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qlib/examples/taskmanager/task_manager_rolling.py
2021-03-18 09:30:01 +00:00

143 lines
4.0 KiB
Python

import qlib
from qlib.config import REG_CN
from qlib.workflow.task.gen import RollingGen, task_generator
from qlib.workflow.task.manage import TaskManager
from qlib.config import C
from qlib.workflow.task.manage import run_task
from qlib.workflow.task.collect import RollingCollector
from qlib.model.trainer import task_train
from qlib.workflow import R
from pprint import pprint
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": "csi100",
}
dataset_config = {
"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"),
},
},
}
record_config = [
{
"class": "SignalRecord",
"module_path": "qlib.workflow.record_temp",
},
{
"class": "SigAnaRecord",
"module_path": "qlib.workflow.record_temp",
},
]
# use lgb
task_lgb_config = {
"model": {
"class": "LGBModel",
"module_path": "qlib.contrib.model.gbdt",
},
"dataset": dataset_config,
"record": record_config,
}
# use xgboost
task_xgboost_config = {
"model": {
"class": "XGBModel",
"module_path": "qlib.contrib.model.xgboost",
},
"dataset": dataset_config,
"record": record_config,
}
# Reset all things to the first status, be careful to save important data
def reset():
print("========== reset ==========")
TaskManager(task_pool=task_pool).remove()
# exp = R.get_exp(experiment_name=exp_name)
# for rid in R.list_recorders():
# exp.delete_recorder(rid)
# This part corresponds to "Task Generating" in the document
def task_generating():
print("========== task_generating ==========")
tasks = task_generator(
tasks=[task_xgboost_config, task_lgb_config],
generators=RollingGen(step=550, rtype=RollingGen.ROLL_SD), # generate different date segment
)
pprint(tasks)
return tasks
# This part corresponds to "Task Storing" in the document
def task_storing(tasks):
print("========== task_storing ==========")
tm = TaskManager(task_pool=task_pool)
tm.create_task(tasks) # all tasks will be saved to MongoDB
# This part corresponds to "Task Running" in the document
def task_running():
print("========== task_running ==========")
run_task(task_train, task_pool, experiment_name=exp_name) # all tasks will be trained using "task_train" method
# This part corresponds to "Task Collecting" in the document
def task_collecting():
print("========== task_collecting ==========")
def get_task_key(task_config):
return task_config["model"]["class"]
def my_filter(recorder):
# only choose the results of "LGBModel"
task_key = get_task_key(rolling_collector.get_task(recorder))
if task_key == "LGBModel":
return True
return False
rolling_collector = RollingCollector(exp_name)
# group tasks by "get_task_key" and filter tasks by "my_filter"
pred_rolling = rolling_collector.collect_rolling_predictions(get_task_key, my_filter)
print(pred_rolling)
if __name__ == "__main__":
provider_uri = "~/.qlib/qlib_data/cn_data" # target_dir
mongo_conf = {
"task_url": "mongodb://10.0.0.4:27017/", # maybe you need to change it to your url
"task_db_name": "rolling_db",
}
exp_name = "rolling_exp" # experiment name, will be used as the experiment in MLflow
task_pool = "rolling_task" # task pool name, will be used as the document in MongoDB
qlib.init(provider_uri=provider_uri, region=REG_CN, mongo=mongo_conf)
reset()
tasks = task_generating()
task_storing(tasks)
task_running()
task_collecting()