import logging from typing import Any, Dict from xgboost import XGBRegressor from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel from freqtrade.freqai.data_kitchen import FreqaiDataKitchen logger = logging.getLogger(__name__) class XGBoostRegressor(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 """ X = data_dictionary["train_features"] y = data_dictionary["train_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']] sample_weight = data_dictionary["train_weights"] xgb_model = self.get_init_model(dk.pair) model = XGBRegressor(**self.model_training_parameters) model.fit(X=X, y=y, sample_weight=sample_weight, eval_set=eval_set, sample_weight_eval_set=eval_weights, xgb_model=xgb_model) return model