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68 lines
2.5 KiB
Python
68 lines
2.5 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.base_models.FreqaiMultiOutputRegressor import FreqaiMultiOutputRegressor
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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logger = logging.getLogger(__name__)
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class XGBoostRegressorMultiTarget(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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xgb = XGBRegressor(**self.model_training_parameters)
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X = data_dictionary["train_features"]
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y = data_dictionary["train_labels"]
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sample_weight = data_dictionary["train_weights"]
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eval_weights = None
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eval_sets = [None] * y.shape[1]
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if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
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eval_weights = [data_dictionary["test_weights"]]
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for i in range(data_dictionary['test_labels'].shape[1]):
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eval_sets[i] = [( # type: ignore
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data_dictionary["test_features"],
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data_dictionary["test_labels"].iloc[:, i]
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)]
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init_model = self.get_init_model(dk.pair)
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if init_model:
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init_models = init_model.estimators_
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else:
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init_models = [None] * y.shape[1]
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fit_params = []
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for i in range(len(eval_sets)):
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fit_params.append(
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{'eval_set': eval_sets[i], 'sample_weight_eval_set': eval_weights,
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'xgb_model': init_models[i]})
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model = FreqaiMultiOutputRegressor(estimator=xgb)
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thread_training = self.freqai_info.get('multitarget_parallel_training', False)
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if thread_training:
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model.n_jobs = y.shape[1]
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model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
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return model
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