online serving V9 middle status

examples/online_srv/online_management_simulate.py

@@ -1,20 +1,23 @@
-import fire
-import qlib
-from qlib.model.ens.ensemble import ens_workflow
-from qlib.model.trainer import DelayTrainerR, DelayTrainerRM, TrainerRM
-from qlib.workflow import R
-from qlib.workflow.online.manager import RollingOnlineManager
-from qlib.workflow.online.simulator import OnlineSimulator
-from qlib.workflow.task.collect import RecorderCollector
-from qlib.workflow.task.gen import RollingGen, task_generator
-from qlib.workflow.task.manage import TaskManager
-from qlib.workflow.task.utils import list_recorders
-
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
 """
 This examples is about the OnlineManager and OnlineSimulator based on rolling tasks. 
 The OnlineManager will focus on the updating of your online models.
 The OnlineSimulator will focus on the simulating real updating routine of your online models.
 """
+import fire
+import qlib
+from qlib.model.ens.ensemble import ens_workflow
+from qlib.model.trainer import DelayTrainerR, DelayTrainerRM, TrainerRM
+from qlib.workflow import R
+from qlib.workflow.online.manager import OnlineM  # RollingOnlineManager
+from qlib.workflow.online.strategy import OnlineStrategy, RollingAverageStrategy
+from qlib.workflow.task.collect import RecorderCollector
+from qlib.workflow.task.gen import RollingGen, task_generator
+from qlib.workflow.task.manage import TaskManager
+from qlib.workflow.task.utils import list_recorders
+
+
 
 
 data_handler_config = {
@@ -105,6 +108,8 @@ class OnlineSimulationExample:
         """
         self.exp_name = exp_name
         self.task_pool = task_pool
+        self.start_time = start_time
+        self.end_time = end_time
         mongo_conf = {
             "task_url": task_url,
             "task_db_name": task_db_name,
@@ -115,17 +120,18 @@ class OnlineSimulationExample:
         )  # The rolling tasks generator, modify_end_time is false because we just need simulate to 2018-10-31.
         self.trainer = DelayTrainerRM(self.exp_name, self.task_pool)
         self.task_manager = TaskManager(self.task_pool)  # A good way to manage all your tasks
-        self.rolling_online_manager = RollingOnlineManager(
-            experiment_name=exp_name,
-            rolling_gen=self.rolling_gen,
-            trainer=self.trainer,
+        self.rolling_online_manager = OnlineM(
+            RollingAverageStrategy(
+                exp_name, task_template=tasks, rolling_gen=self.rolling_gen, trainer=self.trainer, need_log=False
+            ),
+            begin_time=self.start_time,
             need_log=False,
         )  # The OnlineManager based on Rolling
-        self.onlinesimulator = OnlineSimulator(
-            start_time=start_time,
-            end_time=end_time,
-            online_manager=self.rolling_online_manager,
-        )
+        # self.onlinesimulator = OnlineSimulator(
+        #     start_time=start_time,
+        #     end_time=end_time,
+        #     online_manager=self.rolling_online_manager,
+        # )
         self.tasks = tasks
 
     # Reset all things to the first status, be careful to save important data
@@ -137,37 +143,16 @@ class OnlineSimulationExample:
         for rid in exp.list_recorders():
             exp.delete_recorder(rid)
 
-        for rid in list_recorders(
-            RollingOnlineManager.SIGNAL_EXP, lambda x: True if x.info["name"] == self.exp_name else False
-        ):
+        for rid in list_recorders("OnlineManagerSignals", lambda x: True if x.info["name"] == self.exp_name else False):
             exp.delete_recorder(rid)
 
-    # Run this firstly to see the workflow in OnlineManager
-    def first_train(self):
-        print("========== first train ==========")
-        self.reset()
-        self.rolling_online_manager.first_train(self.tasks)
-
-    # Run this secondly to see the simulating in OnlineSimulator
-    def simulate(self):
-        print("========== simulate ==========")
-        self.onlinesimulator.simulate()
-        print(self.rolling_online_manager.collect_artifact())
-
-        print("========== online models ==========")
-        recs_dict = self.onlinesimulator.online_models()
-        for time, recs in recs_dict.items():
-            print(f"{str(time[0])} to {str(time[1])}:")
-            for rec in recs:
-                print(rec.info["id"])
-
-        print("========== online signals ==========")
-        print(self.rolling_online_manager.get_signals())
-
     # Run this to run all workflow automaticly
     def main(self):
-        self.first_train()
-        self.simulate()
+        self.reset()
+        print("========== simulate ==========")
+        self.rolling_online_manager.simulate(end_time=self.end_time)
+        print(self.rolling_online_manager.get_collector()())
+        print(self.rolling_online_manager.get_online_history(self.exp_name))
 
 
 if __name__ == "__main__":