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- Learn the Basics
- ================
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- In a typical Abductive Learning process, as illustrated below,
- data inputs are first mapped to pseudo labels through a machine learning model.
- These pseudo labels then pass through a knowledge base :math:`\mathcal{KB}`
- to obtain the logical result by deductive reasoning. During training,
- alongside the aforementioned forward flow (i.e., prediction --> deduction reasoning),
- there also exists a reverse flow, which starts from the logical result and
- involves abductive reasoning to generate pseudo labels.
- Subsequently, these labels are processed to minimize inconsistencies with machine learning,
- which in turn revise the outcomes of the machine learning model, and then
- fed back into the machine learning model for further training.
- To implement this process, the following four steps are necessary:
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- .. image:: ../img/ABL-Package.png
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- 1. Prepare datasets
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- Prepare the data's input, ground truth for pseudo labels (optional), and ground truth for logical results.
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- 2. Build machine learning part
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- Build a model that defines how to map input to pseudo labels.
- And use ``ABLModel`` to encapsulate the model.
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- 3. Build the reasoning part
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- Build a knowledge base by creating a subclass of ``KBBase``,
- and instantiate a ``ReasonerBase`` for minimizing of inconsistencies
- between the knowledge base and pseudo labels.
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- 4. Define Evaluation Metrics
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- Define the metrics for measuring accuracy by inheriting from ``BaseMetric``.
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- 5. Bridge machine learning and reasoning
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- Use ``SimpleBridge`` to bridge the machine learning and reasoning part
- for integrated training and testing.
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