import logging from typing import Any, Dict from lightgbm import LGBMClassifier from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from freqtrade.freqai.prediction_models.BaseClassifierModel import BaseClassifierModel logger = logging.getLogger(__name__) class LightGBMClassifier(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 :params: :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 test_weights = None else: eval_set = (data_dictionary["test_features"].to_numpy(), data_dictionary["test_labels"].to_numpy()[:, 0]) test_weights = data_dictionary["test_weights"] X = data_dictionary["train_features"].to_numpy() y = data_dictionary["train_labels"].to_numpy()[:, 0] train_weights = data_dictionary["train_weights"] init_model = self.get_init_model(dk.pair) model = LGBMClassifier(**self.model_training_parameters) model.fit(X=X, y=y, eval_set=eval_set, sample_weight=train_weights, eval_sample_weight=[test_weights], init_model=init_model) return model