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Fix the Errors/Warnings when building Qlib's documentation (#1381)

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* Fix the Errors/Warnings when building Qlib's documentation

* Fix

* Fix

* Empty

* Test CI

* Add doc compiling checking to CI

* Fix

* Tries to be consistent with Makefile

Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
This commit is contained in:
Maxim Smolskiy
2022-12-05 04:29:03 +03:00
committed by GitHub
parent 6a47416a2d
commit 5b73b80293
26 changed files with 127 additions and 78 deletions
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18
qlib/model/trainer.py
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@@ -3,7 +3,7 @@
"""
The Trainer will train a list of tasks and return a list of model recorders.
There are two steps in each Trainer including ``train``(make model recorder) and ``end_train``(modify model recorder).
There are two steps in each Trainer including ``train`` (make model recorder) and ``end_train`` (modify model recorder).
This is a concept called ``DelayTrainer``, which can be used in online simulating for parallel training.
In ``DelayTrainer``, the first step is only to save some necessary info to model recorders, and the second step which will be finished in the end can do some concurrent and time-consuming operations such as model fitting.
@@ -242,7 +242,7 @@ class TrainerR(Trainer):
def train(self, tasks: list, train_func: Callable = None, experiment_name: str = None, **kwargs) -> List[Recorder]:
"""
Given a list of `task`s and return a list of trained Recorder. The order can be guaranteed.
Given a list of `tasks` and return a list of trained Recorder. The order can be guaranteed.
Args:
tasks (list): a list of definitions based on `task` dict
@@ -315,7 +315,7 @@ class DelayTrainerR(TrainerR):
Args:
models (list): a list of Recorder, the tasks have been saved to them
end_train_func (Callable, optional): the end_train method which needs at least `recorder`s and `experiment_name`. Defaults to None for using self.end_train_func.
end_train_func (Callable, optional): the end_train method which needs at least `recorders` and `experiment_name`. Defaults to None for using self.end_train_func.
experiment_name (str): the experiment name, None for use default name.
kwargs: the params for end_train_func.
@@ -390,14 +390,14 @@ class TrainerRM(Trainer):
**kwargs,
) -> List[Recorder]:
"""
Given a list of `task`s and return a list of trained Recorder. The order can be guaranteed.
Given a list of `tasks` and return a list of trained Recorder. The order can be guaranteed.
This method defaults to a single process, but TaskManager offered a great way to parallel training.
Users can customize their train_func to realize multiple processes or even multiple machines.
Args:
tasks (list): a list of definitions based on `task` dict
train_func (Callable): the training method which needs at least `task`s and `experiment_name`. None for the default training method.
train_func (Callable): the training method which needs at least `tasks` and `experiment_name`. None for the default training method.
experiment_name (str): the experiment name, None for use default name.
before_status (str): the tasks in before_status will be fetched and trained. Can be STATUS_WAITING, STATUS_PART_DONE.
after_status (str): the tasks after trained will become after_status. Can be STATUS_WAITING, STATUS_PART_DONE.
@@ -470,7 +470,7 @@ class TrainerRM(Trainer):
The multiprocessing method for `train`. It can share a same task_pool with `train` and can run in other progress or other machines.
Args:
train_func (Callable): the training method which needs at least `task`s and `experiment_name`. None for the default training method.
train_func (Callable): the training method which needs at least `tasks` and `experiment_name`. None for the default training method.
experiment_name (str): the experiment name, None for use default name.
"""
if train_func is None:
@@ -525,7 +525,7 @@ class DelayTrainerRM(TrainerRM):
Args:
tasks (list): a list of definition based on `task` dict
train_func (Callable): the train method which need at least `task`s and `experiment_name`. Defaults to None for using self.train_func.
train_func (Callable): the train method which need at least `tasks` and `experiment_name`. Defaults to None for using self.train_func.
experiment_name (str): the experiment name, None for use default name.
Returns:
@@ -554,7 +554,7 @@ class DelayTrainerRM(TrainerRM):
Args:
recs (list): a list of Recorder, the tasks have been saved to them.
end_train_func (Callable, optional): the end_train method which need at least `recorder`s and `experiment_name`. Defaults to None for using self.end_train_func.
end_train_func (Callable, optional): the end_train method which need at least `recorders` and `experiment_name`. Defaults to None for using self.end_train_func.
experiment_name (str): the experiment name, None for use default name.
kwargs: the params for end_train_func.
@@ -596,7 +596,7 @@ class DelayTrainerRM(TrainerRM):
The multiprocessing method for `end_train`. It can share a same task_pool with `end_train` and can run in other progress or other machines.
Args:
end_train_func (Callable, optional): the end_train method which need at least `recorder`s and `experiment_name`. Defaults to None for using self.end_train_func.
end_train_func (Callable, optional): the end_train method which need at least `recorders` and `experiment_name`. Defaults to None for using self.end_train_func.
experiment_name (str): the experiment name, None for use default name.
"""
if end_train_func is None: