stable/freqtrade/freqai/prediction_models/LightGBMRegressor.py

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