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logger & doc

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lzh222333
2021-05-09 11:58:06 +00:00
parent f5ded06a15
commit 370b6aad74
7 changed files with 69 additions and 68 deletions
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28
qlib/workflow/online/strategy.py
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@@ -22,7 +22,7 @@ class OnlineStrategy:
OnlineStrategy is working with `Online Manager <#Online Manager>`_, responsing how the tasks are generated, the models are updated and signals are perpared.
"""
def __init__(self, name_id: str, need_log=True):
def __init__(self, name_id: str):
"""
Init OnlineStrategy.
This module **MUST** use `Trainer <../reference/api.html#Trainer>`_ to finishing model training.
@@ -30,12 +30,10 @@ class OnlineStrategy:
Args:
name_id (str): a unique name or id
trainer (Trainer, optional): a instance of Trainer. Defaults to None.
need_log (bool, optional): print log or not. Defaults to True.
"""
self.name_id = name_id
self.logger = get_module_logger(self.__class__.__name__)
self.need_log = need_log
self.tool = OnlineTool(need_log)
self.tool = OnlineTool()
def prepare_tasks(self, cur_time, **kwargs) -> List[dict]:
"""
@@ -46,20 +44,21 @@ class OnlineStrategy:
"""
raise NotImplementedError(f"Please implement the `prepare_tasks` method.")
def prepare_online_models(self, models, cur_time=None, check_func=None, **kwargs):
def prepare_online_models(self, models, cur_time=None, check_func=None) -> List[object]:
"""
A typically implementation, but maybe you will need old models by online_tool.
Use trainer to train a list of tasks and set the trained model to `online`.
Select some models as the online models from the trained models.
NOTE: This method will first offline all models and online the online models prepared by this method. So you can find last online models by OnlineTool.online_models if you still need them.
NOTE: This method offline all models and online the online models prepared by this method (if have). So you can find last online models by OnlineTool.online_models if you still need them.
Args:
tasks (list): a list of tasks.
check_func: the method to judge if a model can be online.
The parameter is the model record and return True for online.
None for online every models.
**kwargs: will be passed to end_train which means will be passed to customized train method.
Returns:
List[object]: a list of selected models.
"""
if check_func is not None:
online_models = []
@@ -101,7 +100,6 @@ class RollingAverageStrategy(OnlineStrategy):
name_id: str,
task_template: Union[dict, List[dict]],
rolling_gen: RollingGen,
need_log=True,
):
"""
Init RollingAverageStrategy.
@@ -112,15 +110,14 @@ class RollingAverageStrategy(OnlineStrategy):
name_id (str): a unique name or id. Will be also the name of Experiment.
task_template (Union[dict,List[dict]]): a list of task_template or a single template, which will be used to generate many tasks using rolling_gen.
rolling_gen (RollingGen): an instance of RollingGen
need_log (bool, optional): print log or not. Defaults to True.
"""
super().__init__(name_id=name_id, need_log=need_log)
super().__init__(name_id=name_id)
self.exp_name = self.name_id
if not isinstance(task_template, list):
task_template = [task_template]
self.task_template = task_template
self.rg = rolling_gen
self.tool = OnlineToolR(self.exp_name, need_log)
self.tool = OnlineToolR(self.exp_name)
self.ta = TimeAdjuster()
def get_collector(self, process_list=[RollingGroup()], rec_key_func=None, rec_filter_func=None, artifacts_key=None):
@@ -180,10 +177,9 @@ class RollingAverageStrategy(OnlineStrategy):
self.logger.warn(f"No latest online recorders, no new tasks.")
return []
calendar_latest = D.calendar(end_time=cur_time)[-1] if cur_time is None else cur_time
if self.need_log:
self.logger.info(
f"The interval between current time {calendar_latest} and last rolling test begin time {max_test[0]} is {self.ta.cal_interval(calendar_latest, max_test[0])}, the rolling step is {self.rg.step}"
)
self.logger.info(
f"The interval between current time {calendar_latest} and last rolling test begin time {max_test[0]} is {self.ta.cal_interval(calendar_latest, max_test[0])}, the rolling step is {self.rg.step}"
)
if self.ta.cal_interval(calendar_latest, max_test[0]) >= self.rg.step:
old_tasks = []
tasks_tmp = []