sklearn.py

# coding: utf-8
"""Scikit-learn wrapper interface for LightGBM."""
import copy
from inspect import signature
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import numpy as np

from .basic import Booster, Dataset, LightGBMError, _choose_param_value, _ConfigAliases, _log_warning
from .callback import record_evaluation
from .compat import (SKLEARN_INSTALLED, LGBMNotFittedError, _LGBMAssertAllFinite, _LGBMCheckArray,
                     _LGBMCheckClassificationTargets, _LGBMCheckSampleWeight, _LGBMCheckXY, _LGBMClassifierBase,
                     _LGBMComputeSampleWeight, _LGBMCpuCount, _LGBMLabelEncoder, _LGBMModelBase, _LGBMRegressorBase,
                     dt_DataTable, pd_DataFrame)
from .engine import train

_EvalResultType = Tuple[str, float, bool]

_LGBM_ScikitCustomObjectiveFunction = Union[
    Callable[
        [np.ndarray, np.ndarray],
        Tuple[np.ndarray, np.ndarray]
    ],
    Callable[
        [np.ndarray, np.ndarray, np.ndarray],
        Tuple[np.ndarray, np.ndarray]
    ],
]
_LGBM_ScikitCustomEvalFunction = Union[
    Callable[
        [np.ndarray, np.ndarray],
        Union[_EvalResultType, List[_EvalResultType]]
    ],
    Callable[
        [np.ndarray, np.ndarray, np.ndarray],
        Union[_EvalResultType, List[_EvalResultType]]
    ],
    Callable[
        [np.ndarray, np.ndarray, np.ndarray, np.ndarray],
        Union[_EvalResultType, List[_EvalResultType]]
    ],
]


class _ObjectiveFunctionWrapper:
    """Proxy class for objective function."""

    def __init__(self, func: _LGBM_ScikitCustomObjectiveFunction):
        """Construct a proxy class.

        This class transforms objective function to match objective function with signature ``new_func(preds, dataset)``
        as expected by ``lightgbm.engine.train``.

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

                y_true : numpy 1-D array of shape = [n_samples]
                    The target values.
                y_pred : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task)
                    The predicted values.
                    Predicted values are returned before any transformation,
                    e.g. they are raw margin instead of probability of positive class for binary task.
                group : numpy 1-D array
                    Group/query data.
                    Only used in the learning-to-rank task.
                    sum(group) = n_samples.
                    For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,
                    where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.
                grad : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape [n_samples, n_classes] (for multi-class task)
                    The value of the first order derivative (gradient) of the loss
                    with respect to the elements of y_pred for each sample point.
                hess : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task)
                    The value of the second order derivative (Hessian) of the loss
                    with respect to the elements of y_pred for each sample point.

        .. note::

            For multi-class task, y_pred is a numpy 2-D array of shape = [n_samples, n_classes],
            and grad and hess should be returned in the same format.
        """
        self.func = func

    def __call__(self, preds: np.ndarray, dataset: Dataset) -> Tuple[np.ndarray, np.ndarray]:
        """Call passed function with appropriate arguments.

        Parameters
        ----------
        preds : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task)
            The predicted values.
        dataset : Dataset
            The training dataset.

        Returns
        -------
        grad : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task)
            The value of the first order derivative (gradient) of the loss
            with respect to the elements of preds for each sample point.
        hess : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task)
            The value of the second order derivative (Hessian) of the loss
            with respect to the elements of preds for each sample point.
        """
        labels = dataset.get_label()
        argc = len(signature(self.func).parameters)
        if argc == 2:
            grad, hess = self.func(labels, preds)
        elif argc == 3:
            grad, hess = self.func(labels, preds, dataset.get_group())
        else:
            raise TypeError(f"Self-defined objective function should have 2 or 3 arguments, got {argc}")
        """weighted for objective"""
        weight = dataset.get_weight()
        if weight is not None:
            if grad.ndim == 2:  # multi-class
                num_data = grad.shape[0]
                if weight.size != num_data:
                    raise ValueError("grad and hess should be of shape [n_samples, n_classes]")
                weight = weight.reshape(num_data, 1)
            grad *= weight
            hess *= weight
        return grad, hess


class _EvalFunctionWrapper:
    """Proxy class for evaluation function."""

    def __init__(self, func: _LGBM_ScikitCustomEvalFunction):
        """Construct a proxy class.

        This class transforms evaluation function to match evaluation function with signature ``new_func(preds, dataset)``
        as expected by ``lightgbm.engine.train``.

        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, eval_result, is_higher_better) or
            list of (eval_name, eval_result, is_higher_better):

                y_true : numpy 1-D array of shape = [n_samples]
                    The target values.
                y_pred : numpy 1-D array of shape = [n_samples] or numpy 2-D array shape = [n_samples, n_classes] (for multi-class task)
                    The predicted values.
                    In case of custom ``objective``, predicted values are returned before any transformation,
                    e.g. they are raw margin instead of probability of positive class for binary task in this case.
                weight : numpy 1-D array of shape = [n_samples]
                    The weight of samples. Weights should be non-negative.
                group : numpy 1-D array
                    Group/query data.
                    Only used in the learning-to-rank task.
                    sum(group) = n_samples.
                    For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,
                    where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.
                eval_name : str
                    The name of evaluation function (without whitespace).
                eval_result : float
                    The eval result.
                is_higher_better : bool
                    Is eval result higher better, e.g. AUC is ``is_higher_better``.
        """
        self.func = func

    def __call__(self, preds: np.ndarray, dataset: Dataset) -> Tuple[str, float, bool]:
        """Call passed function with appropriate arguments.

        Parameters
        ----------
        preds : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task)
            The predicted values.
        dataset : Dataset
            The training dataset.

        Returns
        -------
        eval_name : str
            The name of evaluation function (without whitespace).
        eval_result : float
            The eval result.
        is_higher_better : bool
            Is eval result higher better, e.g. AUC is ``is_higher_better``.
        """
        labels = dataset.get_label()
        argc = len(signature(self.func).parameters)
        if argc == 2:
            return self.func(labels, preds)
        elif argc == 3:
            return self.func(labels, preds, dataset.get_weight())
        elif argc == 4:
            return self.func(labels, preds, dataset.get_weight(), dataset.get_group())
        else:
            raise TypeError(f"Self-defined eval function should have 2, 3 or 4 arguments, got {argc}")


# documentation templates for LGBMModel methods are shared between the classes in
# this module and those in the ``dask`` module

_lgbmmodel_doc_fit = (
    """
    Build a gradient boosting model from the training set (X, y).

    Parameters
    ----------
    X : {X_shape}
        Input feature matrix.
    y : {y_shape}
        The target values (class labels in classification, real numbers in regression).
    sample_weight : {sample_weight_shape}
        Weights of training data. Weights should be non-negative.
    init_score : {init_score_shape}
        Init score of training data.
    group : {group_shape}
        Group/query data.
        Only used in the learning-to-rank task.
        sum(group) = n_samples.
        For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,
        where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.
    eval_set : list or None, optional (default=None)
        A list of (X, y) tuple pairs to use as validation sets.
    eval_names : list of str, or None, optional (default=None)
        Names of eval_set.
    eval_sample_weight : {eval_sample_weight_shape}
        Weights of eval data. Weights should be non-negative.
    eval_class_weight : list or None, optional (default=None)
        Class weights of eval data.
    eval_init_score : {eval_init_score_shape}
        Init score of eval data.
    eval_group : {eval_group_shape}
        Group data of eval data.
    eval_metric : str, callable, list or None, optional (default=None)
        If str, it should be a built-in evaluation metric to use.
        If callable, it should be a custom evaluation metric, see note below for more details.
        If list, it can be a list of built-in metrics, a list of custom evaluation metrics, or a mix of both.
        In either case, the ``metric`` from the model parameters will be evaluated and used as well.
        Default: 'l2' for LGBMRegressor, 'logloss' for LGBMClassifier, 'ndcg' for LGBMRanker.
    feature_name : list of str, or 'auto', optional (default='auto')
        Feature names.
        If 'auto' and data is pandas DataFrame, data columns names are used.
    categorical_feature : list of str or int, or 'auto', optional (default='auto')
        Categorical features.
        If list of int, interpreted as indices.
        If list of str, interpreted as feature names (need to specify ``feature_name`` as well).
        If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used.
        All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647).
        Large values could be memory consuming. Consider using consecutive integers starting from zero.
        All negative values in categorical features will be treated as missing values.
        The output cannot be monotonically constrained with respect to a categorical feature.
        Floating point numbers in categorical features will be rounded towards 0.
    callbacks : list of callable, or None, optional (default=None)
        List of callback functions that are applied at each iteration.
        See Callbacks in Python API for more information.
    init_model : str, pathlib.Path, Booster, LGBMModel or None, optional (default=None)
        Filename of LightGBM model, Booster instance or LGBMModel instance used for continue training.

    Returns
    -------
    self : LGBMModel
        Returns self.
    """
)

_lgbmmodel_doc_custom_eval_note = """
    Note
    ----
    Custom eval function 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, eval_result, is_higher_better) or
    list of (eval_name, eval_result, is_higher_better):

        y_true : numpy 1-D array of shape = [n_samples]
            The target values.
        y_pred : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task)
            The predicted values.
            In case of custom ``objective``, predicted values are returned before any transformation,
            e.g. they are raw margin instead of probability of positive class for binary task in this case.
        weight : numpy 1-D array of shape = [n_samples]
            The weight of samples. Weights should be non-negative.
        group : numpy 1-D array
            Group/query data.
            Only used in the learning-to-rank task.
            sum(group) = n_samples.
            For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,
            where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.
        eval_name : str
            The name of evaluation function (without whitespace).
        eval_result : float
            The eval result.
        is_higher_better : bool
            Is eval result higher better, e.g. AUC is ``is_higher_better``.
"""

_lgbmmodel_doc_predict = (
    """
    {description}

    Parameters
    ----------
    X : {X_shape}
        Input features matrix.
    raw_score : bool, optional (default=False)
        Whether to predict raw scores.
    start_iteration : int, optional (default=0)
        Start index of the iteration to predict.
        If <= 0, starts from the first iteration.
    num_iteration : int or None, optional (default=None)
        Total number of iterations used in the prediction.
        If None, if the best iteration exists and start_iteration <= 0, the best iteration is used;
        otherwise, all iterations from ``start_iteration`` are used (no limits).
        If <= 0, all iterations from ``start_iteration`` are used (no limits).
    pred_leaf : bool, optional (default=False)
        Whether to predict leaf index.
    pred_contrib : bool, optional (default=False)
        Whether to predict feature contributions.

        .. note::

            If you want to get more explanations for your model's predictions using SHAP values,
            like SHAP interaction values,
            you can install the shap package (https://github.com/slundberg/shap).
            Note that unlike the shap package, with ``pred_contrib`` we return a matrix with an extra
            column, where the last column is the expected value.

    **kwargs
        Other parameters for the prediction.

    Returns
    -------
    {output_name} : {predicted_result_shape}
        The predicted values.
    X_leaves : {X_leaves_shape}
        If ``pred_leaf=True``, the predicted leaf of every tree for each sample.
    X_SHAP_values : {X_SHAP_values_shape}
        If ``pred_contrib=True``, the feature contributions for each sample.
    """
)


class LGBMModel(_LGBMModelBase):
    """Implementation of the scikit-learn API for LightGBM."""

    def __init__(
        self,
        boosting_type: str = 'gbdt',
        num_leaves: int = 31,
        max_depth: int = -1,
        learning_rate: float = 0.1,
        n_estimators: int = 100,
        subsample_for_bin: int = 200000,
        objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None,
        class_weight: Optional[Union[Dict, str]] = None,
        min_split_gain: float = 0.,
        min_child_weight: float = 1e-3,
        min_child_samples: int = 20,
        subsample: float = 1.,
        subsample_freq: int = 0,
        colsample_bytree: float = 1.,
        reg_alpha: float = 0.,
        reg_lambda: float = 0.,
        random_state: Optional[Union[int, np.random.RandomState]] = None,
        n_jobs: Optional[int] = None,
        importance_type: str = 'split',
        **kwargs
    ):
        r"""Construct a gradient boosting model.

        Parameters
        ----------
        boosting_type : str, optional (default='gbdt')
            'gbdt', traditional Gradient Boosting Decision Tree.
            'dart', Dropouts meet Multiple Additive Regression Trees.
            'goss', Gradient-based One-Side Sampling.
            'rf', Random Forest.
        num_leaves : int, optional (default=31)
            Maximum tree leaves for base learners.
        max_depth : int, optional (default=-1)
            Maximum tree depth for base learners, <=0 means no limit.
        learning_rate : float, optional (default=0.1)
            Boosting learning rate.
            You can use ``callbacks`` parameter of ``fit`` method to shrink/adapt learning rate
            in training using ``reset_parameter`` callback.
            Note, that this will ignore the ``learning_rate`` argument in training.
        n_estimators : int, optional (default=100)
            Number of boosted trees to fit.
        subsample_for_bin : int, optional (default=200000)
            Number of samples for constructing bins.
        objective : str, callable or None, optional (default=None)
            Specify the learning task and the corresponding learning objective or
            a custom objective function to be used (see note below).
            Default: 'regression' for LGBMRegressor, 'binary' or 'multiclass' for LGBMClassifier, 'lambdarank' for LGBMRanker.
        class_weight : dict, 'balanced' or None, optional (default=None)
            Weights associated with classes in the form ``{class_label: weight}``.
            Use this parameter only for multi-class classification task;
            for binary classification task you may use ``is_unbalance`` or ``scale_pos_weight`` parameters.
            Note, that the usage of all these parameters will result in poor estimates of the individual class probabilities.
            You may want to consider performing probability calibration
            (https://scikit-learn.org/stable/modules/calibration.html) of your model.
            The 'balanced' mode uses the values of y to automatically adjust weights
            inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))``.
            If None, all classes are supposed to have weight one.
            Note, that these weights will be multiplied with ``sample_weight`` (passed through the ``fit`` method)
            if ``sample_weight`` is specified.
        min_split_gain : float, optional (default=0.)
            Minimum loss reduction required to make a further partition on a leaf node of the tree.
        min_child_weight : float, optional (default=1e-3)
            Minimum sum of instance weight (Hessian) needed in a child (leaf).
        min_child_samples : int, optional (default=20)
            Minimum number of data needed in a child (leaf).
        subsample : float, optional (default=1.)
            Subsample ratio of the training instance.
        subsample_freq : int, optional (default=0)
            Frequency of subsample, <=0 means no enable.
        colsample_bytree : float, optional (default=1.)
            Subsample ratio of columns when constructing each tree.
        reg_alpha : float, optional (default=0.)
            L1 regularization term on weights.
        reg_lambda : float, optional (default=0.)
            L2 regularization term on weights.
        random_state : int, RandomState object or None, optional (default=None)
            Random number seed.
            If int, this number is used to seed the C++ code.
            If RandomState object (numpy), a random integer is picked based on its state to seed the C++ code.
            If None, default seeds in C++ code are used.
        n_jobs : int or None, optional (default=None)
            Number of parallel threads to use for training (can be changed at prediction time by
            passing it as an extra keyword argument).

            For better performance, it is recommended to set this to the number of physical cores
            in the CPU.

            Negative integers are interpreted as following joblib's formula (n_cpus + 1 + n_jobs), just like
            scikit-learn (so e.g. -1 means using all threads). A value of zero corresponds the default number of
            threads configured for OpenMP in the system. A value of ``None`` (the default) corresponds
            to using the number of physical cores in the system (its correct detection requires
            either the ``joblib`` or the ``psutil`` util libraries to be installed).
        importance_type : str, optional (default='split')
            The type of feature importance to be filled into ``feature_importances_``.
            If 'split', result contains numbers of times the feature is used in a model.
            If 'gain', result contains total gains of splits which use the feature.
        **kwargs
            Other parameters for the model.
            Check http://lightgbm.readthedocs.io/en/latest/Parameters.html for more parameters.

            .. warning::

                \*\*kwargs is not supported in sklearn, it may cause unexpected issues.

        Note
        ----
        A custom objective function can be provided for the ``objective`` parameter.
        In this case, it should have the signature
        ``objective(y_true, y_pred) -> grad, hess`` or
        ``objective(y_true, y_pred, group) -> grad, hess``:

            y_true : numpy 1-D array of shape = [n_samples]
                The target values.
            y_pred : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task)
                The predicted values.
                Predicted values are returned before any transformation,
                e.g. they are raw margin instead of probability of positive class for binary task.
            group : numpy 1-D array
                Group/query data.
                Only used in the learning-to-rank task.
                sum(group) = n_samples.
                For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,
                where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.
            grad : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task)
                The value of the first order derivative (gradient) of the loss
                with respect to the elements of y_pred for each sample point.
            hess : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task)
                The value of the second order derivative (Hessian) of the loss
                with respect to the elements of y_pred for each sample point.

        For multi-class task, y_pred is a numpy 2-D array of shape = [n_samples, n_classes],
        and grad and hess should be returned in the same format.
        """
        if not SKLEARN_INSTALLED:
            raise LightGBMError('scikit-learn is required for lightgbm.sklearn. '
                                'You must install scikit-learn and restart your session to use this module.')

        self.boosting_type = boosting_type
        self.objective = objective
        self.num_leaves = num_leaves
        self.max_depth = max_depth
        self.learning_rate = learning_rate
        self.n_estimators = n_estimators
        self.subsample_for_bin = subsample_for_bin
        self.min_split_gain = min_split_gain
        self.min_child_weight = min_child_weight
        self.min_child_samples = min_child_samples
        self.subsample = subsample
        self.subsample_freq = subsample_freq
        self.colsample_bytree = colsample_bytree
        self.reg_alpha = reg_alpha
        self.reg_lambda = reg_lambda
        self.random_state = random_state
        self.n_jobs = n_jobs
        self.importance_type = importance_type
        self._Booster: Optional[Booster] = None
        self._evals_result = None
        self._best_score = None
        self._best_iteration = None
        self._other_params: Dict[str, Any] = {}
        self._objective = objective
        self.class_weight = class_weight
        self._class_weight = None
        self._class_map = None
        self._n_features = None
        self._n_features_in = None
        self._classes = None
        self._n_classes = None
        self.set_params(**kwargs)

    def _more_tags(self) -> Dict[str, Any]:
        return {
            'allow_nan': True,
            'X_types': ['2darray', 'sparse', '1dlabels'],
            '_xfail_checks': {
                'check_no_attributes_set_in_init':
                'scikit-learn incorrectly asserts that private attributes '
                'cannot be set in __init__: '
                '(see https://github.com/microsoft/LightGBM/issues/2628)'
            }
        }

    def __sklearn_is_fitted__(self) -> bool:
        return getattr(self, "fitted_", False)

    def get_params(self, deep: bool = True) -> Dict[str, Any]:
        """Get parameters for this estimator.

        Parameters
        ----------
        deep : bool, optional (default=True)
            If True, will return the parameters for this estimator and
            contained subobjects that are estimators.

        Returns
        -------
        params : dict
            Parameter names mapped to their values.
        """
        params = super().get_params(deep=deep)
        params.update(self._other_params)
        return params

    def set_params(self, **params: Any) -> "LGBMModel":
        """Set the parameters of this estimator.

        Parameters
        ----------
        **params
            Parameter names with their new values.

        Returns
        -------
        self : object
            Returns self.
        """
        for key, value in params.items():
            setattr(self, key, value)
            if hasattr(self, f"_{key}"):
                setattr(self, f"_{key}", value)
            self._other_params[key] = value
        return self

    def _process_params(self, stage: str) -> Dict[str, Any]:
        """Process the parameters of this estimator based on its type, parameter aliases, etc.

        Parameters
        ----------
        stage : str
            Name of the stage (can be ``fit`` or ``predict``) this method is called from.

        Returns
        -------
        processed_params : dict
            Processed parameter names mapped to their values.
        """
        assert stage in {"fit", "predict"}
        params = self.get_params()

        params.pop('objective', None)
        for alias in _ConfigAliases.get('objective'):
            if alias in params:
                obj = params.pop(alias)
                _log_warning(f"Found '{alias}' in params. Will use it instead of 'objective' argument")
                if stage == "fit":
                    self._objective = obj
        if stage == "fit":
            if self._objective is None:
                if isinstance(self, LGBMRegressor):
                    self._objective = "regression"
                elif isinstance(self, LGBMClassifier):
                    if self._n_classes > 2:
                        self._objective = "multiclass"
                    else:
                        self._objective = "binary"
                elif isinstance(self, LGBMRanker):
                    self._objective = "lambdarank"
                else:
                    raise ValueError("Unknown LGBMModel type.")
        if callable(self._objective):
            if stage == "fit":
                params['objective'] = _ObjectiveFunctionWrapper(self._objective)
            else:
                params['objective'] = 'None'
        else:
            params['objective'] = self._objective

        params.pop('importance_type', None)
        params.pop('n_estimators', None)
        params.pop('class_weight', None)

        if isinstance(params['random_state'], np.random.RandomState):
            params['random_state'] = params['random_state'].randint(np.iinfo(np.int32).max)
        if self._n_classes is not None and self._n_classes > 2:
            for alias in _ConfigAliases.get('num_class'):
                params.pop(alias, None)
            params['num_class'] = self._n_classes
        if hasattr(self, '_eval_at'):
            eval_at = self._eval_at
            for alias in _ConfigAliases.get('eval_at'):
                if alias in params:
                    _log_warning(f"Found '{alias}' in params. Will use it instead of 'eval_at' argument")
                    eval_at = params.pop(alias)
            params['eval_at'] = eval_at

        # register default metric for consistency with callable eval_metric case
        original_metric = self._objective if isinstance(self._objective, str) else None
        if original_metric is None:
            # try to deduce from class instance
            if isinstance(self, LGBMRegressor):
                original_metric = "l2"
            elif isinstance(self, LGBMClassifier):
                original_metric = "multi_logloss" if self._n_classes > 2 else "binary_logloss"
            elif isinstance(self, LGBMRanker):
                original_metric = "ndcg"

        # overwrite default metric by explicitly set metric
        params = _choose_param_value("metric", params, original_metric)

        # use joblib conventions for negative n_jobs, just like scikit-learn
        # at predict time, this is handled later due to the order of parameter updates
        if stage == "fit":
            params = _choose_param_value("num_threads", params, self.n_jobs)
            params["num_threads"] = self._process_n_jobs(params["num_threads"])

        return params

    def _process_n_jobs(self, n_jobs: Optional[int]) -> int:
        """Convert special values of n_jobs to their actual values according to the formulas that apply.

        Parameters
        ----------
        n_jobs : int or None
            The original value of n_jobs, potentially having special values such as 'None' or
            negative integers.

        Returns
        -------
        n_jobs : int
            The value of n_jobs with special values converted to actual number of threads.
        """
        if n_jobs is None:
            n_jobs = _LGBMCpuCount(only_physical_cores=True)
        elif n_jobs < 0:
            n_jobs = max(_LGBMCpuCount(only_physical_cores=False) + 1 + n_jobs, 1)
        return n_jobs

    def fit(
        self,
        X,
        y,
        sample_weight=None,
        init_score=None,
        group=None,
        eval_set=None,
        eval_names=None,
        eval_sample_weight=None,
        eval_class_weight=None,
        eval_init_score=None,
        eval_group=None,
        eval_metric=None,
        feature_name='auto',
        categorical_feature='auto',
        callbacks=None,
        init_model=None
    ):
        """Docstring is set after definition, using a template."""
        params = self._process_params(stage="fit")

        # Do not modify original args in fit function
        # Refer to https://github.com/microsoft/LightGBM/pull/2619
        eval_metric_list = copy.deepcopy(eval_metric)
        if not isinstance(eval_metric_list, list):
            eval_metric_list = [eval_metric_list]

        # Separate built-in from callable evaluation metrics
        eval_metrics_callable = [_EvalFunctionWrapper(f) for f in eval_metric_list if callable(f)]
        eval_metrics_builtin = [m for m in eval_metric_list if isinstance(m, str)]

        # concatenate metric from params (or default if not provided in params) and eval_metric
        params['metric'] = [params['metric']] if isinstance(params['metric'], (str, type(None))) else params['metric']
        params['metric'] = [e for e in eval_metrics_builtin if e not in params['metric']] + params['metric']
        params['metric'] = [metric for metric in params['metric'] if metric is not None]

        if not isinstance(X, (pd_DataFrame, dt_DataTable)):
            _X, _y = _LGBMCheckXY(X, y, accept_sparse=True, force_all_finite=False, ensure_min_samples=2)
            if sample_weight is not None:
                sample_weight = _LGBMCheckSampleWeight(sample_weight, _X)
        else:
            _X, _y = X, y

        if self._class_weight is None:
            self._class_weight = self.class_weight
        if self._class_weight is not None:
            class_sample_weight = _LGBMComputeSampleWeight(self._class_weight, y)
            if sample_weight is None or len(sample_weight) == 0:
                sample_weight = class_sample_weight
            else:
                sample_weight = np.multiply(sample_weight, class_sample_weight)

        self._n_features = _X.shape[1]
        # copy for consistency
        self._n_features_in = self._n_features

        def _construct_dataset(X, y, sample_weight, init_score, group, params,
                               categorical_feature='auto'):
            return Dataset(X, label=y, weight=sample_weight, group=group,
                           init_score=init_score, params=params,
                           categorical_feature=categorical_feature)

        train_set = _construct_dataset(_X, _y, sample_weight, init_score, group, params,
                                       categorical_feature=categorical_feature)

        valid_sets = []
        if eval_set is not None:

            def _get_meta_data(collection, name, i):
                if collection is None:
                    return None
                elif isinstance(collection, list):
                    return collection[i] if len(collection) > i else None
                elif isinstance(collection, dict):
                    return collection.get(i, None)
                else:
                    raise TypeError(f"{name} should be dict or list")

            if isinstance(eval_set, tuple):
                eval_set = [eval_set]
            for i, valid_data in enumerate(eval_set):
                # reduce cost for prediction training data
                if valid_data[0] is X and valid_data[1] is y:
                    valid_set = train_set
                else:
                    valid_weight = _get_meta_data(eval_sample_weight, 'eval_sample_weight', i)
                    valid_class_weight = _get_meta_data(eval_class_weight, 'eval_class_weight', i)
                    if valid_class_weight is not None:
                        if isinstance(valid_class_weight, dict) and self._class_map is not None:
                            valid_class_weight = {self._class_map[k]: v for k, v in valid_class_weight.items()}
                        valid_class_sample_weight = _LGBMComputeSampleWeight(valid_class_weight, valid_data[1])
                        if valid_weight is None or len(valid_weight) == 0:
                            valid_weight = valid_class_sample_weight
                        else:
                            valid_weight = np.multiply(valid_weight, valid_class_sample_weight)
                    valid_init_score = _get_meta_data(eval_init_score, 'eval_init_score', i)
                    valid_group = _get_meta_data(eval_group, 'eval_group', i)
                    valid_set = _construct_dataset(valid_data[0], valid_data[1],
                                                   valid_weight, valid_init_score, valid_group, params)
                valid_sets.append(valid_set)

        if isinstance(init_model, LGBMModel):
            init_model = init_model.booster_

        if callbacks is None:
            callbacks = []
        else:
            callbacks = copy.copy(callbacks)  # don't use deepcopy here to allow non-serializable objects

        evals_result = {}
        callbacks.append(record_evaluation(evals_result))

        self._Booster = train(
            params=params,
            train_set=train_set,
            num_boost_round=self.n_estimators,
            valid_sets=valid_sets,
            valid_names=eval_names,
            feval=eval_metrics_callable,
            init_model=init_model,
            feature_name=feature_name,
            callbacks=callbacks
        )

        self._evals_result = evals_result
        self._best_iteration = self._Booster.best_iteration
        self._best_score = self._Booster.best_score

        self.fitted_ = True

        # free dataset
        self._Booster.free_dataset()
        del train_set, valid_sets
        return self

    fit.__doc__ = _lgbmmodel_doc_fit.format(
        X_shape="array-like or sparse matrix of shape = [n_samples, n_features]",
        y_shape="array-like of shape = [n_samples]",
        sample_weight_shape="array-like of shape = [n_samples] or None, optional (default=None)",
        init_score_shape="array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task) or shape = [n_samples, n_classes] (for multi-class task) or None, optional (default=None)",
        group_shape="array-like or None, optional (default=None)",
        eval_sample_weight_shape="list of array, or None, optional (default=None)",
        eval_init_score_shape="list of array, or None, optional (default=None)",
        eval_group_shape="list of array, or None, optional (default=None)"
    ) + "\n\n" + _lgbmmodel_doc_custom_eval_note

    def predict(self, X, raw_score=False, start_iteration=0, num_iteration=None,
                pred_leaf=False, pred_contrib=False, **kwargs):
        """Docstring is set after definition, using a template."""
        if not self.__sklearn_is_fitted__():
            raise LGBMNotFittedError("Estimator not fitted, call fit before exploiting the model.")
        if not isinstance(X, (pd_DataFrame, dt_DataTable)):
            X = _LGBMCheckArray(X, accept_sparse=True, force_all_finite=False)
        n_features = X.shape[1]
        if self._n_features != n_features:
            raise ValueError("Number of features of the model must "
                             f"match the input. Model n_features_ is {self._n_features} and "
                             f"input n_features is {n_features}")
        # retrive original params that possibly can be used in both training and prediction
        # and then overwrite them (considering aliases) with params that were passed directly in prediction
        predict_params = self._process_params(stage="predict")
        for alias in _ConfigAliases.get_by_alias(
            "data",
            "X",
            "raw_score",
            "start_iteration",
            "num_iteration",
            "pred_leaf",
            "pred_contrib",
            *kwargs.keys()
        ):
            predict_params.pop(alias, None)
        predict_params.update(kwargs)

        # number of threads can have values with special meaning which is only applied
        # in the scikit-learn interface, these should not reach the c++ side as-is
        predict_params = _choose_param_value("num_threads", predict_params, self.n_jobs)
        predict_params["num_threads"] = self._process_n_jobs(predict_params["num_threads"])

        return self._Booster.predict(X, raw_score=raw_score, start_iteration=start_iteration, num_iteration=num_iteration,
                                     pred_leaf=pred_leaf, pred_contrib=pred_contrib, **predict_params)

    predict.__doc__ = _lgbmmodel_doc_predict.format(
        description="Return the predicted value for each sample.",
        X_shape="array-like or sparse matrix of shape = [n_samples, n_features]",
        output_name="predicted_result",
        predicted_result_shape="array-like of shape = [n_samples] or shape = [n_samples, n_classes]",
        X_leaves_shape="array-like of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]",
        X_SHAP_values_shape="array-like of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes] or list with n_classes length of such objects"
    )

    @property
    def n_features_(self) -> int:
        """:obj:`int`: The number of features of fitted model."""
        if not self.__sklearn_is_fitted__():
            raise LGBMNotFittedError('No n_features found. Need to call fit beforehand.')
        return self._n_features

    @property
    def n_features_in_(self) -> int:
        """:obj:`int`: The number of features of fitted model."""
        if not self.__sklearn_is_fitted__():
            raise LGBMNotFittedError('No n_features_in found. Need to call fit beforehand.')
        return self._n_features_in

    @property
    def best_score_(self):
        """:obj:`dict`: The best score of fitted model."""
        if not self.__sklearn_is_fitted__():
            raise LGBMNotFittedError('No best_score found. Need to call fit beforehand.')
        return self._best_score

    @property
    def best_iteration_(self) -> int:
        """:obj:`int`: The best iteration of fitted model if ``early_stopping()`` callback has been specified."""
        if not self.__sklearn_is_fitted__():
            raise LGBMNotFittedError('No best_iteration found. Need to call fit with early_stopping callback beforehand.')
        return self._best_iteration

    @property
    def objective_(self) -> Union[str, _LGBM_ScikitCustomObjectiveFunction]:
        """:obj:`str` or :obj:`callable`: The concrete objective used while fitting this model."""
        if not self.__sklearn_is_fitted__():
            raise LGBMNotFittedError('No objective found. Need to call fit beforehand.')
        return self._objective

    @property
    def n_estimators_(self) -> int:
        """:obj:`int`: True number of boosting iterations performed.

        This might be less than parameter ``n_estimators`` if early stopping was enabled or
        if boosting stopped early due to limits on complexity like ``min_gain_to_split``.
        """
        if not self.__sklearn_is_fitted__():
            raise LGBMNotFittedError('No n_estimators found. Need to call fit beforehand.')
        return self._Booster.current_iteration()  # type: ignore

    @property
    def n_iter_(self) -> int:
        """:obj:`int`: True number of boosting iterations performed.

        This might be less than parameter ``n_estimators`` if early stopping was enabled or
        if boosting stopped early due to limits on complexity like ``min_gain_to_split``.
        """
        if not self.__sklearn_is_fitted__():
            raise LGBMNotFittedError('No n_iter found. Need to call fit beforehand.')
        return self._Booster.current_iteration()  # type: ignore

    @property
    def booster_(self):
        """Booster: The underlying Booster of this model."""
        if not self.__sklearn_is_fitted__():
            raise LGBMNotFittedError('No booster found. Need to call fit beforehand.')
        return self._Booster

    @property
    def evals_result_(self):
        """:obj:`dict`: The evaluation results if validation sets have been specified."""
        if not self.__sklearn_is_fitted__():
            raise LGBMNotFittedError('No results found. Need to call fit with eval_set beforehand.')
        return self._evals_result

    @property
    def feature_importances_(self):
        """:obj:`array` of shape = [n_features]: The feature importances (the higher, the more important).

        .. note::

            ``importance_type`` attribute is passed to the function
            to configure the type of importance values to be extracted.
        """
        if not self.__sklearn_is_fitted__():
            raise LGBMNotFittedError('No feature_importances found. Need to call fit beforehand.')
        return self._Booster.feature_importance(importance_type=self.importance_type)

    @property
    def feature_name_(self):
        """:obj:`array` of shape = [n_features]: The names of features."""
        if not self.__sklearn_is_fitted__():
            raise LGBMNotFittedError('No feature_name found. Need to call fit beforehand.')
        return self._Booster.feature_name()


class LGBMRegressor(_LGBMRegressorBase, LGBMModel):
    """LightGBM regressor."""

    def fit(
        self,
        X,
        y,
        sample_weight=None,
        init_score=None,
        eval_set=None,
        eval_names=None,
        eval_sample_weight=None,
        eval_init_score=None,
        eval_metric=None,
        feature_name='auto',
        categorical_feature='auto',
        callbacks=None,
        init_model=None
    ):
        """Docstring is inherited from the LGBMModel."""
        super().fit(
            X,
            y,
            sample_weight=sample_weight,
            init_score=init_score,
            eval_set=eval_set,
            eval_names=eval_names,
            eval_sample_weight=eval_sample_weight,
            eval_init_score=eval_init_score,
            eval_metric=eval_metric,
            feature_name=feature_name,
            categorical_feature=categorical_feature,
            callbacks=callbacks,
            init_model=init_model
        )
        return self

    _base_doc = LGBMModel.fit.__doc__.replace("self : LGBMModel", "self : LGBMRegressor")  # type: ignore
    _base_doc = (_base_doc[:_base_doc.find('group :')]  # type: ignore
                 + _base_doc[_base_doc.find('eval_set :'):])  # type: ignore
    _base_doc = (_base_doc[:_base_doc.find('eval_class_weight :')]
                 + _base_doc[_base_doc.find('eval_init_score :'):])
    fit.__doc__ = (_base_doc[:_base_doc.find('eval_group :')]
                   + _base_doc[_base_doc.find('eval_metric :'):])


class LGBMClassifier(_LGBMClassifierBase, LGBMModel):
    """LightGBM classifier."""

    def fit(
        self,
        X,
        y,
        sample_weight=None,
        init_score=None,
        eval_set=None,
        eval_names=None,
        eval_sample_weight=None,
        eval_class_weight=None,
        eval_init_score=None,
        eval_metric=None,
        feature_name='auto',
        categorical_feature='auto',
        callbacks=None,
        init_model=None
    ):
        """Docstring is inherited from the LGBMModel."""
        _LGBMAssertAllFinite(y)
        _LGBMCheckClassificationTargets(y)
        self._le = _LGBMLabelEncoder().fit(y)
        _y = self._le.transform(y)
        self._class_map = dict(zip(self._le.classes_, self._le.transform(self._le.classes_)))
        if isinstance(self.class_weight, dict):
            self._class_weight = {self._class_map[k]: v for k, v in self.class_weight.items()}

        self._classes = self._le.classes_
        self._n_classes = len(self._classes)

        if not callable(eval_metric):
            if isinstance(eval_metric, (str, type(None))):
                eval_metric = [eval_metric]
            if self._n_classes > 2:
                for index, metric in enumerate(eval_metric):
                    if metric in {'logloss', 'binary_logloss'}:
                        eval_metric[index] = "multi_logloss"
                    elif metric in {'error', 'binary_error'}:
                        eval_metric[index] = "multi_error"
            else:
                for index, metric in enumerate(eval_metric):
                    if metric in {'logloss', 'multi_logloss'}:
                        eval_metric[index] = 'binary_logloss'
                    elif metric in {'error', 'multi_error'}:
                        eval_metric[index] = 'binary_error'

        # do not modify args, as it causes errors in model selection tools
        valid_sets = None
        if eval_set is not None:
            if isinstance(eval_set, tuple):
                eval_set = [eval_set]
            valid_sets = [None] * len(eval_set)
            for i, (valid_x, valid_y) in enumerate(eval_set):
                if valid_x is X and valid_y is y:
                    valid_sets[i] = (valid_x, _y)
                else:
                    valid_sets[i] = (valid_x, self._le.transform(valid_y))

        super().fit(
            X,
            _y,
            sample_weight=sample_weight,
            init_score=init_score,
            eval_set=valid_sets,
            eval_names=eval_names,
            eval_sample_weight=eval_sample_weight,
            eval_class_weight=eval_class_weight,
            eval_init_score=eval_init_score,
            eval_metric=eval_metric,
            feature_name=feature_name,
            categorical_feature=categorical_feature,
            callbacks=callbacks,
            init_model=init_model
        )
        return self

    _base_doc = LGBMModel.fit.__doc__.replace("self : LGBMModel", "self : LGBMClassifier")  # type: ignore
    _base_doc = (_base_doc[:_base_doc.find('group :')]  # type: ignore
                 + _base_doc[_base_doc.find('eval_set :'):])  # type: ignore
    fit.__doc__ = (_base_doc[:_base_doc.find('eval_group :')]
                   + _base_doc[_base_doc.find('eval_metric :'):])

    def predict(self, X, raw_score=False, start_iteration=0, num_iteration=None,
                pred_leaf=False, pred_contrib=False, **kwargs):
        """Docstring is inherited from the LGBMModel."""
        result = self.predict_proba(X, raw_score, start_iteration, num_iteration,
                                    pred_leaf, pred_contrib, **kwargs)
        if callable(self._objective) or raw_score or pred_leaf or pred_contrib:
            return result
        else:
            class_index = np.argmax(result, axis=1)
            return self._le.inverse_transform(class_index)

    predict.__doc__ = LGBMModel.predict.__doc__

    def predict_proba(self, X, raw_score=False, start_iteration=0, num_iteration=None,
                      pred_leaf=False, pred_contrib=False, **kwargs):
        """Docstring is set after definition, using a template."""
        result = super().predict(X, raw_score, start_iteration, num_iteration, pred_leaf, pred_contrib, **kwargs)
        if callable(self._objective) and not (raw_score or pred_leaf or pred_contrib):
            _log_warning("Cannot compute class probabilities or labels "
                         "due to the usage of customized objective function.\n"
                         "Returning raw scores instead.")
            return result
        elif self._n_classes > 2 or raw_score or pred_leaf or pred_contrib:
            return result
        else:
            return np.vstack((1. - result, result)).transpose()

    predict_proba.__doc__ = _lgbmmodel_doc_predict.format(
        description="Return the predicted probability for each class for each sample.",
        X_shape="array-like or sparse matrix of shape = [n_samples, n_features]",
        output_name="predicted_probability",
        predicted_result_shape="array-like of shape = [n_samples] or shape = [n_samples, n_classes]",
        X_leaves_shape="array-like of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]",
        X_SHAP_values_shape="array-like of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes] or list with n_classes length of such objects"
    )

    @property
    def classes_(self):
        """:obj:`array` of shape = [n_classes]: The class label array."""
        if not self.__sklearn_is_fitted__():
            raise LGBMNotFittedError('No classes found. Need to call fit beforehand.')
        return self._classes

    @property
    def n_classes_(self) -> int:
        """:obj:`int`: The number of classes."""
        if not self.__sklearn_is_fitted__():
            raise LGBMNotFittedError('No classes found. Need to call fit beforehand.')
        return self._n_classes


class LGBMRanker(LGBMModel):
    """LightGBM ranker.

    .. warning::

        scikit-learn doesn't support ranking applications yet,
        therefore this class is not really compatible with the sklearn ecosystem.
        Please use this class mainly for training and applying ranking models in common sklearnish way.
    """

    def fit(
        self,
        X,
        y,
        sample_weight=None,
        init_score=None,
        group=None,
        eval_set=None,
        eval_names=None,
        eval_sample_weight=None,
        eval_init_score=None,
        eval_group=None,
        eval_metric=None,
        eval_at=(1, 2, 3, 4, 5),
        feature_name='auto',
        categorical_feature='auto',
        callbacks=None,
        init_model=None
    ):
        """Docstring is inherited from the LGBMModel."""
        # check group data
        if group is None:
            raise ValueError("Should set group for ranking task")

        if eval_set is not None:
            if eval_group is None:
                raise ValueError("Eval_group cannot be None when eval_set is not None")
            elif len(eval_group) != len(eval_set):
                raise ValueError("Length of eval_group should be equal to eval_set")
            elif (isinstance(eval_group, dict)
                  and any(i not in eval_group or eval_group[i] is None for i in range(len(eval_group)))
                  or isinstance(eval_group, list)
                  and any(group is None for group in eval_group)):
                raise ValueError("Should set group for all eval datasets for ranking task; "
                                 "if you use dict, the index should start from 0")

        self._eval_at = eval_at
        super().fit(
            X,
            y,
            sample_weight=sample_weight,
            init_score=init_score,
            group=group,
            eval_set=eval_set,
            eval_names=eval_names,
            eval_sample_weight=eval_sample_weight,
            eval_init_score=eval_init_score,
            eval_group=eval_group,
            eval_metric=eval_metric,
            feature_name=feature_name,
            categorical_feature=categorical_feature,
            callbacks=callbacks,
            init_model=init_model
        )
        return self

    _base_doc = LGBMModel.fit.__doc__.replace("self : LGBMModel", "self : LGBMRanker")  # type: ignore
    fit.__doc__ = (_base_doc[:_base_doc.find('eval_class_weight :')]  # type: ignore
                   + _base_doc[_base_doc.find('eval_init_score :'):])  # type: ignore
    _base_doc = fit.__doc__
    _before_feature_name, _feature_name, _after_feature_name = _base_doc.partition('feature_name :')
    fit.__doc__ = f"""{_before_feature_name}eval_at : iterable of int, optional (default=(1, 2, 3, 4, 5))
        The evaluation positions of the specified metric.
    {_feature_name}{_after_feature_name}"""