import logging from typing import Any, Dict from catboost import CatBoostRegressor # , Pool from sklearn.multioutput import MultiOutputRegressor from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel logger = logging.getLogger(__name__) class CatboostPredictionMultiModel(BaseRegressionModel): """ User created prediction model. The class needs to override three necessary functions, predict(), train(), fit(). The class inherits ModelHandler which has its own DataHandler where data is held, saved, loaded, and managed. """ def fit(self, data_dictionary: Dict) -> Any: """ User sets up the training and test data to fit their desired model here :param data_dictionary: the dictionary constructed by DataHandler to hold all the training and test data/labels. """ cbr = CatBoostRegressor( allow_writing_files=False, **self.model_training_parameters, ) X = data_dictionary["train_features"] y = data_dictionary["train_labels"] eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"]) sample_weight = data_dictionary["train_weights"] model = MultiOutputRegressor(estimator=cbr) model.fit(X=X, y=y, sample_weight=sample_weight) # , eval_set=eval_set) if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0: train_score = model.score(X, y) test_score = model.score(*eval_set) logger.info(f"Train score {train_score}, Test score {test_score}") return model