diff --git a/docs/README.rst b/docs/README.rst deleted file mode 100644 index c510cac..0000000 --- a/docs/README.rst +++ /dev/null @@ -1,89 +0,0 @@ -ABLkit -======= - -.. raw:: html - -

ABLkit is an efficient Python toolkit for Abductive Learning (ABL). - ABL is a novel paradigm that integrates machine learning and - logical reasoning in a unified framework. It is suitable for tasks - where both data and (logical) domain knowledge are available.

- -.. image:: _static/img/ABL.png - -Key Features of ABLkit: - -- **Great Flexibility**: Adaptable to various machine learning modules and logical reasoning components. -- **User-Friendly**: Provide **data**, :blue-bold:`model`, and :green-bold:`KB`, and get started with just a few lines of code. -- **High-Performance**: Optimization for high accuracy and fast training speed. - -ABLkit encapsulates advanced ABL techniques, providing users with -an efficient and convenient toolkit to develop dual-driven ABL systems, -which leverage the power of both data and knowledge. - -.. image:: _static/img/ABLkit.png - -Installation ------------- - -Install from PyPI -^^^^^^^^^^^^^^^^^ - -The easiest way to install ABLkit is using ``pip``: - -.. code:: bash - - pip install ablkit - -Install from Source -^^^^^^^^^^^^^^^^^^^ - -Alternatively, to install from source code, -sequentially run following commands in your terminal/command line. - -.. code:: bash - - git clone https://github.com/AbductiveLearning/ABLkit.git - cd ABLkit - pip install -v -e . - -(Optional) Install SWI-Prolog -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -If the use of a :ref:`Prolog-based knowledge base ` is necessary, the installation of `SWI-Prolog `_ is also required: - -For Linux users: - -.. code:: bash - - sudo apt-get install swi-prolog - -For Windows and Mac users, please refer to the `SWI-Prolog Install Guide `_. - -References ----------- - -For more information about ABL, please refer to: `Zhou, 2019 `_ -and `Zhou and Huang, 2022 `_. - -.. code-block:: latex - - @article{zhou2019abductive, - title = {Abductive learning: towards bridging machine learning and logical reasoning}, - author = {Zhou, Zhi-Hua}, - journal = {Science China Information Sciences}, - volume = {62}, - number = {7}, - pages = {76101}, - year = {2019} - } - - @incollection{zhou2022abductive, - title = {Abductive Learning}, - author = {Zhou, Zhi-Hua and Huang, Yu-Xuan}, - booktitle = {Neuro-Symbolic Artificial Intelligence: The State of the Art}, - editor = {Pascal Hitzler and Md. Kamruzzaman Sarker}, - publisher = {{IOS} Press}, - pages = {353--369}, - address = {Amsterdam}, - year = {2022} - } diff --git a/docs/index.rst b/docs/index.rst index 63af4e5..ac95e55 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,4 +1,96 @@ -.. include:: README.rst +ABLkit +====== + +.. raw:: html + + logo +

+ ABLkit is an efficient Python toolkit for Abductive Learning (ABL). +

+ +ABL is a novel paradigm that integrates machine learning and +logical reasoning in a unified framework. It is suitable for tasks +where both data and (logical) domain knowledge are available. + +.. image:: _static/img/ABL.png + +Key Features of ABLkit: + +- **Great Flexibility**: Adaptable to various machine learning modules and logical reasoning components. +- **User-Friendly**: Provide **data**, :blue-bold:`model`, and :green-bold:`KB`, and get started with just a few lines of code. +- **High-Performance**: Optimization for high accuracy and fast training speed. + +ABLkit encapsulates advanced ABL techniques, providing users with +an efficient and convenient toolkit to develop dual-driven ABL systems, +which leverage the power of both data and knowledge. + +.. image:: _static/img/ABLkit.png + +Installation +------------ + +Install from PyPI +^^^^^^^^^^^^^^^^^ + +The easiest way to install ABLkit is using ``pip``: + +.. code:: bash + + pip install ablkit + +Install from Source +^^^^^^^^^^^^^^^^^^^ + +Alternatively, to install from source code, +sequentially run following commands in your terminal/command line. + +.. code:: bash + + git clone https://github.com/AbductiveLearning/ABLkit.git + cd ABLkit + pip install -v -e . + +(Optional) Install SWI-Prolog +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +If the use of a :ref:`Prolog-based knowledge base ` is necessary, the installation of `SWI-Prolog `_ is also required: + +For Linux users: + +.. code:: bash + + sudo apt-get install swi-prolog + +For Windows and Mac users, please refer to the `SWI-Prolog Install Guide `_. + +References +---------- + +For more information about ABL, please refer to: `Zhou, 2019 `_ +and `Zhou and Huang, 2022 `_. + +.. code-block:: latex + + @article{zhou2019abductive, + title = {Abductive learning: towards bridging machine learning and logical reasoning}, + author = {Zhou, Zhi-Hua}, + journal = {Science China Information Sciences}, + volume = {62}, + number = {7}, + pages = {76101}, + year = {2019} + } + + @incollection{zhou2022abductive, + title = {Abductive Learning}, + author = {Zhou, Zhi-Hua and Huang, Yu-Xuan}, + booktitle = {Neuro-Symbolic Artificial Intelligence: The State of the Art}, + editor = {Pascal Hitzler and Md. Kamruzzaman Sarker}, + publisher = {{IOS} Press}, + pages = {353--369}, + address = {Amsterdam}, + year = {2022} + } .. toctree:: :maxdepth: 1