47 lines
1.6 KiB
Python
47 lines
1.6 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 sklearn.multioutput import MultiOutputRegressor
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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 CatboostPredictionMultiModel(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) -> 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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:params:
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: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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cbr = CatBoostRegressor(
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allow_writing_files=False,
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gpu_ram_part=0.5,
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verbose=100,
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early_stopping_rounds=400,
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**self.model_training_parameters,
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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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eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
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sample_weight = data_dictionary["train_weights"]
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model = MultiOutputRegressor(estimator=cbr)
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model.fit(X=X, y=y, sample_weight=sample_weight) # , eval_set=eval_set)
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train_score = model.score(X, y)
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test_score = model.score(*eval_set)
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logger.info(f"Train score {train_score}, Test score {test_score}")
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return model
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