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OnlineServing V9

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lzh222333
2021-04-29 04:30:09 +00:00
parent 6f669348a8
commit 67c5740c83
19 changed files with 677 additions and 1010 deletions
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43
examples/online_srv/rolling_online_management.py
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@@ -1,22 +1,25 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
"""
This example show how RollingOnlineManager works with rolling tasks.
This example show how OnlineManager works with rolling tasks.
There are two parts including first train and routine.
Firstly, the RollingOnlineManager will finish the first training and set trained models to `online` models.
Next, the RollingOnlineManager will finish a routine process, including update online prediction -> prepare signals -> prepare tasks -> prepare new models -> reset online models
Firstly, the OnlineManager will finish the first training and set trained models to `online` models.
Next, the OnlineManager will finish a routine process, including update online prediction -> prepare signals -> prepare tasks -> prepare new models -> reset online models
"""
import os
from pathlib import Path
import pickle
import fire
import qlib
from qlib.workflow import R
from qlib.workflow.online.strategy import OnlineStrategy, RollingAverageStrategy
from qlib.workflow.online.strategy import RollingAverageStrategy
from qlib.workflow.task.gen import RollingGen
from qlib.workflow.task.manage import TaskManager
from qlib.workflow.online.manager import OnlineM
from qlib.workflow.online.manager import OnlineManager
from qlib.workflow.task.utils import list_recorders
from qlib.model.trainer import TrainerRM
from pprint import pprint
data_handler_config = {
"start_time": "2013-01-01",
@@ -94,7 +97,7 @@ class RollingOnlineExample:
self.rolling_step = rolling_step
strategy = []
for task in tasks:
name_id = task["model"]["class"] + "_" + str(self.rolling_step)
name_id = task["model"]["class"] # NOTE: Assumption: The model class can specify only one strategy
strategy.append(
RollingAverageStrategy(
name_id,
@@ -104,9 +107,12 @@ class RollingOnlineExample:
)
)
self.rolling_online_manager = OnlineM(strategy)
self.rolling_online_manager = OnlineManager(strategy)
self.collector = self.rolling_online_manager.get_collector()
_ROLLING_MANAGER_PATH = ".rolling_manager" # the RollingOnlineManager will dump to this file, for it will be loaded when calling routine.
_ROLLING_MANAGER_PATH = (
".RollingOnlineExample" # the OnlineManager will dump to this file, for it can be loaded when calling routine.
)
# Reset all things to the first status, be careful to save important data
def reset(self):
@@ -125,18 +131,23 @@ class RollingOnlineExample:
exp.delete_recorder(rid)
def first_run(self):
print("========== reset ==========")
self.rolling_online_manager.reset()
print("========== first_run ==========")
self.reset()
self.rolling_online_manager.first_train()
print("========== dump ==========")
self.rolling_online_manager.to_pickle(self._ROLLING_MANAGER_PATH)
print(self.rolling_online_manager.get_collector()())
print("========== collect results ==========")
print(self.collector())
def routine(self):
print("========== routine ==========")
print("========== load ==========")
with Path(self._ROLLING_MANAGER_PATH).open("rb") as f:
self.rolling_online_manager = pickle.load(f)
print("========== routine ==========")
self.rolling_online_manager.routine()
print(self.rolling_online_manager.get_collector()())
print("========== collect results ==========")
print(self.collector())
def main(self):
self.first_run()
@@ -145,11 +156,11 @@ class RollingOnlineExample:
if __name__ == "__main__":
####### to train the first version's models, use the command below
# python task_manager_rolling_with_updating.py first_run
# python rolling_online_management.py first_run
####### to update the models and predictions after the trading time, use the command below
# python task_manager_rolling_with_updating.py after_day
# python rolling_online_management.py after_day
####### to define your own parameters, use `--`
# python task_manager_rolling_with_updating.py first_run --exp_name='your_exp_name' --rolling_step=40
# python rolling_online_management.py first_run --exp_name='your_exp_name' --rolling_step=40
fire.Fire(RollingOnlineExample)