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Enhance Task Dict Var (#778)

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you-n-g
2021-12-26 00:18:44 +08:00
committed by GitHub
parent e33de44cb9
commit 3493f29e16
3 changed files with 71 additions and 10 deletions
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17
examples/model_rolling/task_manager_rolling.py
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@@ -17,7 +17,7 @@ from qlib.workflow.task.gen import RollingGen, task_generator
from qlib.workflow.task.manage import TaskManager, run_task
from qlib.workflow.task.collect import RecorderCollector
from qlib.model.ens.group import RollingGroup
from qlib.model.trainer import TrainerRM, task_train
from qlib.model.trainer import TrainerR, TrainerRM, task_train
from qlib.tests.config import CSI100_RECORD_LGB_TASK_CONFIG, CSI100_RECORD_XGBOOST_TASK_CONFIG
@@ -29,7 +29,7 @@ class RollingTaskExample:
task_url="mongodb://10.0.0.4:27017/",
task_db_name="rolling_db",
experiment_name="rolling_exp",
task_pool="rolling_task",
task_pool=None, # if user want to "rolling_task"
task_config=None,
rolling_step=550,
rolling_type=RollingGen.ROLL_SD,
@@ -43,14 +43,19 @@ class RollingTaskExample:
}
qlib.init(provider_uri=provider_uri, region=region, mongo=mongo_conf)
self.experiment_name = experiment_name
self.task_pool = task_pool
if task_pool is None:
self.trainer = TrainerR(experiment_name=self.experiment_name)
else:
self.task_pool = task_pool
self.trainer = TrainerRM(self.experiment_name, self.task_pool)
self.task_config = task_config
self.rolling_gen = RollingGen(step=rolling_step, rtype=rolling_type)
# Reset all things to the first status, be careful to save important data
def reset(self):
print("========== reset ==========")
TaskManager(task_pool=self.task_pool).remove()
if isinstance(self.trainer, TrainerRM):
TaskManager(task_pool=self.task_pool).remove()
exp = R.get_exp(experiment_name=self.experiment_name)
for rid in exp.list_recorders():
exp.delete_recorder(rid)
@@ -66,10 +71,10 @@ class RollingTaskExample:
def task_training(self, tasks):
print("========== task_training ==========")
trainer = TrainerRM(self.experiment_name, self.task_pool)
trainer.train(tasks)
self.trainer.train(tasks)
def worker(self):
# NOTE: this is only used for TrainerRM
# train tasks by other progress or machines for multiprocessing. It is same as TrainerRM.worker.
print("========== worker ==========")
run_task(task_train, self.task_pool, experiment_name=self.experiment_name)