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Meta Controller: Meta-Task & Meta-Dataset & Meta-Model
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.. 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`.
.. autoclass:: qlib.model.meta.task.MetaTask
: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.
.. autoclass:: qlib.model.meta.dataset.MetaTaskDataset
: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.
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
:members:
Example
=======
``Qlib`` provides an implementation of ``Meta Model`` module, ``DDG-DA``,
which adapts to the market dynamics.
``DDG-DA`` includes four steps:
1. Calculate meta-information and encapsulate it into ``Meta Task`` instances. All the meta-tasks form a ``Meta Dataset`` instance.
2. Train ``DDG-DA`` based on the training data of the meta-dataset.
3. Do the inference of the ``DDG-DA`` to get guide information.
4. Apply guide information to the forecasting models to improve their performances.
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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