import logging import sys from pathlib import Path from typing import Any, Dict from catboost import CatBoostClassifier, Pool from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel from freqtrade.freqai.base_models.FreqaiMultiOutputClassifier import FreqaiMultiOutputClassifier from freqtrade.freqai.data_kitchen import FreqaiDataKitchen logger = logging.getLogger(__name__) class CatboostClassifierMultiTarget(BaseClassifierModel): """ 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. """ cbc = CatBoostClassifier( allow_writing_files=True, loss_function='MultiClass', 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], 'log_cout': sys.stdout, 'log_cerr': sys.stderr, }) model = FreqaiMultiOutputClassifier(estimator=cbc) 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