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be4646b4b7
* Intermediate version * Fix yaml template & Successfully run rolling * Be compatible with benchmark * Get same results with previous linear model * Black formatting * Update black * Update the placeholder mechanism * Update CI * Update CI * Upgrade Black * Fix CI and simplify code * Fix CI * Move the data processing caching mechanism into utils. * Adjusting DDG-DA * Organize import
49 lines
2.0 KiB
Python
49 lines
2.0 KiB
Python
import qlib
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import optuna
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from qlib.constant import REG_CN
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from qlib.utils import init_instance_by_config
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from qlib.tests.data import GetData
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from qlib.tests.config import get_dataset_config, CSI300_MARKET, DATASET_ALPHA360_CLASS
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DATASET_CONFIG = get_dataset_config(market=CSI300_MARKET, dataset_class=DATASET_ALPHA360_CLASS)
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def objective(trial):
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task = {
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"model": {
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"class": "LGBModel",
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"module_path": "qlib.contrib.model.gbdt",
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"kwargs": {
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"loss": "mse",
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"colsample_bytree": trial.suggest_uniform("colsample_bytree", 0.5, 1),
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"learning_rate": trial.suggest_uniform("learning_rate", 0, 1),
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"subsample": trial.suggest_uniform("subsample", 0, 1),
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"lambda_l1": trial.suggest_loguniform("lambda_l1", 1e-8, 1e4),
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"lambda_l2": trial.suggest_loguniform("lambda_l2", 1e-8, 1e4),
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"max_depth": 10,
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"num_leaves": trial.suggest_int("num_leaves", 1, 1024),
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"feature_fraction": trial.suggest_uniform("feature_fraction", 0.4, 1.0),
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"bagging_fraction": trial.suggest_uniform("bagging_fraction", 0.4, 1.0),
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"bagging_freq": trial.suggest_int("bagging_freq", 1, 7),
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"min_data_in_leaf": trial.suggest_int("min_data_in_leaf", 1, 50),
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"min_child_samples": trial.suggest_int("min_child_samples", 5, 100),
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},
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},
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}
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evals_result = dict()
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model = init_instance_by_config(task["model"])
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model.fit(dataset, evals_result=evals_result)
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return min(evals_result["valid"])
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if __name__ == "__main__":
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provider_uri = "~/.qlib/qlib_data/cn_data"
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GetData().qlib_data(target_dir=provider_uri, region=REG_CN, exists_skip=True)
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qlib.init(provider_uri=provider_uri, region=REG_CN)
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dataset = init_instance_by_config(DATASET_CONFIG)
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study = optuna.Study(study_name="LGBM_360", storage="sqlite:///db.sqlite3")
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study.optimize(objective, n_jobs=6)
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