Merge branch 'Dev' into main

README.md

@@ -8,7 +8,7 @@
 [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 [![ABLKit-CI](https://github.com/AbductiveLearning/ABLKit/actions/workflows/build-and-test.yaml/badge.svg)](https://github.com/AbductiveLearning/ABLKit/actions/workflows/build-and-test.yaml)
 
-[📘Documentation](https://www.lamda.nju.edu.cn/abl_test/docs/build/html/index.html) |
+[📘Documentation](https://ablkit.readthedocs.io/en/latest/index.html) |
 [📚Examples](https://github.com/AbductiveLearning/ABLKit/tree/main/examples) |
 [💬Reporting Issues](https://github.com/AbductiveLearning/ABLKit/issues/new)
 
@@ -61,7 +61,7 @@ pip install -v -e .
 
 ### (Optional) Install SWI-Prolog
 
-If the use of a [Prolog-based knowledge base](https://www.lamda.nju.edu.cn/abl_test/docs/build/html/Intro/Reasoning.html#prolog) is necessary, please also install [SWI-Prolog](https://www.swi-prolog.org/):
+If the use of a [Prolog-based knowledge base](https://ablkit.readthedocs.io/en/latest/Intro/Reasoning.html#prolog) is necessary, please also install [SWI-Prolog](https://www.swi-prolog.org/):
 
 For Linux users:
 
@@ -194,7 +194,7 @@ bridge.test(test_data)
 
 </details>
 
-To explore detailed tutorials and information, please refer to - [document](https://www.lamda.nju.edu.cn/abl_test/docs/build/html/index.html).
+To explore detailed tutorials and information, please refer to - [document](https://ablkit.readthedocs.io/en/latest/index.html).
 
 ## Examples
 

docs/Intro/Datasets.rst

@@ -62,11 +62,11 @@ where each sublist in ``X``, e.g., |data_example|, is a data example and each im
 Data Structure
 --------------
 
-Besides the user-provided dataset, various forms of data are utilized and dynamicly generated throughout the training and testing process of ABL framework. Examples include raw data, predicted pseudo-label, abduced pseudo-label, pseudo-label indices, etc. To manage this diversity and ensure a stable, versatile interface, ABL Kit employs `abstract data interfaces <../API/ablkit.data.html#structure>`_ to encapsulate different forms of data that will be used in the total learning process.
+Besides the user-provided dataset, various forms of data are utilized and dynamicly generated throughout the training and testing process of ABL framework. Examples include raw data, predicted pseudo-label, abduced pseudo-label, pseudo-label indices, etc. To manage this diversity and ensure a stable, versatile interface, ABL Kit employs `abstract data interfaces <../API/ablkit.data.html#structures>`_ to encapsulate different forms of data that will be used in the total learning process.
 
 ``ListData`` is the underlying abstract data interface utilized in ABL Kit. As the fundamental data structure, ``ListData`` implements commonly used data manipulation methods and is responsible for transferring data between various components of ABL, ensuring that stages such as prediction, abductive reasoning, and training can utilize ``ListData`` as a unified input format. Before proceeding to other stages, user-provided datasets will be firstly converted into ``ListData``.
 
-Besides providing a tuple of ``(X, gt_pseudo_label, Y)``, ABL Kit also allows users to directly supply data in ``ListData`` format, which similarly requires the inclusion of these three attributes. The following code shows the basic usage of ``ListData``. More information can be found in the `API documentation <../API/ablkit.data.html#structure>`_.
+Besides providing a tuple of ``(X, gt_pseudo_label, Y)``, ABL Kit also allows users to directly supply data in ``ListData`` format, which similarly requires the inclusion of these three attributes. The following code shows the basic usage of ``ListData``. More information can be found in the `API documentation <../API/ablkit.data.html#structures>`_.
 
 .. code-block:: python
 

docs/References.rst

@@ -1,7 +1,7 @@
 References
 ==========
 
-Zhi-Hua Zhou. `Abductive learning: Towards bridging machine learning and logical reasoning. <http://scis.scichina.com/en/2019/076101.pdf>`_. **Science China Information Sciences**, 2019, 62: 076101.
+Zhi-Hua Zhou. `Abductive learning: Towards bridging machine learning and logical reasoning <http://scis.scichina.com/en/2019/076101.pdf>`_. **Science China Information Sciences**, 2019, 62: 076101.
 
 Zhi-Hua Zhou and Yu-Xuan Huang. `Abductive learning <https://www.lamda.nju.edu.cn/publication/chap_ABL.pdf>`_. In P. Hitzler and M. K. Sarker eds., **Neuro-Symbolic Artificial Intelligence: The State of the Art**, IOP Press, Amsterdam, 2022, p.353-379
 

docs/conf.py

@@ -9,8 +9,8 @@ from sphinx.application import Sphinx
 
 # -- Path setup --------------------------------------------------------------
 
-if "READTHEDOCS" not in os.environ:
-    sys.path.insert(0, os.path.abspath(".."))
+sys.path.insert(0, os.path.abspath(".."))
+import ablkit  # noqa: E402
 
 # -- Project information -----------------------------------------------------
 

docs/requirements.txt

@@ -2,4 +2,10 @@ sphinx
 sphinx-rtd-theme 
 recommonmark
 sphinx-markdown-tables
-sphinx-copybutton
+sphinx-copybutton
+numpy
+pyswip
+torch
+torchvision
+zoopt
+termcolor