From f51e04a1cc476bc51bbd6396a3df25951a918893 Mon Sep 17 00:00:00 2001 From: Kenneth Tang Date: Thu, 13 May 2021 23:12:29 +0800 Subject: [PATCH] LightGBM hyperparameter --- examples/hyperparameter/LightGBM/Readme.md | 23 ++++++ .../LightGBM/hyperparameter_158.py | 74 +++++++++++++++++++ .../LightGBM/hyperparameter_360.py | 74 +++++++++++++++++++ .../hyperparameter/LightGBM/requirements.txt | 5 ++ 4 files changed, 176 insertions(+) create mode 100644 examples/hyperparameter/LightGBM/Readme.md create mode 100644 examples/hyperparameter/LightGBM/hyperparameter_158.py create mode 100644 examples/hyperparameter/LightGBM/hyperparameter_360.py create mode 100644 examples/hyperparameter/LightGBM/requirements.txt diff --git a/examples/hyperparameter/LightGBM/Readme.md b/examples/hyperparameter/LightGBM/Readme.md new file mode 100644 index 000000000..320e13828 --- /dev/null +++ b/examples/hyperparameter/LightGBM/Readme.md @@ -0,0 +1,23 @@ +# LightGBM hyperparameter + +## Alpha158 +First terminal +``` +optuna create-study --study LGBM_158 --storage sqlite:///db.sqlite3 +optuna-dashboard --port 5000 --host 0.0.0.0 sqlite:///db.sqlite3 +``` +Second terminal +``` +python hyperparameter_158.py +``` + +## Alpha360 +First terminal +``` +optuna create-study --study LGBM_360 --storage sqlite:///db.sqlite3 +optuna-dashboard --port 5000 --host 0.0.0.0 sqlite:///db.sqlite3 +``` +Second terminal +``` +python hyperparameter_360.py +``` diff --git a/examples/hyperparameter/LightGBM/hyperparameter_158.py b/examples/hyperparameter/LightGBM/hyperparameter_158.py new file mode 100644 index 000000000..dea00d383 --- /dev/null +++ b/examples/hyperparameter/LightGBM/hyperparameter_158.py @@ -0,0 +1,74 @@ +import qlib +from qlib.config import REG_CN +from qlib.utils import exists_qlib_data, init_instance_by_config +import optuna + +provider_uri = "~/.qlib/qlib_data/cn_data" +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') + +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 +} +dataset_task = { + "dataset": { + "class": "DatasetH", + "module_path": "qlib.data.dataset", + "kwargs": { + "handler": { + "class": "Alpha158", + "module_path": "qlib.contrib.data.handler", + "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'), + }, + }, + }, +} +dataset = init_instance_by_config(dataset_task["dataset"]) + +def objective(trial): + task = { + "model": { + "class": "LGBModel", + "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), + "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), + }, + }, + } + + evals_result = dict() + model = init_instance_by_config(task["model"]) + + 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 new file mode 100644 index 000000000..eef2966c2 --- /dev/null +++ b/examples/hyperparameter/LightGBM/hyperparameter_360.py @@ -0,0 +1,74 @@ +import qlib +from qlib.config import REG_CN +from qlib.utils import exists_qlib_data, init_instance_by_config +import optuna + +provider_uri = "~/.qlib/qlib_data/cn_data" +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') + +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 +} +dataset_task = { + "dataset": { + "class": "DatasetH", + "module_path": "qlib.data.dataset", + "kwargs": { + "handler": { + "class": "Alpha360", + "module_path": "qlib.contrib.data.handler", + "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'), + }, + }, + }, +} +dataset = init_instance_by_config(dataset_task["dataset"]) + +def objective(trial): + task = { + "model": { + "class": "LGBModel", + "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), + "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), + }, + }, + } + + evals_result = dict() + model = init_instance_by_config(task["model"]) + + 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) diff --git a/examples/hyperparameter/LightGBM/requirements.txt b/examples/hyperparameter/LightGBM/requirements.txt new file mode 100644 index 000000000..c8b16cefe --- /dev/null +++ b/examples/hyperparameter/LightGBM/requirements.txt @@ -0,0 +1,5 @@ +pandas==1.1.2 +numpy==1.17.4 +lightgbm==3.1.0 +optuna==2.7.0 +optuna-dashboard==0.4.1