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more clearly structure

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lzh222333
2021-03-16 02:23:28 +00:00
parent 0bc49dab60
commit e3730b32d7
5 changed files with 199 additions and 115 deletions
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27
examples/taskmanager/update_online_pred.py
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@@ -1,8 +1,9 @@
import qlib
from qlib.model.trainer import task_train
from qlib.workflow.task.update import ModelUpdater
from qlib.workflow.task.online import RollingOnlineManager
from qlib.config import REG_CN
import fire
from qlib.workflow import R
data_handler_config = {
"start_time": "2008-01-01",
@@ -50,33 +51,33 @@ task = {
},
}
provider_uri = "~/.qlib/qlib_data/cn_data" # target_dir
def first_train(experiment_name="online_svr"):
qlib.init(provider_uri=provider_uri, region=REG_CN)
model_updater = ModelUpdater(experiment_name)
rom = RollingOnlineManager(experiment_name)
rid = task_train(task_config=task, experiment_name=experiment_name)
model_updater.reset_online_model(rid)
rom.reset_online_model(rid)
def update_online_pred(experiment_name="online_svr"):
qlib.init(provider_uri=provider_uri, region=REG_CN)
model_updater = ModelUpdater(experiment_name)
rom = RollingOnlineManager(experiment_name)
print("Here are the online models waiting for update:")
for rid, rec in model_updater.list_online_model().items():
for rid, rec in rom.list_online_model().items():
print(rid)
model_updater.update_online_pred()
rom.update_online_pred()
if __name__ == "__main__":
fire.Fire()
# to train a model and set it to online model, use the command below
## to train a model and set it to online model, use the command below
# python update_online_pred.py first_train
# to update online predictions once a day, use the command below
## to update online predictions once a day, use the command below
# python update_online_pred.py update_online_pred
provider_uri = "~/.qlib/qlib_data/cn_data" # target_dir
qlib.init(provider_uri=provider_uri, region=REG_CN)
fire.Fire()