47 lines
1.7 KiB
Python
47 lines
1.7 KiB
Python
import logging
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from typing import Any, Dict
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from lightgbm import LGBMRegressor
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from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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logger = logging.getLogger(__name__)
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class LightGBMRegressor(BaseRegressionModel):
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"""
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User created prediction model. The class needs to override three necessary
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functions, predict(), train(), fit(). The class inherits ModelHandler which
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has its own DataHandler where data is held, saved, loaded, and managed.
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"""
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def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
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"""
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Most regressors use the same function names and arguments e.g. user
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can drop in LGBMRegressor in place of CatBoostRegressor and all data
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management will be properly handled by Freqai.
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:param data_dictionary: the dictionary constructed by DataHandler to hold
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all the training and test data/labels.
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"""
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if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
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eval_set = None
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eval_weights = None
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else:
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eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
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eval_weights = data_dictionary["test_weights"]
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X = data_dictionary["train_features"]
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y = data_dictionary["train_labels"]
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train_weights = data_dictionary["train_weights"]
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init_model = self.get_init_model(dk.pair)
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model = LGBMRegressor(**self.model_training_parameters)
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model.fit(X=X, y=y, eval_set=eval_set, sample_weight=train_weights,
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eval_sample_weight=[eval_weights], init_model=init_model)
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return model
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