class FedotXGBoostImplementation(ModelImplementation):
    __operation_params = ['n_jobs', 'use_eval_set']

    def __init__(self, params: Optional[OperationParameters] = None):
        super().__init__(params)

        self.model_params = {k: v for k, v in self.params.to_dict().items() if k not in self.__operation_params}
        self.model = None

    def fit(self, input_data: InputData):
        input_data = input_data.get_not_encoded_data()

        if self.params.get('use_eval_set'):
            train_input, eval_input = train_test_data_setup(input_data)

            train_input = self.convert_to_dataframe(train_input)
            eval_input = self.convert_to_dataframe(eval_input)

            train_x, train_y = train_input.drop(columns=['target']), train_input['target']
            eval_x, eval_y = eval_input.drop(columns=['target']), eval_input['target']

            if self.classes_ is None:
                eval_metric = 'rmse'
            elif len(self.classes_) < 3:
                eval_metric = 'auc'
            else:
                eval_metric = 'mlogloss'

            self.model.fit(X=train_x, y=train_y,
                           eval_set=[(eval_x, eval_y)], eval_metric=eval_metric)

        else:

            train_data = self.convert_to_dataframe(input_data)
            train_x, train_y = train_data.drop(columns=['target']), train_data['target']
            self.model.fit(X=train_x, y=train_y)

        return self.model

    def predict(self, input_data: InputData):
        input_data = self.convert_to_dataframe(input_data.get_not_encoded_data())
        train_x, _ = input_data.drop(columns=['target']), input_data['target']
        prediction = self.model.predict(train_x)

        return prediction

@staticmethod
def convert_to_dataframe(data: Optional[InputData]):
    dataframe = pd.DataFrame(data=data.features, columns=data.features_names)
    dataframe['target'] = data.target

    if data.categorical_idx is not None:
        for col in dataframe.columns[data.categorical_idx]:
            dataframe[col] = dataframe[col].astype('category')

    if data.numerical_idx is not None:
        for col in dataframe.columns[data.numerical_idx]:
            dataframe[col] = dataframe[col].astype('float')

    return dataframe