freqtrade_origin/freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py

45 lines
1.6 KiB
Python
Raw Normal View History

2022-07-02 16:09:38 +00:00
import logging
from typing import Any, Dict
2022-07-02 16:09:38 +00:00
from catboost import CatBoostRegressor # , Pool
from sklearn.multioutput import MultiOutputRegressor
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
2022-07-02 16:09:38 +00:00
logger = logging.getLogger(__name__)
class CatboostRegressorMultiTarget(BaseRegressionModel):
2022-07-02 16:09:38 +00:00
"""
User created prediction model. The class needs to override three necessary
functions, predict(), train(), fit(). The class inherits ModelHandler which
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict) -> Any:
"""
User sets up the training and test data to fit their desired model here
2022-07-24 14:54:39 +00:00
:param data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
2022-07-02 16:09:38 +00:00
"""
cbr = CatBoostRegressor(
2022-07-03 08:59:38 +00:00
allow_writing_files=False,
**self.model_training_parameters,
2022-07-02 16:09:38 +00:00
)
X = data_dictionary["train_features"]
y = data_dictionary["train_labels"]
eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
2022-07-03 08:59:38 +00:00
sample_weight = data_dictionary["train_weights"]
2022-07-02 16:09:38 +00:00
model = MultiOutputRegressor(estimator=cbr)
model.fit(X=X, y=y, sample_weight=sample_weight) # , eval_set=eval_set)
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
train_score = model.score(X, y)
test_score = model.score(*eval_set)
logger.info(f"Train score {train_score}, Test score {test_score}")
2022-07-02 16:09:38 +00:00
return model