# Copyright 1999-2021 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 ..utils import make_import_error_func
from .core import xgboost, XGBScikitLearnBase


if not xgboost:
    XGBClassifier = make_import_error_func("xgboost")
else:
    from xgboost.sklearn import XGBClassifierBase

    from .... import tensor as mt
    from .core import wrap_evaluation_matrices
    from .train import train
    from .predict import predict

    class XGBClassifier(XGBScikitLearnBase, XGBClassifierBase):
        """
        Implementation of the scikit-learn API for XGBoost classification.
        """

        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())
            if kw:
                raise TypeError(
                    f"fit got an unexpected keyword argument '{next(iter(kw))}'"
                )

            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()

            self.classes_ = mt.unique(y, aggregate_size=1).to_numpy(
                session=session, **run_kwargs
            )
            self.n_classes_ = len(self.classes_)

            if self.n_classes_ > 2:
                params["objective"] = "multi:softprob"
                params["num_class"] = self.n_classes_
            else:
                params["objective"] = "binary:logistic"

            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):
            session = kw.pop("session", None)
            run_kwargs = kw.pop("run_kwargs", dict())
            run = kw.pop("run", True)
            prob = predict(self.get_booster(), data, run=False, **kw)
            if prob.ndim > 1:
                prediction = mt.argmax(prob, axis=1)
            else:
                prediction = (prob > 0.5).astype(mt.int64)
            if run:
                prediction.execute(session=session, **run_kwargs)
            return prediction

        def predict_proba(self, data, ntree_limit=None, **kw):
            if ntree_limit is not None:
                raise NotImplementedError("ntree_limit is not currently supported")
            return predict(self.get_booster(), data, **kw)
