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 train( self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs ) -> 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_df: Full dataframe for the current training period :param metadata: pair metadata from strategy. :return: :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_df, dk.training_features_list, dk.label_list, training_filter=True, ) start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d") end_date = unfiltered_df["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) or not self.live: 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)}' f' features and {len(data_dictionary["train_features"])} data points' ) model = self.fit(data_dictionary, dk) logger.info(f"--------------------done training {pair}--------------------") return model