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Improve the style of documentation (#1132)

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This commit improves the documentation (rst files) only in the
following three ways:

* Aligned section headers with their underline/overline punctuation characters

* Deleted all trailling whitespaces in rst files

* Deleted a few trailling newlines at the end of the rst files

Co-authored-by: Bingyao Liu <Bingyao.Liu@sofund.com>
This commit is contained in:
YaOzI
2022-07-07 19:42:27 +08:00
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parent e62684eddf
commit 1dededa33f
29 changed files with 400 additions and 411 deletions
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docs/component/meta.rst
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@@ -1,17 +1,17 @@
.. _meta:
=================================
======================================================
Meta Controller: Meta-Task & Meta-Dataset & Meta-Model
=================================
======================================================
.. currentmodule:: qlib
Introduction
=============
============
``Meta Controller`` provides guidance to ``Forecast Model``, which aims to learn regular patterns among a series of forecasting tasks and use learned patterns to guide forthcoming forecasting tasks. Users can implement their own meta-model instance based on ``Meta Controller`` module.
Meta Task
=============
=========
A `Meta Task` instance is the basic element in the meta-learning framework. It saves the data that can be used for the `Meta Model`. Multiple `Meta Task` instances may share the same `Data Handler`, controlled by `Meta Dataset`. Users should use `prepare_task_data()` to obtain the data that can be directly fed into the `Meta Model`.
@@ -19,7 +19,7 @@ A `Meta Task` instance is the basic element in the meta-learning framework. It s
:members:
Meta Dataset
=============
============
`Meta Dataset` controls the meta-information generating process. It is on the duty of providing data for training the `Meta Model`. Users should use `prepare_tasks` to retrieve a list of `Meta Task` instances.
@@ -27,26 +27,26 @@ Meta Dataset
:members:
Meta Model
=============
==========
General Meta Model
------------------
`Meta Model` instance is the part that controls the workflow. The usage of the `Meta Model` includes:
1. Users train their `Meta Model` with the `fit` function.
1. Users train their `Meta Model` with the `fit` function.
2. The `Meta Model` instance guides the workflow by giving useful information via the `inference` function.
.. autoclass:: qlib.model.meta.model.MetaModel
:members:
Meta Task Model
------------------
---------------
This type of meta-model may interact with task definitions directly. Then, the `Meta Task Model` is the class for them to inherit from. They guide the base tasks by modifying the base task definitions. The function `prepare_tasks` can be used to obtain the modified base task definitions.
.. autoclass:: qlib.model.meta.model.MetaTaskModel
:members:
Meta Guide Model
------------------
----------------
This type of meta-model participates in the training process of the base forecasting model. The meta-model may guide the base forecasting models during their training to improve their performances.
.. autoclass:: qlib.model.meta.model.MetaGuideModel
@@ -54,9 +54,9 @@ This type of meta-model participates in the training process of the base forecas
Example
=============
``Qlib`` provides an implementation of ``Meta Model`` module, ``DDG-DA``,
which adapts to the market dynamics.
=======
``Qlib`` provides an implementation of ``Meta Model`` module, ``DDG-DA``,
which adapts to the market dynamics.
``DDG-DA`` includes four steps: