stable/freqtrade/freqai/prediction_models/LightGBMClassifier.py

import logging
from typing import Any, Dict

from lightgbm import LGBMClassifier

from freqtrade.freqai.prediction_models.BaseClassifierModel import BaseClassifierModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen

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) -> 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"]

        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 = 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