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- `Learn the Basics <Basics.html>`_ ||
- `Quick Start <Quick-Start.html>`_ ||
- `Dataset & Data Structure <Datasets.html>`_ ||
- `Learning Part <Learning.html>`_ ||
- `Reasoning Part <Reasoning.html>`_ ||
- `Evaluation Metrics <Evaluation.html>`_ ||
- **Bridge**
-
-
- Bridge
- ======
-
- Bridging machine learning and reasoning to train the model is the fundamental idea of Abductive Learning, ABL-Package implements a set of `bridge class <../API/abl.bridge.html>`_ to achieve this.
-
- ``BaseBridge`` is an abstract class with the following initialization parameters:
-
- - ``model``: an object of type ``ABLModel``. Machine Learning part are wrapped in this object.
- - ``reasoner``: a object of type ``Reasoner``. Reasoning part are wrapped in this object.
-
- ``BaseBridge`` has the following important methods that need to be overridden in subclasses:
-
- +-----------------------------------+--------------------------------------------------------------------------------------+
- | Method Signature | Description |
- +===================================+======================================================================================+
- | predict(data_samples) | Predicts class probabilities and indices for the given data samples. |
- +-----------------------------------+--------------------------------------------------------------------------------------+
- | abduce_pseudo_label(data_samples) | Abduces pseudo labels for the given data samples. |
- +-----------------------------------+--------------------------------------------------------------------------------------+
- | idx_to_pseudo_label(data_samples) | Converts indices to pseudo labels using the provided or default mapping. |
- +-----------------------------------+--------------------------------------------------------------------------------------+
- | pseudo_label_to_idx(data_samples) | Converts pseudo labels to indices using the provided or default remapping. |
- +-----------------------------------+--------------------------------------------------------------------------------------+
- | train(train_data) | Train the model. |
- +-----------------------------------+--------------------------------------------------------------------------------------+
- | test(test_data) | Test the model. |
- +-----------------------------------+--------------------------------------------------------------------------------------+
-
-
- ``SimpleBridge`` inherits from ``BaseBridge`` and provides a basic implementation. Besides the ``model`` and ``reasoner``, ``SimpleBridge`` has an extra initialization arguments, ``metric_list``, which will be used to evaluate model performance. Its training process involves several Abductive Learning loops and each loop consists of the following five steps:
-
- 1. Predict class probabilities and indices for the given data samples.
- 2. Transform indices into pseudo labels.
- 3. Revise pseudo labels based on abdutive reasoning.
- 4. Transform the revised pseudo labels to indices.
- 5. Train the model.
-
- The fundamental part of the ``train`` method is as follows:
-
- .. code-block:: python
-
- def train(self, train_data, loops=50, segment_size=10000):
- if isinstance(train_data, ListData):
- data_samples = train_data
- else:
- data_samples = self.data_preprocess(*train_data)
-
- for loop in range(loops):
- for seg_idx in range((len(data_samples) - 1) // segment_size + 1):
- sub_data_samples = data_samples[
- seg_idx * segment_size : (seg_idx + 1) * segment_size
- ]
- self.predict(sub_data_samples) # 1
- self.idx_to_pseudo_label(sub_data_samples) # 2
- self.abduce_pseudo_label(sub_data_samples) # 3
- self.pseudo_label_to_idx(sub_data_samples) # 4
- loss = self.model.train(sub_data_samples) # 5, self.model is an ABLModel object
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