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LightGBM の API リファレンス

LightGBM (lightgbm-org/LightGBM) の公開 API 88 件 —— クラス 18、関数 10、メソッド 60。実際のソースを静的解析して抽出した正確なシグネチャを掲載しています。

リポジトリ: lightgbm-org/LightGBM

種別件数
クラス18
関数10
メソッド60

API 一覧

func.ci.parameter-generator.gen_parameter_code(config_hpp:Path, config_out_cpp:Path) -> Tuple[List[Tuple[str, int]], List[List[Dict[str, List]]]]
Generate auto config file.
func.ci.parameter-generator.get_alias(infos:List[List[Dict[str, List]]]) -> List[Tuple[str, str]]
Get aliases of all parameters.
func.ci.parameter-generator.get_names(infos:List[List[Dict[str, List]]]) -> List[str]
Get names of all parameters.
func.ci.parameter-generator.get_parameter_infos(config_hpp:Path) -> Tuple[List[Tuple[str, int]], List[List[Dict[str, List]]]]
Parse config header file.
func.ci.parameter-generator.parse_check(check:str, reverse:bool=False) -> Tuple[str, str]
Parse the constraint.
func.ci.parameter-generator.set_one_var_from_string(name:str, param_type:str, checks:List[str]) -> str
Construct code for auto config file for one param value.
classpython-package.lightgbm.basic.Booster
Booster in LightGBM.
funcpython-package.lightgbm.basic.Booster.add(root:Dict[str, Any]) -> None
Recursively add thresholds.
methodpython-package.lightgbm.basic.Booster.add_valid(data:Dataset, name:str) -> 'Booster'
Add validation data.
methodpython-package.lightgbm.basic.Booster.current_iteration() -> int
Get the index of the current iteration.
methodpython-package.lightgbm.basic.Booster.eval(data:Dataset, name:str, feval:Optional[Union[_LGBM_CustomEvalFunction, List[_LGBM_CustomEvalFunction]]]=None) -> List[EvalResult]
Evaluate for data.
methodpython-package.lightgbm.basic.Booster.eval_train(feval:Optional[Union[_LGBM_CustomEvalFunction, List[_LGBM_CustomEvalFunction]]]=None) -> List[EvalResult]
Evaluate for training data.
methodpython-package.lightgbm.basic.Booster.eval_valid(feval:Optional[Union[_LGBM_CustomEvalFunction, List[_LGBM_CustomEvalFunction]]]=None) -> List[EvalResult]
Evaluate for validation data.
methodpython-package.lightgbm.basic.Booster.feature_importance(importance_type:str='split', iteration:Optional[int]=None) -> np.ndarray
Get feature importances.
methodpython-package.lightgbm.basic.Booster.feature_name() -> List[str]
Get names of features.
methodpython-package.lightgbm.basic.Booster.free_dataset() -> 'Booster'
Free Booster's Datasets.
methodpython-package.lightgbm.basic.Booster.free_network() -> 'Booster'
Free Booster's network.
methodpython-package.lightgbm.basic.Booster.get_leaf_output(tree_id:int, leaf_id:int) -> float
Get the output of a leaf.
methodpython-package.lightgbm.basic.Booster.lower_bound() -> float
Get lower bound value of a model.
methodpython-package.lightgbm.basic.Booster.model_from_string(model_str:str) -> 'Booster'
Load Booster from a string.
methodpython-package.lightgbm.basic.Booster.model_to_string(num_iteration:Optional[int]=None, start_iteration:int=0, importance_type:str='split') -> str
Save Booster to string.
methodpython-package.lightgbm.basic.Booster.num_feature() -> int
Get number of features.
methodpython-package.lightgbm.basic.Booster.num_model_per_iteration() -> int
Get number of models per iteration.
methodpython-package.lightgbm.basic.Booster.num_trees() -> int
Get number of weak sub-models.
methodpython-package.lightgbm.basic.Booster.reset_parameter(params:Dict[str, Any]) -> 'Booster'
Reset parameters of Booster.
methodpython-package.lightgbm.basic.Booster.rollback_one_iter() -> 'Booster'
Rollback one iteration.
methodpython-package.lightgbm.basic.Booster.save_model(filename:Union[str, Path], num_iteration:Optional[int]=None, start_iteration:int=0, importance_type:str='split') -> 'Booster'
Save Booster to file.
methodpython-package.lightgbm.basic.Booster.set_leaf_output(tree_id:int, leaf_id:int, value:float) -> 'Booster'
Set the output of a leaf.
methodpython-package.lightgbm.basic.Booster.set_network(machines:Union[List[str], Set[str], str], local_listen_port:int=12400, listen_time_out:int=120, num_machines:int=1) -> 'Booster'
Set the network configuration.
methodpython-package.lightgbm.basic.Booster.set_train_data_name(name:str) -> 'Booster'
Set the name to the training Dataset.
methodpython-package.lightgbm.basic.Booster.shuffle_models(start_iteration:int=0, end_iteration:int=-1) -> 'Booster'
Shuffle models.
methodpython-package.lightgbm.basic.Booster.update(train_set:Optional[Dataset]=None, fobj:Optional[_LGBM_CustomObjectiveFunction]=None) -> bool
Update Booster for one iteration.
methodpython-package.lightgbm.basic.Booster.upper_bound() -> float
Get upper bound value of a model.
classpython-package.lightgbm.basic.Dataset
Dataset in LightGBM.
methodpython-package.lightgbm.basic.Dataset.add_features_from(other:'Dataset') -> 'Dataset'
Add features from other Dataset to the current Dataset.
methodpython-package.lightgbm.basic.Dataset.construct() -> 'Dataset'
Lazy init.
methodpython-package.lightgbm.basic.Dataset.feature_num_bin(feature:Union[int, str]) -> int
Get the number of bins for a feature.
methodpython-package.lightgbm.basic.Dataset.get_data() -> Optional[_LGBM_TrainDataType]
Get the raw data of the Dataset.
methodpython-package.lightgbm.basic.Dataset.get_feature_name() -> List[str]
Get the names of columns (features) in the Dataset.
methodpython-package.lightgbm.basic.Dataset.get_field(field_name:str) -> Optional[np.ndarray]
Get property from the Dataset.
methodpython-package.lightgbm.basic.Dataset.get_group() -> Optional[_LGBM_GroupType]
Get the group of the Dataset.
methodpython-package.lightgbm.basic.Dataset.get_init_score() -> Optional[_LGBM_InitScoreType]
Get the initial score of the Dataset.
methodpython-package.lightgbm.basic.Dataset.get_label() -> Optional[_LGBM_LabelType]
Get the label of the Dataset.
methodpython-package.lightgbm.basic.Dataset.get_params() -> Dict[str, Any]
Get the used parameters in the Dataset.
methodpython-package.lightgbm.basic.Dataset.get_position() -> Optional[_LGBM_PositionType]
Get the position of the Dataset.
methodpython-package.lightgbm.basic.Dataset.get_ref_chain(ref_limit:int=100) -> Set['Dataset']
Get a chain of Dataset objects.
methodpython-package.lightgbm.basic.Dataset.get_weight() -> Optional[_LGBM_WeightType]
Get the weight of the Dataset.
methodpython-package.lightgbm.basic.Dataset.num_data() -> int
Get the number of rows in the Dataset.
methodpython-package.lightgbm.basic.Dataset.num_feature() -> int
Get the number of columns (features) in the Dataset.
methodpython-package.lightgbm.basic.Dataset.save_binary(filename:Union[str, Path]) -> 'Dataset'
Save Dataset to a binary file.
methodpython-package.lightgbm.basic.Dataset.set_categorical_feature(categorical_feature:_LGBM_CategoricalFeatureConfiguration) -> 'Dataset'
Set categorical features.
methodpython-package.lightgbm.basic.Dataset.set_feature_name(feature_name:_LGBM_FeatureNameConfiguration) -> 'Dataset'
Set feature name.
methodpython-package.lightgbm.basic.Dataset.set_field(field_name:str, data:Optional[_LGBM_SetFieldType]) -> 'Dataset'
Set property into the Dataset.
methodpython-package.lightgbm.basic.Dataset.set_group(group:Optional[_LGBM_GroupType]) -> 'Dataset'
Set group size of Dataset (used for ranking).
methodpython-package.lightgbm.basic.Dataset.set_init_score(init_score:Optional[_LGBM_InitScoreType]) -> 'Dataset'
Set init score of Booster to start from.
methodpython-package.lightgbm.basic.Dataset.set_label(label:Optional[_LGBM_LabelType]) -> 'Dataset'
Set label of Dataset.
methodpython-package.lightgbm.basic.Dataset.set_position(position:Optional[_LGBM_PositionType]) -> 'Dataset'
Set position of Dataset (used for ranking).
methodpython-package.lightgbm.basic.Dataset.set_reference(reference:'Dataset') -> 'Dataset'
Set reference Dataset.
methodpython-package.lightgbm.basic.Dataset.set_weight(weight:Optional[_LGBM_WeightType]) -> 'Dataset'
Set weight of each instance.
methodpython-package.lightgbm.basic.Dataset.subset(used_indices:List[int], params:Optional[Dict[str, Any]]=None) -> 'Dataset'
Get subset of current Dataset.
classpython-package.lightgbm.basic.EvalResult
Result from computing an evaluation metric on a dataset.
methodpython-package.lightgbm.basic.EvalResult.is_cv_result() -> bool
Whether the result was created by ``cv()``.
classpython-package.lightgbm.basic.LGBMDeprecationWarning
Custom deprecation warning.
classpython-package.lightgbm.basic.LightGBMError
Error thrown by LightGBM.
classpython-package.lightgbm.basic.Sequence
Generic data access interface.
funcpython-package.lightgbm.basic.register_logger(logger:Any, info_method_name:str='info', warning_method_name:str='warning') -> None
Register custom logger.
classpython-package.lightgbm.callback.EarlyStopException
Exception of early stopping.
funcpython-package.lightgbm.callback.log_evaluation(period:int=1, show_stdv:bool=True) -> _LogEvaluationCallback
Create a callback that logs the evaluation results.
classpython-package.lightgbm.compat.pd_CategoricalDtype
Dummy class for pandas.CategoricalDtype.
classpython-package.lightgbm.compat.pd_DataFrame
Dummy class for pandas.DataFrame.
classpython-package.lightgbm.compat.pd_Series
Dummy class for pandas.Series.
classpython-package.lightgbm.dask.DaskLGBMClassifier
Distributed version of lightgbm.LGBMClassifier.
classpython-package.lightgbm.dask.DaskLGBMRanker
Distributed version of lightgbm.LGBMRanker.
classpython-package.lightgbm.dask.DaskLGBMRegressor
Distributed version of lightgbm.LGBMRegressor.
classpython-package.lightgbm.engine.CVBooster
CVBooster in LightGBM.
methodpython-package.lightgbm.engine.CVBooster.model_from_string(model_str:str) -> 'CVBooster'
Load CVBooster from a string.
methodpython-package.lightgbm.engine.CVBooster.model_to_string(num_iteration:Optional[int]=None, start_iteration:int=0, importance_type:str='split') -> str
Save CVBooster to JSON string.
funcpython-package.lightgbm.plotting.add(root:Dict[str, Any], total_count:int, parent:Optional[str], decision:Optional[str], highlight:bool) -> None
Recursively add node or edge.
classpython-package.lightgbm.sklearn.LGBMClassifier
LightGBM classifier.
methodpython-package.lightgbm.sklearn.LGBMClassifier.n_classes_() -> int
:obj:`int`: The number of classes.
classpython-package.lightgbm.sklearn.LGBMModel
Implementation of the scikit-learn API for LightGBM.
methodpython-package.lightgbm.sklearn.LGBMModel.best_score_() -> _LGBM_BoosterBestScoreType
:obj:`dict`: The best score of fitted model.
methodpython-package.lightgbm.sklearn.LGBMModel.booster_() -> Booster
Booster: The underlying Booster of this model.
methodpython-package.lightgbm.sklearn.LGBMModel.get_params(deep:bool=True) -> Dict[str, Any]
Get parameters for this estimator.
methodpython-package.lightgbm.sklearn.LGBMModel.n_features_() -> int
:obj:`int`: The number of features of fitted model.
methodpython-package.lightgbm.sklearn.LGBMModel.set_params(**params:Any) -> 'LGBMModel'
Set the parameters of this estimator.
classpython-package.lightgbm.sklearn.LGBMRanker
LightGBM ranker.
classpython-package.lightgbm.sklearn.LGBMRegressor
LightGBM regressor.

この情報について

掲載しているシグネチャは lightgbm-org/LightGBM の公開ソースコードを Python の ast モジュールで静的解析し、引数名・デフォルト値・ 型注釈・戻り値型をそのまま抽出したものです。実装コードは保存していません。 詳しくは仕組みの解説をご覧ください。

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