55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
import logging
|
|
import sys
|
|
from pathlib import Path
|
|
from typing import Any, Dict
|
|
|
|
from catboost import CatBoostRegressor, Pool
|
|
|
|
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
|
|
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class CatboostRegressor(BaseRegressionModel):
|
|
"""
|
|
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, dk: FreqaiDataKitchen, **kwargs) -> Any:
|
|
"""
|
|
User sets up the training and test data to fit their desired model here
|
|
:param data_dictionary: the dictionary constructed by DataHandler to hold
|
|
all the training and test data/labels.
|
|
"""
|
|
|
|
train_data = Pool(
|
|
data=data_dictionary["train_features"],
|
|
label=data_dictionary["train_labels"],
|
|
weight=data_dictionary["train_weights"],
|
|
)
|
|
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
|
|
test_data = None
|
|
else:
|
|
test_data = Pool(
|
|
data=data_dictionary["test_features"],
|
|
label=data_dictionary["test_labels"],
|
|
weight=data_dictionary["test_weights"],
|
|
)
|
|
|
|
init_model = self.get_init_model(dk.pair)
|
|
|
|
model = CatBoostRegressor(
|
|
allow_writing_files=True,
|
|
train_dir=Path(dk.data_path),
|
|
**self.model_training_parameters,
|
|
)
|
|
|
|
model.fit(X=train_data, eval_set=test_data, init_model=init_model,
|
|
log_cout=sys.stdout, log_cerr=sys.stderr)
|
|
|
|
return model
|