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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>
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ReStructuredText
69 lines
2.9 KiB
ReStructuredText
.. _meta:
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======================================================
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Meta Controller: Meta-Task & Meta-Dataset & Meta-Model
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======================================================
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.. currentmodule:: qlib
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Introduction
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============
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``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.
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Meta Task
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=========
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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`.
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.. autoclass:: qlib.model.meta.task.MetaTask
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:members:
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Meta Dataset
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============
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`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.
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.. autoclass:: qlib.model.meta.dataset.MetaTaskDataset
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:members:
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Meta Model
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==========
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General Meta Model
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------------------
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`Meta Model` instance is the part that controls the workflow. The usage of the `Meta Model` includes:
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1. Users train their `Meta Model` with the `fit` function.
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2. The `Meta Model` instance guides the workflow by giving useful information via the `inference` function.
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.. autoclass:: qlib.model.meta.model.MetaModel
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:members:
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Meta Task Model
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---------------
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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.
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.. autoclass:: qlib.model.meta.model.MetaTaskModel
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:members:
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Meta Guide Model
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----------------
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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.
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.. autoclass:: qlib.model.meta.model.MetaGuideModel
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:members:
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Example
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=======
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``Qlib`` provides an implementation of ``Meta Model`` module, ``DDG-DA``,
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which adapts to the market dynamics.
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``DDG-DA`` includes four steps:
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1. Calculate meta-information and encapsulate it into ``Meta Task`` instances. All the meta-tasks form a ``Meta Dataset`` instance.
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2. Train ``DDG-DA`` based on the training data of the meta-dataset.
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3. Do the inference of the ``DDG-DA`` to get guide information.
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4. Apply guide information to the forecasting models to improve their performances.
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The `above example <https://github.com/microsoft/qlib/tree/main/examples/benchmarks_dynamic/DDG-DA>`_ can be found in ``examples/benchmarks_dynamic/DDG-DA/workflow.py``.
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