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Fix CI lint with black

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This commit is contained in:
Kenneth Tang
2021-05-17 23:09:42 +08:00
parent f51e04a1cc
commit 8f67010b58
2 changed files with 50 additions and 50 deletions
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48
examples/hyperparameter/LightGBM/hyperparameter_158.py
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@@ -8,18 +8,18 @@ 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")
market = "csi300" market = "csi300"
benchmark = "SH000300" benchmark = "SH000300"
data_handler_config = { data_handler_config = {
'start_time': '2008-01-01', "start_time": "2008-01-01",
'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": {
@@ -32,15 +32,16 @@ dataset_task = {
"kwargs": data_handler_config, "kwargs": data_handler_config,
}, },
"segments": { "segments": {
'train': ('2008-01-01', '2014-12-31'), "train": ("2008-01-01", "2014-12-31"),
'valid': ('2015-01-01', '2016-12-31'), "valid": ("2015-01-01", "2016-12-31"),
'test': ('2017-01-01', '2020-08-01'), "test": ("2017-01-01", "2020-08-01"),
}, },
}, },
}, },
} }
dataset = init_instance_by_config(dataset_task["dataset"]) dataset = init_instance_by_config(dataset_task["dataset"])
def objective(trial): def objective(trial):
task = { task = {
"model": { "model": {
@@ -48,27 +49,26 @@ def objective(trial):
"module_path": "qlib.contrib.model.gbdt", "module_path": "qlib.contrib.model.gbdt",
"kwargs": { "kwargs": {
"loss": "mse", "loss": "mse",
"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, 1e+4),
"lambda_l2": trial.suggest_loguniform('lambda_l2', 1e-8, 1e+4), "lambda_l2": trial.suggest_loguniform("lambda_l2", 1e-8, 1e+4),
"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),
'bagging_fraction': trial.suggest_uniform('bagging_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), "bagging_freq": trial.suggest_int("bagging_freq", 1, 7),
'min_data_in_leaf': trial.suggest_int('min_data_in_leaf', 1, 50), "min_data_in_leaf": trial.suggest_int("min_data_in_leaf", 1, 50),
'min_child_samples': trial.suggest_int('min_child_samples', 5, 100), "min_child_samples": trial.suggest_int("min_child_samples", 5, 100),
}, },
}, },
} }
evals_result = dict() evals_result = dict()
model = init_instance_by_config(task["model"]) model = init_instance_by_config(task["model"])
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)

48
examples/hyperparameter/LightGBM/hyperparameter_360.py
View File

@@ -8,18 +8,18 @@ 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")
market = "csi300" market = "csi300"
benchmark = "SH000300" benchmark = "SH000300"
data_handler_config = { data_handler_config = {
'start_time': '2008-01-01', "start_time": "2008-01-01",
'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": {
@@ -32,15 +32,16 @@ dataset_task = {
"kwargs": data_handler_config, "kwargs": data_handler_config,
}, },
"segments": { "segments": {
'train': ('2008-01-01', '2014-12-31'), "train": ("2008-01-01", "2014-12-31"),
'valid': ('2015-01-01', '2016-12-31'), "valid": ("2015-01-01", "2016-12-31"),
'test': ('2017-01-01', '2020-08-01'), "test": ("2017-01-01", "2020-08-01"),
}, },
}, },
}, },
} }
dataset = init_instance_by_config(dataset_task["dataset"]) dataset = init_instance_by_config(dataset_task["dataset"])
def objective(trial): def objective(trial):
task = { task = {
"model": { "model": {
@@ -48,27 +49,26 @@ def objective(trial):
"module_path": "qlib.contrib.model.gbdt", "module_path": "qlib.contrib.model.gbdt",
"kwargs": { "kwargs": {
"loss": "mse", "loss": "mse",
"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, 1e+4),
"lambda_l2": trial.suggest_loguniform('lambda_l2', 1e-8, 1e+4), "lambda_l2": trial.suggest_loguniform("lambda_l2", 1e-8, 1e+4),
"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),
'bagging_fraction': trial.suggest_uniform('bagging_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), "bagging_freq": trial.suggest_int("bagging_freq", 1, 7),
'min_data_in_leaf': trial.suggest_int('min_data_in_leaf', 1, 50), "min_data_in_leaf": trial.suggest_int("min_data_in_leaf", 1, 50),
'min_child_samples': trial.suggest_int('min_child_samples', 5, 100), "min_child_samples": trial.suggest_int("min_child_samples", 5, 100),
}, },
}, },
} }
evals_result = dict() evals_result = dict()
model = init_instance_by_config(task["model"]) model = init_instance_by_config(task["model"])
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)