From 8f67010b5838e13bfc27c28b7e9566567d0f52ad Mon Sep 17 00:00:00 2001 From: Kenneth Tang Date: Mon, 17 May 2021 23:09:42 +0800 Subject: [PATCH] Fix CI lint with black --- .../LightGBM/hyperparameter_158.py | 50 +++++++++---------- .../LightGBM/hyperparameter_360.py | 50 +++++++++---------- 2 files changed, 50 insertions(+), 50 deletions(-) diff --git a/examples/hyperparameter/LightGBM/hyperparameter_158.py b/examples/hyperparameter/LightGBM/hyperparameter_158.py index dea00d383..93c70596c 100644 --- a/examples/hyperparameter/LightGBM/hyperparameter_158.py +++ b/examples/hyperparameter/LightGBM/hyperparameter_158.py @@ -8,18 +8,18 @@ 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') + GetData().qlib_data(target_dir=provider_uri, region="cn") +qlib.init(provider_uri=provider_uri, region="cn") market = "csi300" benchmark = "SH000300" data_handler_config = { - 'start_time': '2008-01-01', - 'end_time': '2020-08-01', - 'fit_start_time': '2008-01-01', - 'fit_end_time': '2014-12-31', - 'instruments': market + "start_time": "2008-01-01", + "end_time": "2020-08-01", + "fit_start_time": "2008-01-01", + "fit_end_time": "2014-12-31", + "instruments": market } dataset_task = { "dataset": { @@ -32,15 +32,16 @@ dataset_task = { "kwargs": data_handler_config, }, "segments": { - 'train': ('2008-01-01', '2014-12-31'), - 'valid': ('2015-01-01', '2016-12-31'), - 'test': ('2017-01-01', '2020-08-01'), + "train": ("2008-01-01", "2014-12-31"), + "valid": ("2015-01-01", "2016-12-31"), + "test": ("2017-01-01", "2020-08-01"), }, }, }, } dataset = init_instance_by_config(dataset_task["dataset"]) + def objective(trial): task = { "model": { @@ -48,27 +49,26 @@ def objective(trial): "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, 1e+4), - "lambda_l2": trial.suggest_loguniform('lambda_l2', 1e-8, 1e+4), + "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), "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), + "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']) + 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) diff --git a/examples/hyperparameter/LightGBM/hyperparameter_360.py b/examples/hyperparameter/LightGBM/hyperparameter_360.py index eef2966c2..3b72355a6 100644 --- a/examples/hyperparameter/LightGBM/hyperparameter_360.py +++ b/examples/hyperparameter/LightGBM/hyperparameter_360.py @@ -8,18 +8,18 @@ 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') + GetData().qlib_data(target_dir=provider_uri, region="cn") +qlib.init(provider_uri=provider_uri, region="cn") market = "csi300" benchmark = "SH000300" data_handler_config = { - 'start_time': '2008-01-01', - 'end_time': '2020-08-01', - 'fit_start_time': '2008-01-01', - 'fit_end_time': '2014-12-31', - 'instruments': market + "start_time": "2008-01-01", + "end_time": "2020-08-01", + "fit_start_time": "2008-01-01", + "fit_end_time": "2014-12-31", + "instruments": market } dataset_task = { "dataset": { @@ -32,15 +32,16 @@ dataset_task = { "kwargs": data_handler_config, }, "segments": { - 'train': ('2008-01-01', '2014-12-31'), - 'valid': ('2015-01-01', '2016-12-31'), - 'test': ('2017-01-01', '2020-08-01'), + "train": ("2008-01-01", "2014-12-31"), + "valid": ("2015-01-01", "2016-12-31"), + "test": ("2017-01-01", "2020-08-01"), }, }, }, } dataset = init_instance_by_config(dataset_task["dataset"]) + def objective(trial): task = { "model": { @@ -48,27 +49,26 @@ def objective(trial): "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, 1e+4), - "lambda_l2": trial.suggest_loguniform('lambda_l2', 1e-8, 1e+4), + "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), "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), + "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']) + 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)