51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
import logging
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from typing import Any, Dict
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from catboost import CatBoostRegressor, Pool
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
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logger = logging.getLogger(__name__)
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class CatboostRegressor(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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User sets up the training and test data to fit their desired model here
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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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train_data = Pool(
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data=data_dictionary["train_features"],
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label=data_dictionary["train_labels"],
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weight=data_dictionary["train_weights"],
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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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test_data = None
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else:
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test_data = Pool(
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data=data_dictionary["test_features"],
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label=data_dictionary["test_labels"],
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weight=data_dictionary["test_weights"],
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)
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init_model = self.get_init_model(dk.pair)
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model = CatBoostRegressor(
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allow_writing_files=False,
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**self.model_training_parameters,
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)
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model.fit(X=train_data, eval_set=test_data, init_model=init_model)
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return model
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