import logging from time import time from typing import Any, Tuple import numpy as np import numpy.typing as npt import pandas as pd from pandas import DataFrame from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from freqtrade.freqai.freqai_interface import IFreqaiModel logger = logging.getLogger(__name__) class BaseClassifierModel(IFreqaiModel): """ Base class for regression type models (e.g. Catboost, LightGBM, XGboost etc.). User *must* inherit from this class and set fit(). See example scripts such as prediction_models/CatboostClassifier.py for guidance. """ 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(f"-------------------- Starting training {pair} --------------------") start_time = time() # 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. dd = dk.make_train_test_datasets(features_filtered, labels_filtered) if not self.freqai_info.get("fit_live_predictions_candles", 0) or not self.live: dk.fit_labels() dk.feature_pipeline = self.define_data_pipeline(threads=dk.thread_count) (dd["train_features"], dd["train_labels"], dd["train_weights"]) = dk.feature_pipeline.fit_transform(dd["train_features"], dd["train_labels"], dd["train_weights"]) if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0: (dd["test_features"], dd["test_labels"], dd["test_weights"]) = dk.feature_pipeline.transform(dd["test_features"], dd["test_labels"], dd["test_weights"]) logger.info( f"Training model on {len(dk.data_dictionary['train_features'].columns)} features" ) logger.info(f"Training model on {len(dd['train_features'])} data points") model = self.fit(dd, dk) end_time = time() logger.info(f"-------------------- Done training {pair} " f"({end_time - start_time:.2f} secs) --------------------") return model def predict( self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs ) -> Tuple[DataFrame, npt.NDArray[np.int_]]: """ Filter the prediction features data and predict with it. :param unfiltered_df: 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_df) filtered_df, _ = dk.filter_features( unfiltered_df, dk.training_features_list, training_filter=False ) dk.data_dictionary["prediction_features"] = filtered_df dk.data_dictionary["prediction_features"], outliers, _ = dk.feature_pipeline.transform( dk.data_dictionary["prediction_features"], outlier_check=True) predictions = self.model.predict(dk.data_dictionary["prediction_features"]) if self.CONV_WIDTH == 1: predictions = np.reshape(predictions, (-1, len(dk.label_list))) pred_df = DataFrame(predictions, columns=dk.label_list) predictions_prob = self.model.predict_proba(dk.data_dictionary["prediction_features"]) if self.CONV_WIDTH == 1: predictions_prob = np.reshape(predictions_prob, (-1, len(self.model.classes_))) pred_df_prob = DataFrame(predictions_prob, columns=self.model.classes_) pred_df = pd.concat([pred_df, pred_df_prob], axis=1) if dk.feature_pipeline["di"]: dk.DI_values = dk.feature_pipeline["di"].di_values else: dk.DI_values = np.zeros(outliers.shape[0]) dk.do_predict = outliers return (pred_df, dk.do_predict)