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Fix CI lint with black

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This commit is contained in:
Kenneth Tang
2021-05-18 00:01:45 +08:00
parent 8f67010b58
commit 41ab130807
2 changed files with 10 additions and 6 deletions
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8
examples/hyperparameter/LightGBM/hyperparameter_158.py
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@@ -8,6 +8,7 @@ if not exists_qlib_data(provider_uri):
print(f"Qlib data is not found in {provider_uri}") print(f"Qlib data is not found in {provider_uri}")
sys.path.append(str(scripts_dir)) sys.path.append(str(scripts_dir))
from get_data import GetData from get_data import GetData
GetData().qlib_data(target_dir=provider_uri, region="cn") GetData().qlib_data(target_dir=provider_uri, region="cn")
qlib.init(provider_uri=provider_uri, region="cn") qlib.init(provider_uri=provider_uri, region="cn")
@@ -19,7 +20,7 @@ data_handler_config = {
"end_time": "2020-08-01", "end_time": "2020-08-01",
"fit_start_time": "2008-01-01", "fit_start_time": "2008-01-01",
"fit_end_time": "2014-12-31", "fit_end_time": "2014-12-31",
"instruments": market "instruments": market,
} }
dataset_task = { dataset_task = {
"dataset": { "dataset": {
@@ -52,8 +53,8 @@ def objective(trial):
"colsample_bytree": trial.suggest_uniform("colsample_bytree", 0.5, 1), "colsample_bytree": trial.suggest_uniform("colsample_bytree", 0.5, 1),
"learning_rate": trial.suggest_uniform("learning_rate", 0, 1), "learning_rate": trial.suggest_uniform("learning_rate", 0, 1),
"subsample": trial.suggest_uniform("subsample", 0, 1), "subsample": trial.suggest_uniform("subsample", 0, 1),
"lambda_l1": trial.suggest_loguniform("lambda_l1", 1e-8, 1e+4), "lambda_l1": trial.suggest_loguniform("lambda_l1", 1e-8, 1e4),
"lambda_l2": trial.suggest_loguniform("lambda_l2", 1e-8, 1e+4), "lambda_l2": trial.suggest_loguniform("lambda_l2", 1e-8, 1e4),
"max_depth": 10, "max_depth": 10,
"num_leaves": trial.suggest_int("num_leaves", 1, 1024), "num_leaves": trial.suggest_int("num_leaves", 1, 1024),
"feature_fraction": trial.suggest_uniform("feature_fraction", 0.4, 1.0), "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) model.fit(dataset, evals_result=evals_result)
return min(evals_result["valid"]) return min(evals_result["valid"])
study = optuna.Study(study_name="LGBM_158", storage="sqlite:///db.sqlite3") study = optuna.Study(study_name="LGBM_158", storage="sqlite:///db.sqlite3")
study.optimize(objective, n_jobs=6) study.optimize(objective, n_jobs=6)

8
examples/hyperparameter/LightGBM/hyperparameter_360.py
View File

@@ -8,6 +8,7 @@ if not exists_qlib_data(provider_uri):
print(f"Qlib data is not found in {provider_uri}") print(f"Qlib data is not found in {provider_uri}")
sys.path.append(str(scripts_dir)) sys.path.append(str(scripts_dir))
from get_data import GetData from get_data import GetData
GetData().qlib_data(target_dir=provider_uri, region="cn") GetData().qlib_data(target_dir=provider_uri, region="cn")
qlib.init(provider_uri=provider_uri, region="cn") qlib.init(provider_uri=provider_uri, region="cn")
@@ -19,7 +20,7 @@ data_handler_config = {
"end_time": "2020-08-01", "end_time": "2020-08-01",
"fit_start_time": "2008-01-01", "fit_start_time": "2008-01-01",
"fit_end_time": "2014-12-31", "fit_end_time": "2014-12-31",
"instruments": market "instruments": market,
} }
dataset_task = { dataset_task = {
"dataset": { "dataset": {
@@ -52,8 +53,8 @@ def objective(trial):
"colsample_bytree": trial.suggest_uniform("colsample_bytree", 0.5, 1), "colsample_bytree": trial.suggest_uniform("colsample_bytree", 0.5, 1),
"learning_rate": trial.suggest_uniform("learning_rate", 0, 1), "learning_rate": trial.suggest_uniform("learning_rate", 0, 1),
"subsample": trial.suggest_uniform("subsample", 0, 1), "subsample": trial.suggest_uniform("subsample", 0, 1),
"lambda_l1": trial.suggest_loguniform("lambda_l1", 1e-8, 1e+4), "lambda_l1": trial.suggest_loguniform("lambda_l1", 1e-8, 1e4),
"lambda_l2": trial.suggest_loguniform("lambda_l2", 1e-8, 1e+4), "lambda_l2": trial.suggest_loguniform("lambda_l2", 1e-8, 1e4),
"max_depth": 10, "max_depth": 10,
"num_leaves": trial.suggest_int("num_leaves", 1, 1024), "num_leaves": trial.suggest_int("num_leaves", 1, 1024),
"feature_fraction": trial.suggest_uniform("feature_fraction", 0.4, 1.0), "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) model.fit(dataset, evals_result=evals_result)
return min(evals_result["valid"]) return min(evals_result["valid"])
study = optuna.Study(study_name="LGBM_360", storage="sqlite:///db.sqlite3") study = optuna.Study(study_name="LGBM_360", storage="sqlite:///db.sqlite3")
study.optimize(objective, n_jobs=6) study.optimize(objective, n_jobs=6)