lightgbm_callbacks.sklearn package

class lightgbm_callbacks.sklearn.EstimatorWrapperBase(estimator: TEstimator)

Bases: BaseEstimator, RegressorMixin, Generic[TEstimator]

A base class for estimator wrappers that delegates all attributes to the wrapped estimator.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → EstimatorWrapperBase

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to score.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

class lightgbm_callbacks.sklearn.LGBMDartEarlyStoppingEstimator(estimator: LGBMModel, *, early_stopping_factory: Callable[[int, bool, bool, float | list[float]], Callable[[CallbackEnv], None]] | Callable[[int, bool, bool], Callable[[CallbackEnv], None]] = <class 'lightgbm_callbacks._early_stopping_callback.DartEarlyStoppingCallback'>, stopping_rounds: int | None = None, first_metric_only: bool = False, verbose: bool = False, min_delta: float | None = None, eval_metric: str | Callable[[NDArray, NDArray], tuple[str, float, bool]] | None = None, test_size: float | int | None = None, train_size: float | int | None = None, random_state: int = 0, shuffle: bool = True, stratify: bool = False, split_enabled: bool = True, dart_early_stopping_method: Literal['save', 'refit', 'none'] = 'save', metric_idx: int = -1, tqdm_cls: Literal['auto', 'autonotebook', 'std', 'notebook', 'asyncio', 'keras', 'dask', 'tk', 'gui', 'rich', 'contrib.slack', 'contrib.discord', 'contrib.telegram', 'contrib.bells'] | type[tqdm.std.tqdm] | None = None, tqdm_kwargs: dict[str, Any] | None = None, **kwargs: Any)

Bases: LGBMEarlyStoppingEstimator

LightGBM wrapper that does early stopping with sklearn.train_test_split and uses LGBMDartEarlyStoppingSimpleWrapper to support early stopping in dart mode.

fit(X, y=None, **fit_params) → Self

Fit the model according to the given training data.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → LGBMDartEarlyStoppingEstimator

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to score.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

class lightgbm_callbacks.sklearn.LGBMDartEarlyStoppingSimpleWrapper(estimator: LGBMModel, *, method: Literal['save', 'refit', 'none'] = 'save', metric_idx: int = -1)

Bases: EstimatorWrapperBase[LGBMModel]

A simple wrapper for dart LGBMModel that returns the best model after early stopping.

fit(X, y, **fit_params: Any) → Self

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → LGBMDartEarlyStoppingSimpleWrapper

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to score.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

class lightgbm_callbacks.sklearn.LGBMEarlyStoppingEstimator(estimator: LGBMModel, *, early_stopping_factory: Callable[[int, bool, bool, float | list[float]], Callable[[CallbackEnv], None]] | Callable[[int, bool, bool], Callable[[CallbackEnv], None]] = <class 'lightgbm_callbacks._early_stopping_callback.DartEarlyStoppingCallback'>, stopping_rounds: int | None = None, first_metric_only: bool = False, verbose: bool = False, min_delta: float | None = None, eval_metric: str | Callable[[NDArray, NDArray], tuple[str, float, bool]] | None = None, test_size: float | int | None = None, train_size: float | int | None = None, random_state: int = 0, shuffle: bool = True, stratify: bool = False, split_enabled: bool = True, tqdm_cls: Literal['auto', 'autonotebook', 'std', 'notebook', 'asyncio', 'keras', 'dask', 'tk', 'gui', 'rich', 'contrib.slack', 'contrib.discord', 'contrib.telegram', 'contrib.bells'] | type[tqdm.std.tqdm] | None = None, tqdm_kwargs: dict[str, Any] | None = None, **kwargs: Any)

Bases: EstimatorWrapperBase[LGBMModel]

LightGBM wrapper that does early stopping with sklearn.train_test_split.

fit(X, y=None, **fit_params) → Self

Fit the model according to the given training data.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → LGBMEarlyStoppingEstimator

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to score.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object