| 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) |
|
|
