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61 lines
2.0 KiB
Python
61 lines
2.0 KiB
Python
import logging
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from pathlib import Path
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from typing import Any
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from catboost import CatBoostRegressor, Pool
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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 CatboostRegressor(BaseRegressionModel):
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"""
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User created prediction model. The class inherits IFreqaiModel, which
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means it has full access to all Frequency AI functionality. Typically,
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users would use this to override the common `fit()`, `train()`, or
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`predict()` methods to add their custom data handling tools or change
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various aspects of the training that cannot be configured via the
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top level config.json file.
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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 holding all data for train, test,
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labels, weights
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:param dk: The datakitchen object for the current coin/model
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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=True,
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train_dir=Path(dk.data_path),
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**self.model_training_parameters,
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)
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model.fit(
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X=train_data,
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eval_set=test_data,
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init_model=init_model,
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)
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return model
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