import sys
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
# 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(object):
"""Callback for LightGBM to prune unpromising trials.
See `the example <https://github.com/optuna/optuna/blob/master/
examples/pruning/lightgbm_integration.py>`__
if you want to add a pruning callback which observes AUC
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.
"""
[docs] def __init__(self, trial, metric, valid_name="valid_0"):
# type: (optuna.trial.Trial, str, str) -> None
_imports.check()
self._trial = trial
self._valid_name = valid_name
self._metric = metric
def __call__(self, env):
# type: (lgb.callback.CallbackEnv) -> None
# 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
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
if is_higher_better:
if (
self._trial.storage.get_study_direction(self._trial.study._study_id)
!= 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.storage.get_study_direction(self._trial.study._study_id)
!= 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)
return 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)
)
)