import logging from typing import Any, Dict from lightgbm import LGBMRegressor from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel from freqtrade.freqai.data_kitchen import FreqaiDataKitchen logger = logging.getLogger(__name__) class LightGBMRegressor(BaseRegressionModel): """ User created prediction model. The class inherits IFreqaiModel, which means it has full access to all Frequency AI functionality. Typically, users would use this to override the common `fit()`, `train()`, or `predict()` methods to add their custom data handling tools or change various aspects of the training that cannot be configured via the top level config.json file. """ 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 holding all data for train, test, labels, weights :param dk: The datakitchen object for the current coin/model """ 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"] init_model = self.get_init_model(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