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f2361f1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | import qlib
import optuna
from qlib.constant import REG_CN
from qlib.utils import init_instance_by_config
from qlib.tests.config import CSI300_DATASET_CONFIG
from qlib.tests.data import GetData
def objective(trial):
task = {
"model": {
"class": "LGBModel",
"module_path": "qlib.contrib.model.gbdt",
"kwargs": {
"loss": "mse",
"colsample_bytree": trial.suggest_uniform("colsample_bytree", 0.5, 1),
"learning_rate": trial.suggest_uniform("learning_rate", 0, 1),
"subsample": trial.suggest_uniform("subsample", 0, 1),
"lambda_l1": trial.suggest_loguniform("lambda_l1", 1e-8, 1e4),
"lambda_l2": trial.suggest_loguniform("lambda_l2", 1e-8, 1e4),
"max_depth": 10,
"num_leaves": trial.suggest_int("num_leaves", 1, 1024),
"feature_fraction": trial.suggest_uniform("feature_fraction", 0.4, 1.0),
"bagging_fraction": trial.suggest_uniform("bagging_fraction", 0.4, 1.0),
"bagging_freq": trial.suggest_int("bagging_freq", 1, 7),
"min_data_in_leaf": trial.suggest_int("min_data_in_leaf", 1, 50),
"min_child_samples": trial.suggest_int("min_child_samples", 5, 100),
},
},
}
evals_result = dict()
model = init_instance_by_config(task["model"])
model.fit(dataset, evals_result=evals_result)
return min(evals_result["valid"])
if __name__ == "__main__":
provider_uri = "~/.qlib/qlib_data/cn_data"
GetData().qlib_data(target_dir=provider_uri, region=REG_CN, exists_skip=True)
qlib.init(provider_uri=provider_uri, region="cn")
dataset = init_instance_by_config(CSI300_DATASET_CONFIG)
study = optuna.Study(study_name="LGBM_158", storage="sqlite:///db.sqlite3")
study.optimize(objective, n_jobs=6)
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