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add test/config.py

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This commit is contained in:
zhupr
2021-05-28 13:24:47 +08:00
parent 0a4e241608
commit 98eacf8f88
21 changed files with 246 additions and 637 deletions
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58
examples/hyperparameter/LightGBM/hyperparameter_158.py
View File

@@ -1,46 +1,9 @@
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"])
from qlib.config import REG_CN
from qlib.utils import init_instance_by_config
from qlib.tests.config import CSI300_DATASET_CONFIG
from qlib.tests.data import GetData
def objective(trial):
@@ -65,12 +28,19 @@ def objective(trial):
},
},
}
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)
if __name__ == "__main__":
provider_uri = "~/.qlib/qlib_data/cn_data"
GetData().qlib_data(target_dir=provider_uri, region=REG_CN)
qlib.init(provider_uri=provider_uri, region="cn")
dataset = init_instance_by_config(CSI300_DATASET_CONFIG)
study = optuna.Study(study_name="LGBM_158", storage="sqlite:///db.sqlite3")
study.optimize(objective, n_jobs=6)

57
examples/hyperparameter/LightGBM/hyperparameter_360.py
View File

@@ -1,46 +1,11 @@
import qlib
from qlib.config import REG_CN
from qlib.utils import exists_qlib_data, init_instance_by_config
import optuna
from qlib.config import REG_CN
from qlib.utils import init_instance_by_config
from qlib.tests.data import GetData
from qlib.tests.config import get_dataset_config, CSI300_MARKET, DATASET_ALPHA360_CLASS
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"])
DATASET_CONFIG = get_dataset_config(market=CSI300_MARKET, dataset_class=DATASET_ALPHA360_CLASS)
def objective(trial):
@@ -72,5 +37,13 @@ def objective(trial):
return min(evals_result["valid"])
study = optuna.Study(study_name="LGBM_360", storage="sqlite:///db.sqlite3")
study.optimize(objective, n_jobs=6)
if __name__ == "__main__":
provider_uri = "~/.qlib/qlib_data/cn_data"
GetData().qlib_data(target_dir=provider_uri, region=REG_CN)
qlib.init(provider_uri=provider_uri, region=REG_CN)
dataset = init_instance_by_config(DATASET_CONFIG)
study = optuna.Study(study_name="LGBM_360", storage="sqlite:///db.sqlite3")
study.optimize(objective, n_jobs=6)