LightGBM API reference

88 public APIs from LightGBM (lightgbm-org/LightGBM) — 18 classes, 10 functions, 60 methods. Signatures extracted by static analysis of the actual source.

Repository: lightgbm-org/LightGBM

Kind	Count
Classes	18
Functions	10
Methods	60

API list

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.

method

python-package.lightgbm.basic.Booster.eval(data:Dataset, name:str, feval:Optional[Union[_LGBM_CustomEvalFunction, List[_LGBM_CustomEvalFunction]]]=None) -> List[EvalResult]

Evaluate for data.

method

python-package.lightgbm.basic.Booster.eval_train(feval:Optional[Union[_LGBM_CustomEvalFunction, List[_LGBM_CustomEvalFunction]]]=None) -> List[EvalResult]

Evaluate for training data.

method

python-package.lightgbm.basic.Booster.eval_valid(feval:Optional[Union[_LGBM_CustomEvalFunction, List[_LGBM_CustomEvalFunction]]]=None) -> List[EvalResult]

Evaluate for validation data.

method

python-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.

method

python-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.

method

python-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.

method

python-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.

method

python-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.

method

python-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.

func

python-package.lightgbm.basic.register_logger(logger:Any, info_method_name:str='info', warning_method_name:str='warning') -> None

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.

method

python-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.

func

python-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.

About this data

These signatures were extracted from the public source of lightgbm-org/LightGBM using Python's ast module. Argument names, default values, type annotations and return types are taken verbatim from the code. Implementation bodies are never stored. See how it works for details.

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