import sys
from typing import List
from typing import Optional
import optuna
from optuna._imports import try_import
from optuna.integration import _lightgbm_tuner as tuner
with try_import() as _imports:
import lightgbm as lgb # NOQA
from lightgbm.callback import CallbackEnv # NOQA
# Attach lightgbm API.
if _imports.is_successful():
# To pass tests/integration_tests/lightgbm_tuner_tests/test_optimize.py.
from lightgbm import Dataset # NOQA
from optuna.integration._lightgbm_tuner import LightGBMTuner # NOQA
from optuna.integration._lightgbm_tuner import LightGBMTunerCV # NOQA
_names_from_tuners = ["train", "LGBMModel", "LGBMClassifier", "LGBMRegressor"]
# API from lightgbm.
for api_name in lgb.__dict__["__all__"]:
if api_name in _names_from_tuners:
continue
setattr(sys.modules[__name__], api_name, lgb.__dict__[api_name])
# API from lightgbm_tuner.
for api_name in _names_from_tuners:
setattr(sys.modules[__name__], api_name, tuner.__dict__[api_name])
else:
# To create docstring of train.
setattr(sys.modules[__name__], "train", tuner.__dict__["train"])
setattr(sys.modules[__name__], "LightGBMTuner", tuner.__dict__["LightGBMTuner"])
setattr(sys.modules[__name__], "LightGBMTunerCV", tuner.__dict__["LightGBMTunerCV"])
[docs]class LightGBMPruningCallback:
"""Callback for LightGBM to prune unpromising trials.
See `the example <https://github.com/optuna/optuna-examples/blob/main/
lightgbm/lightgbm_integration.py>`__
if you want to add a pruning callback which observes accuracy
of a LightGBM model.
Args:
trial:
A :class:`~optuna.trial.Trial` corresponding to the current evaluation of
the objective function.
metric:
An evaluation metric for pruning, e.g., ``binary_error`` and ``multi_error``.
Please refer to
`LightGBM reference
<https://lightgbm.readthedocs.io/en/latest/Parameters.html#metric>`_
for further details.
valid_name:
The name of the target validation.
Validation names are specified by ``valid_names`` option of
`train method
<https://lightgbm.readthedocs.io/en/latest/Python-API.html#lightgbm.train>`_.
If omitted, ``valid_0`` is used which is the default name of the first validation.
Note that this argument will be ignored if you are calling
`cv method <https://lightgbm.readthedocs.io/en/latest/Python-API.html#lightgbm.cv>`_
instead of train method.
report_interval:
Check if the trial should report intermediate values for pruning every n-th boosting
iteration. By default ``report_interval=1`` and reporting is performed after every
iteration. Note that the pruning itself is performed according to the interval
definition of the pruner.
"""
def __init__(
self,
trial: optuna.trial.Trial,
metric: str,
valid_name: str = "valid_0",
report_interval: int = 1,
) -> None:
_imports.check()
self._trial = trial
self._valid_name = valid_name
self._metric = metric
self._report_interval = report_interval
def _find_evaluation_result(
self, target_valid_name: str, env: "CallbackEnv"
) -> Optional[List]:
for evaluation_result in env.evaluation_result_list:
valid_name, metric, current_score, is_higher_better = evaluation_result[:4]
if valid_name != target_valid_name or metric != self._metric:
continue
return evaluation_result
return None
def __call__(self, env: "CallbackEnv") -> None:
if (env.iteration + 1) % self._report_interval == 0:
# If this callback has been passed to `lightgbm.cv` function,
# the value of `is_cv` becomes `True`. See also:
# https://github.com/Microsoft/LightGBM/blob/v2.2.2/python-package/lightgbm/engine.py#L329
# Note that `5` is not the number of folds but the length of sequence.
is_cv = len(env.evaluation_result_list) > 0 and len(env.evaluation_result_list[0]) == 5
if is_cv:
target_valid_name = "cv_agg"
else:
target_valid_name = self._valid_name
evaluation_result = self._find_evaluation_result(target_valid_name, env)
if evaluation_result is None:
raise ValueError(
'The entry associated with the validation name "{}" and the metric name "{}" '
"is not found in the evaluation result list {}.".format(
target_valid_name, self._metric, str(env.evaluation_result_list)
)
)
valid_name, metric, current_score, is_higher_better = evaluation_result[:4]
if is_higher_better:
if self._trial.study.direction != optuna.study.StudyDirection.MAXIMIZE:
raise ValueError(
"The intermediate values are inconsistent with the objective values"
"in terms of study directions. Please specify a metric to be minimized"
"for LightGBMPruningCallback."
)
else:
if self._trial.study.direction != optuna.study.StudyDirection.MINIMIZE:
raise ValueError(
"The intermediate values are inconsistent with the objective values"
"in terms of study directions. Please specify a metric to be"
"maximized for LightGBMPruningCallback."
)
self._trial.report(current_score, step=env.iteration)
if self._trial.should_prune():
message = "Trial was pruned at iteration {}.".format(env.iteration)
raise optuna.TrialPruned(message)