From 41ab130807c1f91dc6818a63f75e52bf7dca4ba5 Mon Sep 17 00:00:00 2001 From: Kenneth Tang Date: Tue, 18 May 2021 00:01:45 +0800 Subject: [PATCH] Fix CI lint with black --- examples/hyperparameter/LightGBM/hyperparameter_158.py | 8 +++++--- examples/hyperparameter/LightGBM/hyperparameter_360.py | 8 +++++--- 2 files changed, 10 insertions(+), 6 deletions(-) diff --git a/examples/hyperparameter/LightGBM/hyperparameter_158.py b/examples/hyperparameter/LightGBM/hyperparameter_158.py index 93c70596c..5e4887a14 100644 --- a/examples/hyperparameter/LightGBM/hyperparameter_158.py +++ b/examples/hyperparameter/LightGBM/hyperparameter_158.py @@ -8,6 +8,7 @@ if not exists_qlib_data(provider_uri): print(f"Qlib data is not found in {provider_uri}") sys.path.append(str(scripts_dir)) from get_data import GetData + GetData().qlib_data(target_dir=provider_uri, region="cn") qlib.init(provider_uri=provider_uri, region="cn") @@ -19,7 +20,7 @@ data_handler_config = { "end_time": "2020-08-01", "fit_start_time": "2008-01-01", "fit_end_time": "2014-12-31", - "instruments": market + "instruments": market, } dataset_task = { "dataset": { @@ -52,8 +53,8 @@ def objective(trial): "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, 1e+4), - "lambda_l2": trial.suggest_loguniform("lambda_l2", 1e-8, 1e+4), + "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), @@ -70,5 +71,6 @@ def objective(trial): model.fit(dataset, evals_result=evals_result) return min(evals_result["valid"]) + study = optuna.Study(study_name="LGBM_158", storage="sqlite:///db.sqlite3") study.optimize(objective, n_jobs=6) diff --git a/examples/hyperparameter/LightGBM/hyperparameter_360.py b/examples/hyperparameter/LightGBM/hyperparameter_360.py index 3b72355a6..8b498e912 100644 --- a/examples/hyperparameter/LightGBM/hyperparameter_360.py +++ b/examples/hyperparameter/LightGBM/hyperparameter_360.py @@ -8,6 +8,7 @@ if not exists_qlib_data(provider_uri): print(f"Qlib data is not found in {provider_uri}") sys.path.append(str(scripts_dir)) from get_data import GetData + GetData().qlib_data(target_dir=provider_uri, region="cn") qlib.init(provider_uri=provider_uri, region="cn") @@ -19,7 +20,7 @@ data_handler_config = { "end_time": "2020-08-01", "fit_start_time": "2008-01-01", "fit_end_time": "2014-12-31", - "instruments": market + "instruments": market, } dataset_task = { "dataset": { @@ -52,8 +53,8 @@ def objective(trial): "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, 1e+4), - "lambda_l2": trial.suggest_loguniform("lambda_l2", 1e-8, 1e+4), + "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), @@ -70,5 +71,6 @@ def objective(trial): model.fit(dataset, evals_result=evals_result) return min(evals_result["valid"]) + study = optuna.Study(study_name="LGBM_360", storage="sqlite:///db.sqlite3") study.optimize(objective, n_jobs=6)