Merge pull request #6 from AbductiveLearning/Dev

Merge into main

README.md

@@ -41,7 +41,7 @@ an efficient and convenient toolkit to develop dual-driven ABL systems,
 which leverage the power of both data and knowledge.
 
 <p align="center">
-<img src="https://raw.githubusercontent.com/AbductiveLearning/ABLkit/main/docs/_static/img/ABLKit.png" alt="ABLkit" style="width: 80%;"/>
+<img src="https://raw.githubusercontent.com/AbductiveLearning/ABLkit/main/docs/_static/img/ABLkit.png" alt="ABLkit" style="width: 80%;"/>
 </p>
 
 ## Installation

docs/Intro/Basics.rst

@@ -20,29 +20,28 @@ AI: data, models, and knowledge. Below is an overview of the ABLkit.
 
 .. image:: ../_static/img/ABLkit.png
 
-**Data** part manages the storage, operation, and evaluation of data efficiently.
+**Data** part efficiently manages data storage, operations, and evaluations.
 It includes the ``ListData`` class, which defines the data structures used in
-ABL, and comprises common data operations like insertion, deletion, 
+ABLkit, and comprises common data operations like insertion, deletion, 
 retrieval, slicing, etc. Additionally, it contains a series of evaluation metrics 
 such as ``SymbolAccuracy`` and ``ReasoningMetric`` (both specialized metrics 
-inherited from the ``BaseMetric`` class), for evaluating model quality from a 
+inherited from the ``BaseMetric`` class), for evaluating performance from a 
 data perspective.
 
 :blue-bold:`Learning` part focuses on the construction, training, and
 prediction of machine learning models. The ``ABLModel`` class is the 
 central class that encapsulates the machine learning model. This class is
-compatible with various frameworks, including those based on Scikit-learn
+compatible with various frameworks, including those based on scikit-learn
 or PyTorch neural networks constructed by the ``BasicNN`` class.
 
 :green-bold:`Reasoning` part concentrates on constructing domain knowledge and 
-performing reasoning. The ``KBBase`` class allows users to define a 
+performing reasoning. The ``KBBase`` class allows users to customize a 
 domain knowledge base. For diverse types of knowledge, we also offer
 implementations like ``GroundKB`` and ``PrologKB`` (both inherited 
-from the ``KBBase`` class). The latter, for instance, enables 
-knowledge bases to be imported in the form of Prolog files.
-Upon building the knowledge base, the ``Reasoner`` class is
-responsible for minimizing the inconsistency between the knowledge base
-and data.
+from the ``KBBase`` class). The latter, for instance, imports
+knowledge bases via Prolog files. Upon building the knowledge base, 
+the ``Reasoner`` class is responsible for minimizing the inconsistency
+between the knowledge base and data.
 
 The integration of these three parts is achieved through the
 :yellow-bold:`Bridge` part, which features the ``SimpleBridge`` class (derived 

docs/Intro/Reasoning.rst

@@ -36,8 +36,8 @@ Building a knowledge base from ``KBBase``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 For the user-built KB from ``KBBase`` (a derived subclass), it's only
-required to pass the ``pseudo_label_list`` parameter in the ``__init__`` function
-and override the ``logic_forward`` function:
+required to pass the ``pseudo_label_list`` parameter in the ``__init__`` method
+and override the ``logic_forward`` method:
 
 -  ``pseudo_label_list`` is the list of possible pseudo-labels (also,
    the output of the machine learning model).
@@ -46,7 +46,7 @@ and override the ``logic_forward`` function:
 
 .. note::
 
-   Generally, the overridden function ``logic_forward`` provided by the user accepts 
+   Generally, the overridden method ``logic_forward`` provided by the user accepts 
    only one parameter, ``pseudo_label`` (pseudo-labels of an example). However, for certain 
    scenarios, deductive reasoning in the knowledge base may necessitate information 
    from the input. In these scenarios, ``logic_forward`` can also accept two parameters: 
@@ -93,7 +93,7 @@ Out:
 Other optional parameters
 ^^^^^^^^^^^^^^^^^^^^^^^^^
 
-We can also pass the following parameters in the ``__init__`` function when building our
+We can also pass the following parameters in the ``__init__`` method when building our
 knowledge base:
 
 -  ``max_err`` (float, optional), specifying the upper tolerance limit
@@ -119,7 +119,7 @@ When aiming to leverage knowledge base from an external Prolog file
 (which contains how to perform reasoning), we can directly create an
 instance of class ``PrologKB``. Upon instantiation of
 ``PrologKB``, we are required to pass the ``pseudo_label_list`` (same as ``KBBase``)
-and ``pl_file`` (the Prolog file) in the ``__init__`` function.
+and ``pl_file`` (the Prolog file) in the ``__init__`` method.
 
 .. admonition:: What is a Prolog file?
 
@@ -176,10 +176,10 @@ knowledge base. In this way, the knowledge built will have a Ground KB
    accelerate abductive reasoning.
 
 ``GroundKB`` is a subclass of ``GKBBase``. Similar to ``KBBase``, we
-are required to pass the ``pseudo_label_list`` parameter in the ``__init__`` function and
-override the ``logic_forward`` function, and are allowed to pass other
+are required to pass the ``pseudo_label_list`` parameter in the ``__init__`` method and
+override the ``logic_forward`` method, and are allowed to pass other
 :ref:`optional parameters <other-par>`. Additionally, we are required pass the
-``GKB_len_list`` parameter in the ``__init__`` function.
+``GKB_len_list`` parameter in the ``__init__`` method.
 
 -  ``GKB_len_list`` is the list of possible lengths for pseudo-labels of an example.