import logging from typing import Any, Dict from lightgbm import LGBMRegressor from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel from freqtrade.freqai.data_kitchen import FreqaiDataKitchen logger = logging.getLogger(__name__) class LightGBMRegressor(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) -> Any: """ Most regressors use the same function names and arguments e.g. user can drop in LGBMRegressor in place of CatBoostRegressor and all data management will be properly handled by Freqai. :param data_dictionary: the dictionary constructed by DataHandler to hold all the training and test data/labels. """ if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0: eval_set = None eval_weights = None else: eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"]) eval_weights = data_dictionary["test_weights"] X = data_dictionary["train_features"] y = data_dictionary["train_labels"] train_weights = data_dictionary["train_weights"] if dk.pair not in self.dd.model_dictionary or not self.continual_learning: init_model = None else: init_model = self.dd.model_dictionary[dk.pair] model = LGBMRegressor(**self.model_training_parameters) model.fit(X=X, y=y, eval_set=eval_set, sample_weight=train_weights, eval_sample_weight=[eval_weights], init_model=init_model) return model