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62 lines
2.2 KiB
Python
62 lines
2.2 KiB
Python
import logging
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from typing import Any, Dict
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from xgboost import XGBRegressor
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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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from freqtrade.freqai.tensorboard import TBCallback
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logger = logging.getLogger(__name__)
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class XGBoostRegressor(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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X = data_dictionary["train_features"]
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y = data_dictionary["train_labels"]
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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 = [
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(data_dictionary["test_features"],
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data_dictionary["test_labels"]),
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(X, y)
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]
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eval_weights = [
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data_dictionary['test_weights'],
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data_dictionary['train_weights']
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]
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sample_weight = data_dictionary["train_weights"]
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xgb_model = self.get_init_model(dk.pair)
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model = XGBRegressor(**self.model_training_parameters)
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model.set_params(callbacks=[TBCallback(dk.data_path)])
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model.fit(X=X, y=y, sample_weight=sample_weight, eval_set=eval_set,
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sample_weight_eval_set=eval_weights, xgb_model=xgb_model)
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# set the callbacks to empty so that we can serialize to disk later
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model.set_params(callbacks=[])
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return model
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