freqtrade_origin/freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py

79 lines
2.7 KiB
Python
Raw Normal View History

2022-07-02 16:09:38 +00:00
import logging
from pathlib import Path
from typing import Any, Dict
2022-07-02 16:09:38 +00:00
from catboost import CatBoostRegressor, Pool
2022-09-07 16:58:55 +00:00
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.base_models.FreqaiMultiOutputRegressor import FreqaiMultiOutputRegressor
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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 inherits IFreqaiModel, which
means it has full access to all Frequency AI functionality. Typically,
users would use this to override the common `fit()`, `train()`, or
`predict()` methods to add their custom data handling tools or change
various aspects of the training that cannot be configured via the
top level config.json file.
2022-07-02 16:09:38 +00:00
"""
2022-09-07 16:58:55 +00:00
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
2022-07-02 16:09:38 +00:00
"""
User sets up the training and test data to fit their desired model here
:param data_dictionary: the dictionary holding all data for train, test,
labels, weights
:param dk: The datakitchen object for the current coin/model
2022-07-02 16:09:38 +00:00
"""
cbr = CatBoostRegressor(
allow_writing_files=True,
train_dir=Path(dk.data_path),
2022-07-03 08:59:38 +00:00
**self.model_training_parameters,
2022-07-02 16:09:38 +00:00
)
X = data_dictionary["train_features"]
y = data_dictionary["train_labels"]
sample_weight = data_dictionary["train_weights"]
eval_sets = [None] * y.shape[1]
2024-05-12 15:12:20 +00:00
if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) != 0:
eval_sets = [None] * data_dictionary["test_labels"].shape[1]
2024-05-12 15:12:20 +00:00
for i in range(data_dictionary["test_labels"].shape[1]):
eval_sets[i] = Pool(
data=data_dictionary["test_features"],
label=data_dictionary["test_labels"].iloc[:, i],
weight=data_dictionary["test_weights"],
)
init_model = self.get_init_model(dk.pair)
if init_model:
init_models = init_model.estimators_
else:
init_models = [None] * y.shape[1]
fit_params = []
for i in range(len(eval_sets)):
2024-05-12 15:12:20 +00:00
fit_params.append(
{
"eval_set": eval_sets[i],
"init_model": init_models[i],
}
)
model = FreqaiMultiOutputRegressor(estimator=cbr)
2024-05-12 15:12:20 +00:00
thread_training = self.freqai_info.get("multitarget_parallel_training", False)
if thread_training:
model.n_jobs = y.shape[1]
model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
2022-07-02 16:09:38 +00:00
return model