stable/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py

import logging
from typing import Any

from pandas import DataFrame

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


logger = logging.getLogger(__name__)


class BaseTensorFlowModel(IFreqaiModel):
    """
    Base class for TensorFlow type models.
    User *must* inherit from this class and set fit() and predict().
    """

    def return_values(self, dataframe: DataFrame) -> 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
    ) -> Any:
        """
        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.
        :param unfiltered_dataframe: Full dataframe for the current training period
        :param 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,
        )

        # split data into train/test data.
        data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)

        # 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)

        if self.freqai_info.get('fit_live_predictions_candles', 0) and self.live:
            self.fit_live_predictions(dk)
        else:
            dk.fit_labels()

        self.dd.save_historic_predictions_to_disk()

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

        return model