mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-10 10:21:59 +00:00
39 lines
1.2 KiB
Python
39 lines
1.2 KiB
Python
|
import logging
|
||
|
|
from typing import Any, Dict
|
||
|
|
|
||
|
|
from lightgbm import LGBMClassifier
|
||
|
|
|
||
|
|
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
|
||
|
|
|
||
|
|
|
||
|
|
logger = logging.getLogger(__name__)
|
||
|
|
|
||
|
|
|
||
|
|
class LightGBMClassifier(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) -> Any:
|
||
|
|
"""
|
||
|
|
User sets up the training and test data to fit their desired model here
|
||
|
|
:params:
|
||
|
|
:data_dictionary: the dictionary constructed by DataHandler to hold
|
||
|
|
all the training and test data/labels.
|
||
|
|
"""
|
||
|
|
|
||
|
|
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
|
||
|
|
eval_set = None
|
||
|
|
else:
|
||
|
|
eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
|
||
|
|
X = data_dictionary["train_features"]
|
||
|
|
y = data_dictionary["train_labels"]
|
||
|
|
|
||
|
|
model = LGBMClassifier(**self.model_training_parameters)
|
||
|
|
|
||
|
|
model.fit(X=X, y=y, eval_set=eval_set)
|
||
|
|
|
||
|
|
return model
|