lightgbm.sklearn Namespace Reference

Data Structures
class	LGBMClassifier
class	LGBMModel
class	LGBMRanker
class	LGBMRegressor

Functions
	_objective_function_wrapper (func)
	_eval_function_wrapper (func)

Detailed Description

Scikit-learn wrapper interface for LightGBM.

Function Documentation

_eval_function_wrapper()

lightgbm.sklearn._eval_function_wrapper (   func )

protected

Decorate an eval function.

Note
----
For multi-class task, the y_pred is group by class_id first, then group by row_id.
If you want to get i-th row y_pred in j-th class, the access way is y_pred[j * num_data + i].

Parameters
----------
func : callable
    Expects a callable with following signatures:
    ``func(y_true, y_pred)``,
    ``func(y_true, y_pred, weight)``
    or ``func(y_true, y_pred, weight, group)``
    and returns (eval_name->string, eval_result->float, is_bigger_better->bool):

        y_true : array-like of shape = [n_samples]
            The target values.
        y_pred : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
            The predicted values.
        weight : array-like of shape = [n_samples]
            The weight of samples.
        group : array-like
            Group/query data, used for ranking task.

Returns
-------
new_func : callable
    The new eval function as expected by ``lightgbm.engine.train``.
    The signature is ``new_func(preds, dataset)``:

        preds : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
            The predicted values.
        dataset : Dataset
            The training set from which the labels will be extracted using ``dataset.get_label()``.

_objective_function_wrapper()

lightgbm.sklearn._objective_function_wrapper (   func )

protected

Decorate an objective function.

Note
----
For multi-class task, the y_pred is group by class_id first, then group by row_id.
If you want to get i-th row y_pred in j-th class, the access way is y_pred[j * num_data + i]
and you should group grad and hess in this way as well.

Parameters
----------
func : callable
    Expects a callable with signature ``func(y_true, y_pred)`` or ``func(y_true, y_pred, group):

        y_true : array-like of shape = [n_samples]
            The target values.
        y_pred : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
            The predicted values.
        group : array-like
            Group/query data, used for ranking task.

Returns
-------
new_func : callable
    The new objective function as expected by ``lightgbm.engine.train``.
    The signature is ``new_func(preds, dataset)``:

        preds : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
            The predicted values.
        dataset : Dataset
            The training set from which the labels will be extracted using ``dataset.get_label()``.