Source code for optuna.integration.lightgbm

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
    from lightgbm.callback import CallbackEnv

# Attach lightgbm API.
if _imports.is_successful():
    # To pass tests/integration_tests/lightgbm_tuner_tests/
    from lightgbm import Dataset

    from optuna.integration._lightgbm_tuner import LightGBMTuner
    from optuna.integration._lightgbm_tuner import LightGBMTunerCV

    _names_from_tuners = ["train", "LGBMModel", "LGBMClassifier", "LGBMRegressor"]

    # API from lightgbm.
    for api_name in lgb.__dict__["__all__"]:
        if api_name in _names_from_tuners:
        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])
    # 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"])

__all__ = ["Dataset", "LightGBMTuner", "LightGBMTunerCV"]

[docs]class LightGBMPruningCallback: """Callback for LightGBM to prune unpromising trials. See `the example < lightgbm/>`__ 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 <>`_ for further details. valid_name: The name of the target validation. Validation names are specified by ``valid_names`` option of `train method <>`_. 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 <>`_ 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 `` function, # the value of `is_cv` becomes `True`. See also: # # 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 != 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 != 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." ), step=env.iteration) if self._trial.should_prune(): message = "Trial was pruned at iteration {}.".format(env.iteration) raise optuna.TrialPruned(message)