# Copyright 1999-2025 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import Union

from ..utils import make_import_error_func
from .core import XGBScikitLearnBase, xgboost

if not xgboost:
    XGBRegressor = make_import_error_func("xgboost")
else:
    from xgboost.sklearn import XGBRegressorBase

    from .core import wrap_evaluation_matrices
    from .predict import predict
    from .train import train

    class XGBRegressor(XGBScikitLearnBase, XGBRegressorBase):
        """
        Implementation of the scikit-learn API for XGBoost regressor.
        """

        def __init__(
            self,
            xgb_model: Union[xgboost.XGBRegressor, xgboost.Booster] = None,
            **kwargs,
        ):
            super().__init__(**kwargs)
            self._set_model(xgb_model)

        def fit(
            self,
            X,
            y,
            sample_weight=None,
            base_margin=None,
            eval_set=None,
            sample_weight_eval_set=None,
            base_margin_eval_set=None,
            **kw,
        ):
            session = kw.pop("session", None)
            run_kwargs = kw.pop("run_kwargs", dict())

            self._n_features_in = X.shape[1]

            dtrain, evals = wrap_evaluation_matrices(
                None,
                X,
                y,
                sample_weight,
                base_margin,
                eval_set,
                sample_weight_eval_set,
                base_margin_eval_set,
            )
            params = self.get_xgb_params()
            if not params.get("objective"):
                params["objective"] = "reg:squarederror"
            self.evals_result_ = dict()
            result = train(
                params,
                dtrain,
                num_boost_round=self.get_num_boosting_rounds(),
                evals=evals,
                evals_result=self.evals_result_,
                session=session,
                run_kwargs=run_kwargs,
            )
            self._Booster = result
            return self

        def predict(self, data, **kw):
            return predict(self.get_booster(), data, **kw)