import logging from pathlib import Path from typing import Any, Dict from catboost import CatBoostRegressor, Pool from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel from freqtrade.freqai.base_models.FreqaiMultiOutputRegressor import FreqaiMultiOutputRegressor from freqtrade.freqai.data_kitchen import FreqaiDataKitchen logger = logging.getLogger(__name__) class CatboostRegressorMultiTarget(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, dk: FreqaiDataKitchen, **kwargs) -> 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=True, train_dir=Path(dk.data_path), **self.model_training_parameters, ) X = data_dictionary["train_features"] y = data_dictionary["train_labels"] sample_weight = data_dictionary["train_weights"] eval_sets = [None] * y.shape[1] if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0: eval_sets = [None] * data_dictionary['test_labels'].shape[1] for i in range(data_dictionary['test_labels'].shape[1]): eval_sets[i] = Pool( data=data_dictionary["test_features"], label=data_dictionary["test_labels"].iloc[:, i], weight=data_dictionary["test_weights"], ) init_model = self.get_init_model(dk.pair) if init_model: init_models = init_model.estimators_ else: init_models = [None] * y.shape[1] fit_params = [] for i in range(len(eval_sets)): fit_params.append( {'eval_set': eval_sets[i], 'init_model': init_models[i]}) model = FreqaiMultiOutputRegressor(estimator=cbr) thread_training = self.freqai_info.get('multitarget_parallel_training', False) if thread_training: model.n_jobs = y.shape[1] model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params) return model