import logging
from typing import Any, Dict

from xgboost import XGBRegressor

from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.tensorboard import TBCallback


# from datasieve.pipeline import Pipeline
# from freqtrade.freqai.transforms import FreqaiQuantileTransformer

logger = logging.getLogger(__name__)


class XGBoostRegressor(BaseRegressionModel):
    """
    User created prediction model. The class inherits IFreqaiModel, which
    means it has full access to all Frequency AI functionality. Typically,
    users would use this to override the common `fit()`, `train()`, or
    `predict()` methods to add their custom data handling tools or change
    various aspects of the training that cannot be configured via the
    top level config.json file.
    """

    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
        """
        User sets up the training and test data to fit their desired model here
        :param data_dictionary: the dictionary holding all data for train, test,
            labels, weights
        :param dk: The datakitchen object for the current coin/model
        """

        X = data_dictionary["train_features"]
        y = data_dictionary["train_labels"]

        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
            eval_set = None
            eval_weights = None
        else:
            eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"])]
            eval_weights = [data_dictionary['test_weights']]

        sample_weight = data_dictionary["train_weights"]

        xgb_model = self.get_init_model(dk.pair)

        model = XGBRegressor(**self.model_training_parameters)

        model.set_params(callbacks=[TBCallback(dk.data_path)], activate=self.activate_tensorboard)
        model.fit(X=X, y=y, sample_weight=sample_weight, eval_set=eval_set,
                  sample_weight_eval_set=eval_weights, xgb_model=xgb_model)
        # set the callbacks to empty so that we can serialize to disk later
        model.set_params(callbacks=[])

        return model

    # def define_data_pipeline(self, dk: FreqaiDataKitchen) -> None:
    #     """
    #     User defines their custom eature pipeline here (if they wish)
    #     """
    #     dk.feature_pipeline = Pipeline([
    #         ('qt', FreqaiQuantileTransformer(output_distribution='normal'))
    #     ])

    #     return

    # def define_label_pipeline(self, dk: FreqaiDataKitchen) -> None:
    #     """
    #     User defines their custom label pipeline here (if they wish)
    #     """
    #     dk.label_pipeline = Pipeline([
    #         ('qt', FreqaiQuantileTransformer(output_distribution='normal'))
    #     ])

    #     return