import logging
from typing import Tuple

from pandas import DataFrame

from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.freqai_interface import IFreqaiModel


logger = logging.getLogger(__name__)


class BaseRegressionModel(IFreqaiModel):
    """
    Base class for regression type models (e.g. Catboost, LightGBM, XGboost etc.).
    User *must* inherit from this class and set fit() and predict(). See example scripts
    such as prediction_models/CatboostPredictionModel.py for guidance.
    """

    def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
        """
        User uses this function to add any additional return values to the dataframe.
        e.g.
        dataframe['volatility'] = dk.volatility_values
        """

        return dataframe

    def train(
        self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
    ) -> Tuple[DataFrame, DataFrame]:
        """
        Filter the training data and train a model to it. Train makes heavy use of the datakitchen
        for storing, saving, loading, and analyzing the data.
        :params:
        :unfiltered_dataframe: Full dataframe for the current training period
        :metadata: pair metadata from strategy.
        :returns:
        :model: Trained model which can be used to inference (self.predict)
        """

        logger.info("-------------------- Starting training " f"{pair} --------------------")

        # filter the features requested by user in the configuration file and elegantly handle NaNs
        features_filtered, labels_filtered = dk.filter_features(
            unfiltered_dataframe,
            dk.training_features_list,
            dk.label_list,
            training_filter=True,
        )

        start_date = unfiltered_dataframe["date"].iloc[0].strftime("%Y-%m-%d")
        end_date = unfiltered_dataframe["date"].iloc[-1].strftime("%Y-%m-%d")
        logger.info(f"-------------------- Training on data from {start_date} to "
                    f"{end_date}--------------------")
        # split data into train/test data.
        data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
        if not self.freqai_info.get('fit_live_predictions', 0):
            dk.fit_labels()
        # normalize all data based on train_dataset only
        data_dictionary = dk.normalize_data(data_dictionary)

        # optional additional data cleaning/analysis
        self.data_cleaning_train(dk)

        logger.info(
            f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
        )
        logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')

        model = self.fit(data_dictionary)

        if pair not in self.dd.historic_predictions:
            self.set_initial_historic_predictions(
                data_dictionary['train_features'], model, dk, pair)
        elif self.freqai_info.get('fit_live_predictions_candles', 0):
            dk.fit_live_predictions()

        self.dd.save_historic_predictions_to_disk()

        logger.info(f"--------------------done training {pair}--------------------")

        return model

    def predict(
        self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
    ) -> Tuple[DataFrame, DataFrame]:
        """
        Filter the prediction features data and predict with it.
        :param: unfiltered_dataframe: Full dataframe for the current backtest period.
        :return:
        :pred_df: dataframe containing the predictions
        :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
        data (NaNs) or felt uncertain about data (PCA and DI index)
        """

        dk.find_features(unfiltered_dataframe)
        filtered_dataframe, _ = dk.filter_features(
            unfiltered_dataframe, dk.training_features_list, training_filter=False
        )
        filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
        dk.data_dictionary["prediction_features"] = filtered_dataframe

        # optional additional data cleaning/analysis
        self.data_cleaning_predict(dk, filtered_dataframe)

        predictions = self.model.predict(dk.data_dictionary["prediction_features"])
        pred_df = DataFrame(predictions, columns=dk.label_list)

        pred_df = dk.denormalize_labels_from_metadata(pred_df)

        return (pred_df, dk.do_predict)