_result_list[i][0],
eval_name=eval_name_splitted[0],
env=env,
):
continue # train data for lgb.cv or sklearn wrapper (underlying lgb.train)
elif env.iteration - self.best_iter[i] >= self.stopping_rounds:
if self.verbose:
eval_result_str = "\t".join(
[_format_eval_result(x, show_stdv=True) for x in self.best_score_list[i]]
)
_log_info(f"Early stopping, best iteration is:\n[{self.best_iter[i] + 1}]\t{eval_result_str}")
if self.first_metric_only:
_log_info(f"Evaluated only: {eval_name_splitted[-1]}")
raise EarlyStopException(self.best_iter[i], self.best_score_list[i])
self._final_iteration_check(env, eval_name_splitted, i)
[docs]def early_stopping(
stopping_rounds: int,
first_metric_only: bool = False,
verbose: bool = True,
min_delta: Union[float, List[float]] = 0.0,
) -> _EarlyStoppingCallback:
"""Create a callback that activates early stopping.
Activates early stopping.
The model will train until the validation score doesn't improve by at least ``min_delta``.
Validation score needs to improve at least every ``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.
To check only the first metric set ``first_metric_only`` to True.
The index of iteration that has the best performance will be saved in the ``best_iteration`` attribute of a model.
Parameters
----------
stopping_rounds : int
The possible number of rounds without the trend occurrence.
first_metric_only : bool, optional (default=False)
Whether to use only the first metric for early stopping.
verbose : bool, optional (default=True)
Whether to log message with early stopping information.
By default, standard output resource is used.
Use ``register_logger()`` function to register a custom logger.
min_delta : float or list of float, optional (default=0.0)
Minimum improvement in score to keep training.
If float, this single value is used for all metrics.
If list, its length should match the total number of metrics.
.. versionadded:: 4.0.0
Returns
-------
callback : _EarlyStoppingCallback
The callback that activates early stopping.
"""
return _EarlyStoppingCallback(
stopping_rounds=stopping_rounds,
first_metric_only=first_metric_only,
verbose=verbose,
min_delta=min_delta,
)