stable/freqtrade/freqai/prediction_models/CatboostRegressor.py

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